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Jiang G, Zheng JY, Ren SN, Yin W, Xia X, Li Y, Wang HL. A comprehensive workflow for optimizing RNA-seq data analysis. BMC Genomics 2024; 25:631. [PMID: 38914930 PMCID: PMC11197194 DOI: 10.1186/s12864-024-10414-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 05/15/2024] [Indexed: 06/26/2024] Open
Abstract
BACKGROUND Current RNA-seq analysis software for RNA-seq data tends to use similar parameters across different species without considering species-specific differences. However, the suitability and accuracy of these tools may vary when analyzing data from different species, such as humans, animals, plants, fungi, and bacteria. For most laboratory researchers lacking a background in information science, determining how to construct an analysis workflow that meets their specific needs from the array of complex analytical tools available poses a significant challenge. RESULTS By utilizing RNA-seq data from plants, animals, and fungi, it was observed that different analytical tools demonstrate some variations in performance when applied to different species. A comprehensive experiment was conducted specifically for analyzing plant pathogenic fungal data, focusing on differential gene analysis as the ultimate goal. In this study, 288 pipelines using different tools were applied to analyze five fungal RNA-seq datasets, and the performance of their results was evaluated based on simulation. This led to the establishment of a relatively universal and superior fungal RNA-seq analysis pipeline that can serve as a reference, and certain standards for selecting analysis tools were derived for reference. Additionally, we compared various tools for alternative splicing analysis. The results based on simulated data indicated that rMATS remained the optimal choice, although consideration could be given to supplementing with tools such as SpliceWiz. CONCLUSION The experimental results demonstrate that, in comparison to the default software parameter configurations, the analysis combination results after tuning can provide more accurate biological insights. It is beneficial to carefully select suitable analysis software based on the data, rather than indiscriminately choosing tools, in order to achieve high-quality analysis results more efficiently.
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Affiliation(s)
- Gao Jiang
- School of Information Science and Technology, School of Artificial Intelligence, Beijing Forestry University, Beijing, 100083, People's Republic of China
| | - Juan-Yu Zheng
- School of Information Science and Technology, School of Artificial Intelligence, Beijing Forestry University, Beijing, 100083, People's Republic of China
| | - Shu-Ning Ren
- State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, People's Republic of China
| | - Weilun Yin
- State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, People's Republic of China
| | - Xinli Xia
- State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, People's Republic of China
| | - Yun Li
- School of Information Science and Technology, School of Artificial Intelligence, Beijing Forestry University, Beijing, 100083, People's Republic of China.
| | - Hou-Ling Wang
- State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, People's Republic of China.
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2
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Sun D, Yu H, Kong L, Liu S, Xu C, Li Q. The role of DNA methylation reprogramming during sex determination and sex reversal in the Pacific oyster Crassostrea gigas. Int J Biol Macromol 2024; 259:128964. [PMID: 38219938 DOI: 10.1016/j.ijbiomac.2023.128964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 10/19/2023] [Accepted: 12/14/2023] [Indexed: 01/16/2024]
Abstract
DNA methylation is instrumental in vertebrate sex reversal. However, the mechanism of DNA methylation regulation regarding sex reversal in invertebrates is unclear. In this study, we used whole genome bisulfite sequencing (WGBS) to map single-base resolution methylation profiles of the Pacific oyster, including female-to-male (FMa-to-FMb) and male-to-female (MFa-to-MFb) sex reversal, as well as sex non-reversed males and females (MMa-to-MMb and FFa-to-FFb). The results showed that global DNA methylation levels increase during female-to-male sex reversals, with a particular increase in the proportion of high methylation levels (mCGs >0.75) and a decrease in the proportion of intermediate methylation levels (0.25 < mCGs <0.75). This increase in DNA methylation was mainly associated with the elevated expression of DNA methylase genes. Genome-wide methylation patterns of females were accurately remodeled to those of males after sex reversal, while the opposite was true for the male-to-female reversal. Those findings directly indicate that alterations in DNA methylation play a significant role in sex reversal in Pacific oysters. Comparative analysis of the DNA methylomes of pre- and post- sex reversal gonadal tissues (FMb-vs-FMa or MFb-vs-MFa) revealed that differentially methylated genes were mainly involved in the biological processes of sex determination or gonadal development. However critical genes such as Dmrt1, Foxl2 and Sox-like, which are involved in the putative sex determination pathway in Pacific oysters, showed almost an absence of methylation modifications, varying greatly from vertebrates. Additionally, comparative analysis of the DNA methylomes of sexual reversal and sex non-reversal (FMa-vs-FFa or MFa-vs-MMa) revealed that heat shock protein genes, such as Hsp68-like and Hsp70B, were important for the occurrence of sex reversal. These findings shed light on the epigenetic mechanisms underlying the maintenance of gonadal plasticity and the reversal of organ architecture in oysters.
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Affiliation(s)
- Dongfang Sun
- Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao 266003, China
| | - Hong Yu
- Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao 266003, China
| | - Lingfeng Kong
- Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao 266003, China
| | - Shikai Liu
- Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao 266003, China
| | - Chengxun Xu
- Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao 266003, China
| | - Qi Li
- Key Laboratory of Mariculture, Ministry of Education, Ocean University of China, Qingdao 266003, China; Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China.
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3
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Jin Z, Wang M, Tang C, Zheng X, Zhang W, Sha X, An S. Predicting miRNA-disease association via graph attention learning and multiplex adaptive modality fusion. Comput Biol Med 2024; 169:107904. [PMID: 38181611 DOI: 10.1016/j.compbiomed.2023.107904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 12/12/2023] [Accepted: 12/23/2023] [Indexed: 01/07/2024]
Abstract
miRNAs are a class of small non-coding RNA molecules that play important roles in gene regulation. They are crucial for maintaining normal cellular functions, and dysregulation or dysfunction of miRNAs which are linked to the onset and advancement of multiple human diseases. Research on miRNAs has unveiled novel avenues in the realm of the diagnosis, treatment, and prevention of human diseases. However, clinical trials pose challenges and drawbacks, such as complexity and time-consuming processes, which create obstacles for many researchers. Graph Attention Network (GAT) has shown excellent performance in handling graph-structured data for tasks such as link prediction. Some studies have successfully applied GAT to miRNA-disease association prediction. However, there are several drawbacks to existing methods. Firstly, most of the previous models rely solely on concatenation operations to merge features of miRNAs and diseases, which results in the deprivation of significant modality-specific information and even the inclusion of redundant information. Secondly, as the number of layers in GAT increases, there is a possibility of excessive smoothing in the feature extraction process, which significantly affects the prediction accuracy. To address these issues and effectively complete miRNA disease prediction tasks, we propose an innovative model called Multiplex Adaptive Modality Fusion Graph Attention Network (MAMFGAT). MAMFGAT utilizes GAT as the main structure for feature aggregation and incorporates a multi-modal adaptive fusion module to extract features from three interconnected networks: the miRNA-disease association network, the miRNA similarity network, and the disease similarity network. It employs adaptive learning and cross-modality contrastive learning to fuse more effective miRNA and disease feature embeddings as well as incorporates multi-modal residual feature fusion to tackle the problem of excessive feature smoothing in GATs. Finally, we employ a Multi-Layer Perceptron (MLP) model that takes the embeddings of miRNA and disease features as input to anticipate the presence of potential miRNA-disease associations. Extensive experimental results provide evidence of the superior performance of MAMFGAT in comparison to other state-of-the-art methods. To validate the significance of various modalities and assess the efficacy of the designed modules, we performed an ablation analysis. Furthermore, MAMFGAT shows outstanding performance in three cancer case studies, indicating that it is a reliable method for studying the association between miRNA and diseases. The implementation of MAMFGAT can be accessed at the following GitHub repository: https://github.com/zixiaojin66/MAMFGAT-master.
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Affiliation(s)
- Zixiao Jin
- School of Computer, China University of Geosciences, Wuhan, 430074, China.
| | - Minhui Wang
- Department of Pharmacy, Lianshui People's Hospital of Kangda College Affiliated to Nanjing Medical University, Huai'an 223300, China.
| | - Chang Tang
- School of Computer, China University of Geosciences, Wuhan, 430074, China.
| | - Xiao Zheng
- School of Computer, National University of Defense Technology, Changsha, 410073, China.
| | - Wen Zhang
- College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China.
| | - Xiaofeng Sha
- Department of Oncology, Huai'an Hongze District People's Hospital, Huai'an, 223100, China.
| | - Shan An
- JD Health International Inc., China.
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Rao X. Plant Transcriptome Analysis with HISAT-StringTie-Ballgown and TopHat-Cufflinks Pipelines. Methods Mol Biol 2024; 2812:203-213. [PMID: 39068364 DOI: 10.1007/978-1-0716-3886-6_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
The development of next-generation sequencing technology has led to a burst of data in a single assay. Management of a large dataset requires high demands on bioinformatic skills and computing resources. Here we present two popular pipelines for RNA-seq data analysis, using open-source software tools HISAT-StringTie-Ballgown and TopHat-Cufflinks. To meet the need of plant scientist, we describe in detail how to perform such comprehensive analysis beginning with raw RNA-seq reads and available reference genome. It allows biologists to align short reads to a reference genome, measure the transcript abundance, and analyze gene differential expression under two or more conditions. We also discuss other RNA-seq tools that are comparable or alternative to this protocol.
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Affiliation(s)
- Xiaolan Rao
- State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan, China.
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5
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Xia Y. Statistical normalization methods in microbiome data with application to microbiome cancer research. Gut Microbes 2023; 15:2244139. [PMID: 37622724 PMCID: PMC10461514 DOI: 10.1080/19490976.2023.2244139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 07/12/2023] [Accepted: 07/31/2023] [Indexed: 08/26/2023] Open
Abstract
Mounting evidence has shown that gut microbiome is associated with various cancers, including gastrointestinal (GI) tract and non-GI tract cancers. But microbiome data have unique characteristics and pose major challenges when using standard statistical methods causing results to be invalid or misleading. Thus, to analyze microbiome data, it not only needs appropriate statistical methods, but also requires microbiome data to be normalized prior to statistical analysis. Here, we first describe the unique characteristics of microbiome data and the challenges in analyzing them (Section 2). Then, we provide an overall review on the available normalization methods of 16S rRNA and shotgun metagenomic data along with examples of their applications in microbiome cancer research (Section 3). In Section 4, we comprehensively investigate how the normalization methods of 16S rRNA and shotgun metagenomic data are evaluated. Finally, we summarize and conclude with remarks on statistical normalization methods (Section 5). Altogether, this review aims to provide a broad and comprehensive view and remarks on the promises and challenges of the statistical normalization methods in microbiome data with microbiome cancer research examples.
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Affiliation(s)
- Yinglin Xia
- Division of Gastroenterology and Hepatology, Department of Medicine, University of Illinois Chicago, Chicago, USA
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6
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Thawng CN, Smith GB. Transcriptome software results show significant variation among different commercial pipelines. BMC Genomics 2023; 24:662. [PMID: 37919675 PMCID: PMC10623858 DOI: 10.1186/s12864-023-09683-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 09/19/2023] [Indexed: 11/04/2023] Open
Abstract
BACKGROUND We have been documenting the biological responses to low levels of radiation (natural background) and very low level radiation (below background), and thus these studies are testing mild external stimuli to which we would expect relatively mild biological responses. We recently published a transcriptome software comparison study based on RNA-Seqs from a below background radiation treatment of two model organisms, E. coli and C. elegans (Thawng and Smith, BMC Genomics 23:452, 2022). We reported DNAstar-D (Deseq2 in the DNAstar software pipeline) to be the more conservative, realistic tool for differential gene expression compared to other transcriptome software packages (CLC, Partek and DNAstar-E (using edgeR). Here we report two follow-up studies (one with a new model organism, Aedes aegypti and another software package (Azenta) on transcriptome responses from varying dose rates using three different sources of natural radiation. RESULTS When E. coli was exposed to varying levels of K40, we again found that the DNAstar-D pipeline yielded a more conservative number of DEGs and a lower fold-difference than the CLC pipeline and DNAstar-E run in parallel. After a 30 read minimum cutoff criterion was applied to the data, the number of significant DEGs ranged from 0 to 81 with DNAstar-D, while the number of significant DEGs ranged from 4 to 117 and 14 to 139 using DNAstar-E and the CLC pipelines, respectively. In terms of the extent of expression, the highest foldchange DEG was observed in DNAstar-E with 19.7-fold followed by 12.5-fold in CLC and 4.3-fold in DNAstar-D. In a recently completed study with Ae. Aegypti and using another software package (Azenta), we analyzed the RNA-Seq response to similar sources of low-level radiation and again found the DNAstar-D pipeline to give the more conservative number and fold-expression of DEGs compared to other softwares. The number of significant DEGs ranged 31-221 in Azenta and 31 to 237 in CLC, 19-252 in DNAstar-E and 0-67 in DNAStar-D. The highest fold-change of DEGs were found in CLC (1,350.9-fold), with DNAstar-E (5.9 -fold) and Azenta (5.5-fold) intermediate, and the lowest levels of expression (4-fold) found in DNAstar-D. CONCLUSIONS This study once again highlights the importance of choosing appropriate software for transcriptome analysis. Using three different biological models (bacteria, nematode and mosquito) in four different studies testing very low levels of radiation (Van Voorhies et al., Front Public Health 8:581796, 2020; Thawng and Smith, BMC Genomics 23:452, 2022; current study), the CLC software package resulted in what appears to be an exaggerated gene expression response in terms of numbers of DEGs and extent of expression. Setting a 30-read cutoff diminishes this exaggerated response in most of the software tested. We have further affirmed that DNAstar-Deseq2 gives a more conservative transcriptome expression pattern which appears more suitable for studies expecting subtle gene expression patterns.
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Affiliation(s)
- Cung Nawl Thawng
- Biology Department and Molecular Biology Program, New Mexico State University, Las Cruces, NM, USA
| | - Geoffrey Battle Smith
- Biology Department and Molecular Biology Program, New Mexico State University, Las Cruces, NM, USA.
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7
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Crandall JG, Fisher KJ, Sato TK, Hittinger CT. Ploidy evolution in a wild yeast is linked to an interaction between cell type and metabolism. PLoS Biol 2023; 21:e3001909. [PMID: 37943740 PMCID: PMC10635434 DOI: 10.1371/journal.pbio.3001909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 10/06/2023] [Indexed: 11/12/2023] Open
Abstract
Ploidy is an evolutionarily labile trait, and its variation across the tree of life has profound impacts on evolutionary trajectories and life histories. The immediate consequences and molecular causes of ploidy variation on organismal fitness are frequently less clear, although extreme mating type skews in some fungi hint at links between cell type and adaptive traits. Here, we report an unusual recurrent ploidy reduction in replicate populations of the budding yeast Saccharomyces eubayanus experimentally evolved for improvement of a key metabolic trait, the ability to use maltose as a carbon source. We find that haploids have a substantial, but conditional, fitness advantage in the absence of other genetic variation. Using engineered genotypes that decouple the effects of ploidy and cell type, we show that increased fitness is primarily due to the distinct transcriptional program deployed by haploid-like cell types, with a significant but smaller contribution from absolute ploidy. The link between cell-type specification and the carbon metabolism adaptation can be traced to the noncanonical regulation of a maltose transporter by a haploid-specific gene. This study provides novel mechanistic insight into the molecular basis of an environment-cell type fitness interaction and illustrates how selection on traits unexpectedly linked to ploidy states or cell types can drive karyotypic evolution in fungi.
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Affiliation(s)
- Johnathan G. Crandall
- Laboratory of Genetics, Wisconsin Energy Institute, J. F. Crow Institute for the Study of Evolution, Center for Genomic Science Innovation, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Kaitlin J. Fisher
- Laboratory of Genetics, Wisconsin Energy Institute, J. F. Crow Institute for the Study of Evolution, Center for Genomic Science Innovation, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Trey K. Sato
- DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Chris Todd Hittinger
- Laboratory of Genetics, Wisconsin Energy Institute, J. F. Crow Institute for the Study of Evolution, Center for Genomic Science Innovation, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
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8
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Tao S, Hou Y, Diao L, Hu Y, Xu W, Xie S, Xiao Z. Long noncoding RNA study: Genome-wide approaches. Genes Dis 2023; 10:2491-2510. [PMID: 37554208 PMCID: PMC10404890 DOI: 10.1016/j.gendis.2022.10.024] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 10/09/2022] [Accepted: 10/23/2022] [Indexed: 11/30/2022] Open
Abstract
Long noncoding RNAs (lncRNAs) have been confirmed to play a crucial role in various biological processes across several species. Though many efforts have been devoted to the expansion of the lncRNAs landscape, much about lncRNAs is still unknown due to their great complexity. The development of high-throughput technologies and the constantly improved bioinformatic methods have resulted in a rapid expansion of lncRNA research and relevant databases. In this review, we introduced genome-wide research of lncRNAs in three parts: (i) novel lncRNA identification by high-throughput sequencing and computational pipelines; (ii) functional characterization of lncRNAs by expression atlas profiling, genome-scale screening, and the research of cancer-related lncRNAs; (iii) mechanism research by large-scale experimental technologies and computational analysis. Besides, primary experimental methods and bioinformatic pipelines related to these three parts are summarized. This review aimed to provide a comprehensive and systemic overview of lncRNA genome-wide research strategies and indicate a genome-wide lncRNA research system.
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Affiliation(s)
- Shuang Tao
- The Biotherapy Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510630, China
| | - Yarui Hou
- The Biotherapy Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510630, China
| | - Liting Diao
- The Biotherapy Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510630, China
| | - Yanxia Hu
- The Biotherapy Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510630, China
| | - Wanyi Xu
- The Biotherapy Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510630, China
| | - Shujuan Xie
- The Biotherapy Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510630, China
- Institute of Vaccine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510630, China
| | - Zhendong Xiao
- The Biotherapy Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510630, China
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9
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Liang J, Abdullah ALB, Wang H, Liu G, Han M. Change in energy-consuming strategy, nucleolar metabolism and physical defense in Macrobrachium rosenbergii after acute and chronic polystyrene nanoparticles exposure. AQUATIC TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2023; 263:106711. [PMID: 37783050 DOI: 10.1016/j.aquatox.2023.106711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 09/25/2023] [Accepted: 09/27/2023] [Indexed: 10/04/2023]
Abstract
The COVID-19 pandemic has further intensified plastic pollution due to the escalated use of single-use gloves and masks, consequently leading to the widespread presence of microplastics (MPs) and nanoplastics (NPs) in major rivers and lakes worldwide. Macrobrachium rosenbergii has become an important experimental subject due to its ecological role and environmental sensitivity. In this study, we sought to comprehend the ramifications of NPs on the widely-distributed freshwater prawn, M rosenbergii, by conducting a detailed analysis of its responses to NPs after both 96 h and 30 days of exposure. The transcriptome analysis revealed 918 differentially expressed unigenes (DEGs) after 30 days of NPs exposure (356 upregulated, 562 downregulated) and 2376 DEGs after 96 h of NPs exposure (1541 upregulated, 835 downregulated). The results of DEGs expression indicated that acute NPs exposure enhanced carbohydrate transport and metabolism, fostering chitin and extracellular matrix processes. In contrast, chronic NPs exposure induced nucleolar stress in M. rosenbergii, impeding ribosome development and mRNA maturation while showing no significant changes in glucose metabolism. Our findings underscore the M. rosenbergii distinct coping mechanisms during acute and chronic NPs exposure, elucidating its vital adaptive strategies. These results contribute to our understanding of the ecological implications of NPs pollution and its impact on aquatic animals.
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Affiliation(s)
- Ji Liang
- School of Humanities, University Sains Malaysia, Minden, Penang 11800, Malaysia
| | | | - Hong Wang
- College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan 611130, China
| | - Guoxing Liu
- Freshwater Fisheries Research Institute of Jiangsu Province, 79 Chating East Street, Nanjing 210017, China
| | - Mingming Han
- Centre for marine and coastal studies, University Sains Malaysia, Minden, Penang 11800, Malaysia.
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10
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O'Connell GC. Variability in donor leukocyte counts confound the use of common RNA sequencing data normalization strategies in transcriptomic biomarker studies performed with whole blood. Sci Rep 2023; 13:15514. [PMID: 37726353 PMCID: PMC10509252 DOI: 10.1038/s41598-023-41443-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 08/26/2023] [Indexed: 09/21/2023] Open
Abstract
Gene expression data generated from whole blood via next generation sequencing is frequently used in studies aimed at identifying mRNA-based biomarker panels with utility for diagnosis or monitoring of human disease. These investigations often employ data normalization techniques more typically used for analysis of data originating from solid tissues, which largely operate under the general assumption that specimens have similar transcriptome composition. However, this assumption may be violated when working with data generated from whole blood, which is more cellularly dynamic, leading to potential confounds. In this study, we used next generation sequencing in combination with flow cytometry to assess the influence of donor leukocyte counts on the transcriptional composition of whole blood specimens sampled from a cohort of 138 human subjects, and then subsequently examined the effect of four frequently used data normalization approaches on our ability to detect inter-specimen biological variance, using the flow cytometry data to benchmark each specimens true cellular and molecular identity. Whole blood samples originating from donors with differing leukocyte counts exhibited dramatic differences in both genome-wide distributions of transcript abundance and gene-level expression patterns. Consequently, three of the normalization strategies we tested, including median ratio (MRN), trimmed mean of m-values (TMM), and quantile normalization, noticeably masked the true biological structure of the data and impaired our ability to detect true interspecimen differences in mRNA levels. The only strategy that improved our ability to detect true biological variance was simple scaling of read counts by sequencing depth, which unlike the aforementioned approaches, makes no assumptions regarding transcriptome composition.
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Affiliation(s)
- Grant C O'Connell
- Molecular Biomarker Core, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106-4904, USA.
- School of Nursing, Case Western Reserve University, Cleveland, OH, USA.
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11
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Kuyateh O, Obbard DJ. Viruses in Laboratory Drosophila and Their Impact on Host Gene Expression. Viruses 2023; 15:1849. [PMID: 37766256 PMCID: PMC10537266 DOI: 10.3390/v15091849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 08/18/2023] [Accepted: 08/28/2023] [Indexed: 09/29/2023] Open
Abstract
Drosophila melanogaster has one of the best characterized antiviral immune responses among invertebrates. However, relatively few easily transmitted natural virus isolates are available, and so many Drosophila experiments have been performed using artificial infection routes and artificial host-virus combinations. These may not reflect natural infections, especially for subtle phenotypes such as gene expression. Here, to explore the laboratory virus community and to better understand how natural virus infections induce changes in gene expression, we have analysed seven publicly available D. melanogaster transcriptomic sequencing datasets that were originally sequenced for projects unrelated to virus infection. We have found ten known viruses-including five that have not been experimentally isolated-but no previously unknown viruses. Our analysis of host gene expression revealed that numerous genes were differentially expressed in flies that were naturally infected with a virus. For example, flies infected with nora virus showed patterns of gene expression consistent with intestinal vacuolization and possible host repair via the upd3 JAK/STAT pathway. We also found marked sex differences in virus-induced differential gene expression. Our results show that natural virus infection in laboratory Drosophila does indeed induce detectable changes in gene expression, suggesting that this may form an important background condition for experimental studies in the laboratory.
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Affiliation(s)
- Oumie Kuyateh
- Institute of Ecology and Evolution, University of Edinburgh, Ashworth Laboratories, Charlotte Auerbach Road, Edinburgh EH9 3FL, UK;
- Parasites and Microbes, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Darren J. Obbard
- Institute of Ecology and Evolution, University of Edinburgh, Ashworth Laboratories, Charlotte Auerbach Road, Edinburgh EH9 3FL, UK;
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12
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Kumara PM, Varun E, Sanjay JR, Madhushree AH, Thimmappa R. De novo transcriptome analysis of Dysoxylum binectariferum to unravel the biosynthesis of pharmaceutically relevant specialized metabolites. FRONTIERS IN PLANT SCIENCE 2023; 14:1098987. [PMID: 37636089 PMCID: PMC10450223 DOI: 10.3389/fpls.2023.1098987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 07/05/2023] [Indexed: 08/29/2023]
Abstract
The tropical tree, D. binectariferum, is a prominent source of chromone alkaloid rohitukine, which is used in the semi-syntheses of anticancer molecules such as flavopiridol and P-276-00. The biosynthetic pathway of rohitukine or its derivatives is currently unknown in plants. Here, we explored chromone alkaloid biosynthesis in D. binectariferum through targeted transcriptome sequencing. Illumina sequencing of leaves and roots of a year-old D. binectariferum seedling generated, 42.43 and 38.74 million paired-end short reads, respectively. Quality filtering and de novo assembly of the transcriptome generated 274,970 contigs and 126,788 unigenes with an N50 contig length of 1560 bp. The assembly generated 117,619 translated unigene protein sequences and 51,598 non-redundant sequences. Nearly 80% of these non-redundant sequences were annotated to publicly available protein and nucleotide databases, suggesting the completeness and effectiveness of the transcriptome assembly. Using the assembly, we identified a chalcone synthase (CHS) and three type III polyketide synthases (PKS-III; non-CHS type) that are likely to be involved in the biosynthesis of chromone ring/noreugenin moiety of rohitukine. We also identified key enzymes like lysine decarboxylase in the piperidine pathway that make the piperidine moiety of rohitukine. Besides these, the upstream enzymes in flavonoid biosynthesis like phenylalanine ammonia-lyase (PAL), trans-cinnamate 4-hydroxylase (C4H),4-coumarate-CoA ligase (4CL), and chalcone isomerase (CHI) have also been identified. Also, terpene synthases that are likely to be involved in the biosynthesis of various terpenoid scaffolds have been identified. Together, the D. binectariferum transcriptome resource forms a basis for further exploration of biosynthetic pathways of these valuable compounds through functional validation of the candidate genes and metabolic engineering in heterologous hosts. Additionally, the transcriptome dataset generated will serve as an important resource for research on functional genomics and enzyme discovery in D. binectariferum and comparative analysis with other Meliaceae family members.
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Affiliation(s)
- Patel Mohana Kumara
- Department of Biotechnology and Crop Improvement, Kittur Rani Chennamma College of Horticulture, Arabhavi, University of Horticultural Sciences, Bagalkot, Karnataka, India
- Center for Ayurveda Biology and Holistic Nutrition, The University of Trans-Disciplinary Health Sciences and Technology (TDU), Bengaluru, Karnataka, India
| | - Eranna Varun
- Center for Ayurveda Biology and Holistic Nutrition, The University of Trans-Disciplinary Health Sciences and Technology (TDU), Bengaluru, Karnataka, India
| | - Joshi Renuka Sanjay
- Center for Ayurveda Biology and Holistic Nutrition, The University of Trans-Disciplinary Health Sciences and Technology (TDU), Bengaluru, Karnataka, India
| | - Anchedoddi Hanumegowda Madhushree
- Center for Ayurveda Biology and Holistic Nutrition, The University of Trans-Disciplinary Health Sciences and Technology (TDU), Bengaluru, Karnataka, India
| | - Ramesha Thimmappa
- Amity Institute of Genome Engineering, Amity University Uttar Pradesh, Noida, India
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13
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Bullones A, Castro AJ, Lima-Cabello E, Fernandez-Pozo N, Bautista R, Alché JDD, Claros MG. Transcriptomic Insight into the Pollen Tube Growth of Olea europaea L. subsp. europaea Reveals Reprogramming and Pollen-Specific Genes Including New Transcription Factors. PLANTS (BASEL, SWITZERLAND) 2023; 12:2894. [PMID: 37631106 PMCID: PMC10459472 DOI: 10.3390/plants12162894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/01/2023] [Accepted: 08/02/2023] [Indexed: 08/27/2023]
Abstract
The pollen tube is a key innovation of land plants that is essential for successful fertilisation. Its development and growth have been profusely studied in model organisms, but in spite of the economic impact of olive trees, little is known regarding the genome-wide events underlying pollen hydration and growth in this species. To fill this gap, triplicate mRNA samples at 0, 1, 3, and 6 h of in vitro germination of olive cultivar Picual pollen were analysed by RNA-seq. A bioinformatics R workflow called RSeqFlow was developed contemplating the best practices described in the literature, covering from expression data filtering to differential expression and clustering, to finally propose hub genes. The resulting olive pollen transcriptome consisted of 22,418 reliable transcripts, where 5364 were differentially expressed, out of which 173 have no orthologue in plants and up to 3 of them might be pollen-specific transcription factors. Functional enrichment revealed a deep transcriptional reprogramming in mature olive pollen that is also dependent on protein stability and turnover to allow pollen tube emergence, with many hub genes related to heat shock proteins and F-box-containing proteins. Reprogramming extends to the first 3 h of growth, including processes consistent with studies performed in other plant species, such as global down-regulation of biosynthetic processes, vesicle/organelle trafficking and cytoskeleton remodelling. In the last stages, growth should be maintained from persistent transcripts. Mature pollen is equipped with transcripts to successfully cope with adverse environments, even though the in vitro growth seems to induce several stress responses. Finally, pollen-specific transcription factors were proposed as probable drivers of pollen germination in olive trees, which also shows an overall increased number of pollen-specific gene isoforms relative to other plants.
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Affiliation(s)
- Amanda Bullones
- Department of Molecular Biology and Biochemistry, Universidad de Málaga, 29010 Malaga, Spain;
- Institute for Mediterranean and Subtropical Horticulture “La Mayora” (IHSM-UMA-CSIC), 29010 Malaga, Spain;
| | - Antonio Jesús Castro
- Plant Reproductive Biology and Advanced Imaging Laboratory (BReMAP), Estación Experimental del Zaidín (EEZ-CSIC), 18008 Granada, Spain; (A.J.C.); (E.L.-C.); (J.d.D.A.)
| | - Elena Lima-Cabello
- Plant Reproductive Biology and Advanced Imaging Laboratory (BReMAP), Estación Experimental del Zaidín (EEZ-CSIC), 18008 Granada, Spain; (A.J.C.); (E.L.-C.); (J.d.D.A.)
| | - Noe Fernandez-Pozo
- Institute for Mediterranean and Subtropical Horticulture “La Mayora” (IHSM-UMA-CSIC), 29010 Malaga, Spain;
| | - Rocío Bautista
- Plataforma Andaluza de Bioinformática, Supercomputing and Bioinnovation Center (SCBI), Universidad de Málaga, 29590 Malaga, Spain;
| | - Juan de Dios Alché
- Plant Reproductive Biology and Advanced Imaging Laboratory (BReMAP), Estación Experimental del Zaidín (EEZ-CSIC), 18008 Granada, Spain; (A.J.C.); (E.L.-C.); (J.d.D.A.)
- University Institute of Research on Olive Grove and Olive Oils (INUO), Universidad de Jaén, 23071 Jaen, Spain
| | - Manuel Gonzalo Claros
- Department of Molecular Biology and Biochemistry, Universidad de Málaga, 29010 Malaga, Spain;
- Institute for Mediterranean and Subtropical Horticulture “La Mayora” (IHSM-UMA-CSIC), 29010 Malaga, Spain;
- CIBER de Enfermedades Raras (CIBERER) U741, 29071 Malaga, Spain
- Institute of Biomedical Research in Málaga (IBIMA), IBIMA-RARE, 29010 Malaga, Spain
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Hu Qian S, Shi MW, Wang DY, Fear JM, Chen L, Tu YX, Liu HS, Zhang Y, Zhang SJ, Yu SS, Oliver B, Chen ZX. Integrating massive RNA-seq data to elucidate transcriptome dynamics in Drosophila melanogaster. Brief Bioinform 2023; 24:bbad177. [PMID: 37232385 PMCID: PMC10505420 DOI: 10.1093/bib/bbad177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 04/19/2023] [Accepted: 04/20/2023] [Indexed: 05/27/2023] Open
Abstract
The volume of ribonucleic acid (RNA)-seq data has increased exponentially, providing numerous new insights into various biological processes. However, due to significant practical challenges, such as data heterogeneity, it is still difficult to ensure the quality of these data when integrated. Although some quality control methods have been developed, sample consistency is rarely considered and these methods are susceptible to artificial factors. Here, we developed MassiveQC, an unsupervised machine learning-based approach, to automatically download and filter large-scale high-throughput data. In addition to the read quality used in other tools, MassiveQC also uses the alignment and expression quality as model features. Meanwhile, it is user-friendly since the cutoff is generated from self-reporting and is applicable to multimodal data. To explore its value, we applied MassiveQC to Drosophila RNA-seq data and generated a comprehensive transcriptome atlas across 28 tissues from embryogenesis to adulthood. We systematically characterized fly gene expression dynamics and found that genes with high expression dynamics were likely to be evolutionarily young and expressed at late developmental stages, exhibiting high nonsynonymous substitution rates and low phenotypic severity, and they were involved in simple regulatory programs. We also discovered that human and Drosophila had strong positive correlations in gene expression in orthologous organs, revealing the great potential of the Drosophila system for studying human development and disease.
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Affiliation(s)
- Sheng Hu Qian
- Hubei Hongshan Laboratory, College of Biomedicine and Health, Huazhong Agricultural University, Wuhan 430070, China
| | - Meng-Wei Shi
- Hubei Hongshan Laboratory, College of Biomedicine and Health, Huazhong Agricultural University, Wuhan 430070, China
| | - Dan-Yang Wang
- Hubei Hongshan Laboratory, College of Biomedicine and Health, Huazhong Agricultural University, Wuhan 430070, China
| | - Justin M Fear
- Section of Developmental Genomics, National Institute of Diabetes and Kidney and Digestive Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Lu Chen
- Hubei Hongshan Laboratory, College of Biomedicine and Health, Huazhong Agricultural University, Wuhan 430070, China
| | - Yi-Xuan Tu
- Hubei Hongshan Laboratory, College of Biomedicine and Health, Huazhong Agricultural University, Wuhan 430070, China
| | - Hong-Shan Liu
- Hubei Hongshan Laboratory, College of Biomedicine and Health, Huazhong Agricultural University, Wuhan 430070, China
| | - Yuan Zhang
- Hubei Hongshan Laboratory, College of Biomedicine and Health, Huazhong Agricultural University, Wuhan 430070, China
| | - Shuai-Jie Zhang
- Hubei Hongshan Laboratory, College of Biomedicine and Health, Huazhong Agricultural University, Wuhan 430070, China
| | - Shan-Shan Yu
- Hubei Hongshan Laboratory, College of Biomedicine and Health, Huazhong Agricultural University, Wuhan 430070, China
| | - Brian Oliver
- Section of Developmental Genomics, National Institute of Diabetes and Kidney and Digestive Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Zhen-Xia Chen
- Hubei Hongshan Laboratory, College of Biomedicine and Health, Huazhong Agricultural University, Wuhan 430070, China
- Section of Developmental Genomics, National Institute of Diabetes and Kidney and Digestive Diseases, National Institutes of Health, Bethesda, MD 20892, USA
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
- Interdisciplinary Sciences Institute, Huazhong Agricultural University, Wuhan 430070, China
- Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shenzhen 518000, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518000, China
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15
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Liebhoff AM, Menden K, Laschtowitz A, Franke A, Schramm C, Bonn S. Pathogen detection in RNA-seq data with Pathonoia. BMC Bioinformatics 2023; 24:53. [PMID: 36803415 PMCID: PMC9938591 DOI: 10.1186/s12859-023-05144-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 01/10/2023] [Indexed: 02/19/2023] Open
Abstract
BACKGROUND Bacterial and viral infections may cause or exacerbate various human diseases and to detect microbes in tissue, one method of choice is RNA sequencing. The detection of specific microbes using RNA sequencing offers good sensitivity and specificity, but untargeted approaches suffer from high false positive rates and a lack of sensitivity for lowly abundant organisms. RESULTS We introduce Pathonoia, an algorithm that detects viruses and bacteria in RNA sequencing data with high precision and recall. Pathonoia first applies an established k-mer based method for species identification and then aggregates this evidence over all reads in a sample. In addition, we provide an easy-to-use analysis framework that highlights potential microbe-host interactions by correlating the microbial to the host gene expression. Pathonoia outperforms state-of-the-art methods in microbial detection specificity, both on in silico and real datasets. CONCLUSION Two case studies in human liver and brain show how Pathonoia can support novel hypotheses on microbial infection exacerbating disease. The Python package for Pathonoia sample analysis and a guided analysis Jupyter notebook for bulk RNAseq datasets are available on GitHub.
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Affiliation(s)
- Anna-Maria Liebhoff
- Institute for Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. .,Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, USA.
| | - Kevin Menden
- grid.424247.30000 0004 0438 0426Department of Genome Biology of Neurodegenerative Diseases, DZNE, Tübingen, Germany
| | - Alena Laschtowitz
- grid.13648.380000 0001 2180 3484I. Department of Medicine, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Andre Franke
- grid.9764.c0000 0001 2153 9986Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Christoph Schramm
- grid.13648.380000 0001 2180 3484I. Department of Medicine, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany ,grid.13648.380000 0001 2180 3484Martin Zeitz Center for Rare Diseases, University Medical Center Hamburg-Eppendorf, Hamburg, Germany ,grid.13648.380000 0001 2180 3484Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan Bonn
- grid.13648.380000 0001 2180 3484Institute for Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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Altay G, Zapardiel-Gonzalo J, Peters B. RNA-seq preprocessing and sample size considerations for gene network inference. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.02.522518. [PMID: 36711979 PMCID: PMC9881880 DOI: 10.1101/2023.01.02.522518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Background Gene network inference (GNI) methods have the potential to reveal functional relationships between different genes and their products. Most GNI algorithms have been developed for microarray gene expression datasets and their application to RNA-seq data is relatively recent. As the characteristics of RNA-seq data are different from microarray data, it is an unanswered question what preprocessing methods for RNA-seq data should be applied prior to GNI to attain optimal performance, or what the required sample size for RNA-seq data is to obtain reliable GNI estimates. Results We ran 9144 analysis of 7 different RNA-seq datasets to evaluate 300 different preprocessing combinations that include data transformations, normalizations and association estimators. We found that there was no single best performing preprocessing combination but that there were several good ones. The performance varied widely over various datasets, which emphasized the importance of choosing an appropriate preprocessing configuration before GNI. Two preprocessing combinations appeared promising in general: First, Log-2 TPM (transcript per million) with Variance-stabilizing transformation (VST) and Pearson Correlation Coefficient (PCC) association estimator. Second, raw RNA-seq count data with PCC. Along with these two, we also identified 18 other good preprocessing combinations. Any of these algorithms might perform best in different datasets. Therefore, the GNI performances of these approaches should be measured on any new dataset to select the best performing one for it. In terms of the required biological sample size of RNA-seq data, we found that between 30 to 85 samples were required to generate reliable GNI estimates. Conclusions This study provides practical recommendations on default choices for data preprocessing prior to GNI analysis of RNA-seq data to obtain optimal performance results.
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Affiliation(s)
- Gökmen Altay
- La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | | | - Bjoern Peters
- La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
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17
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Thawng CN, Smith GB. A transcriptome software comparison for the analyses of treatments expected to give subtle gene expression responses. BMC Genomics 2022; 23:452. [PMID: 35725382 PMCID: PMC9208185 DOI: 10.1186/s12864-022-08673-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 05/26/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In this comparative study we evaluate the performance of four software tools: DNAstar-D (DESeq2), DNAstar-E (edgeR), CLC Genomics and Partek Flow for identification of differentially expressed genes (DEGs) using a transcriptome of E. coli. The RNA-seq data are from the effect of below-background radiation 5.5 nGy total dose (0.2nGy/hr) on E. coli grown shielded from natural radiation 655 m below ground in a pre-World War II steel vault. The gene expression response to three supplemented sources of radiation designed to mimic natural background, 1952 - 5720 nGy in total dose (71-208 nGy/hr), are compared to this "radiation-deprived" treatment. In addition, RNA-seq data of Caenorhabditis elegans nematode from similar radiation treatments was analyzed by three of the software packages. RESULTS In E. coli, the four software programs identified one of the supplementary sources of radiation (KCl) to evoke about 5 times more transcribed genes than the minus-radiation treatment (69-114 differentially expressed genes, DEGs), and so the rest of the analyses used this KCl vs "Minus" comparison. After imposing a 30-read minimum cutoff, one of the DNAStar options shared two of the three steps (mapping, normalization, and statistic) with Partek Flow (they both used median of ratios to normalize and the DESeq2 statistical package), and these two programs identified the highest number of DEGs in common with each other (53). In contrast, when the programs used different approaches in each of the three steps, between 31 and 40 DEGs were found in common. Regarding the extent of expression differences, three of the four programs gave high fold-change results (15-178 fold), but one (DNAstar's DESeq2) resulted in more conservative fold-changes (1.5-3.5). In a parallel study comparing three qPCR commercial validation software programs, these programs also gave variable results as to which genes were significantly regulated. Similarly, the C. elegans analysis showed exaggerated fold-changes in CLC and DNAstar's edgeR while DNAstar-D was more conservative. CONCLUSIONS Regarding the extent of expression (fold-change), and considering the subtlety of the very low level radiation treatments, in E. coli three of the four programs gave what we consider exaggerated fold-change results (15 - 178 fold), but one (DNAstar's DESeq2) gave more realistic fold-changes (1.5-3.5). When RT-qPCR validation comparisons to transcriptome results were carried out, they supported the more conservative DNAstar-D's expression results. When another model organism's (nematode) response to these radiation differences was similarly analyzed, DNAstar-D also resulted in the most conservative expression patterns. Therefore, we would propose DESeq2 ("DNAstar-D") as an appropriate software tool for differential gene expression studies for treatments expected to give subtle transcriptome responses.
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Affiliation(s)
- Cung Nawl Thawng
- Biology Department and Molecular Biology Program, New Mexico State University, Las Cruces, NM, USA
| | - Geoffrey Battle Smith
- Biology Department and Molecular Biology Program, New Mexico State University, Las Cruces, NM, USA.
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Pantaleón García J, Kulkarni VV, Reese TC, Wali S, Wase SJ, Zhang J, Singh R, Caetano MS, Kadara H, Moghaddam S, Johnson FM, Wang J, Wang Y, Evans S. OBIF: an omics-based interaction framework to reveal molecular drivers of synergy. NAR Genom Bioinform 2022; 4:lqac028. [PMID: 35387383 PMCID: PMC8982434 DOI: 10.1093/nargab/lqac028] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 02/28/2022] [Accepted: 03/10/2022] [Indexed: 01/08/2023] Open
Abstract
Bioactive molecule library screening may empirically identify effective combination therapies, but molecular mechanisms underlying favorable drug–drug interactions often remain unclear, precluding further rational design. In the absence of an accepted systems theory to interrogate synergistic responses, we introduce Omics-Based Interaction Framework (OBIF) to reveal molecular drivers of synergy through integration of statistical and biological interactions in synergistic biological responses. OBIF performs full factorial analysis of feature expression data from single versus dual exposures to identify molecular clusters that reveal synergy-mediating pathways, functions and regulators. As a practical demonstration, OBIF analyzed transcriptomic and proteomic data of a dyad of immunostimulatory molecules that induces synergistic protection against influenza A and revealed unanticipated NF-κB/AP-1 cooperation that is required for antiviral protection. To demonstrate generalizability, OBIF analyzed data from a diverse array of Omics platforms and experimental conditions, successfully identifying the molecular clusters driving their synergistic responses. Hence, unlike existing synergy quantification and prediction methods, OBIF is a phenotype-driven systems model that supports multiplatform interrogation of synergy mechanisms.
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Affiliation(s)
- Jezreel Pantaleón García
- Department of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA
| | - Vikram V Kulkarni
- Department of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA
- MD Anderson Cancer Center UT Health Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Tanner C Reese
- Department of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA
- Rice University, Houston, TX 77005, USA
| | - Shradha Wali
- Department of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA
- MD Anderson Cancer Center UT Health Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Saima J Wase
- Department of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA
| | - Jiexin Zhang
- Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Ratnakar Singh
- Department of Thoracic, Head and Neck Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Comparative Biosciences, University of Illinois at Urbana-Champaign, Urbana, IL 61802, USA
| | - Mauricio S Caetano
- Department of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA
| | - Humam Kadara
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Seyed Javad Moghaddam
- Department of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA
- MD Anderson Cancer Center UT Health Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Faye M Johnson
- Department of Thoracic, Head and Neck Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jing Wang
- Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Yongxing Wang
- Department of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA
| | - Scott E Evans
- Department of Pulmonary Medicine, University of Texas MD Anderson Cancer Center, HoustonTX 77030, USA
- MD Anderson Cancer Center UT Health Graduate School of Biomedical Sciences, Houston, TX 77030, USA
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Wang Z, Pan N, Yan J, Wan J, Wan C. Systematic Identification of Microproteins during the Development of Drosophila melanogaster. J Proteome Res 2022; 21:1114-1123. [PMID: 35227063 DOI: 10.1021/acs.jproteome.2c00004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Short open reading frame-encoded peptides (SEPs) are microproteins with less than 100 amino acids that play an essential role in the growth and development of organisms. There are plenty of short open reading frames in Drosophila melanogaster that potentially code polypeptides. We chose 11 time points during the life cycle of Drosophila to investigate microproteins, particularly those related to development. Finally, we identified a total of 410 microproteins, of which 27 were noncoding RNA-encoded proteins. Of the 410 microproteins, 74 were expressed in all stages from embryo to adults, whereas 300 microproteins were only found in one or two time points. Approximately, one-third of the microproteins were not reported previously and 44 were obtained from de novo sequencing, validated by synthetic peptides. These microproteins are related to the main bioprocesses of growth and development, such as multicellular organism reproduction, postmating behavior, and oviposition. Over half of the microproteins have predicted functional domains and are conserved across species, suggesting that these microproteins have critical functions in fly development. This work enriches the D. melanogaster proteome and provides a significant data resource for growth and development research.
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Affiliation(s)
- Zhiwei Wang
- School of Life Sciences and Hubei Key Laboratory of Genetic Regulation and Integrative Biology, Central China Normal University, Wuhan, Hubei 430079, People's Republic of China
| | - Ni Pan
- School of Life Sciences and Hubei Key Laboratory of Genetic Regulation and Integrative Biology, Central China Normal University, Wuhan, Hubei 430079, People's Republic of China
| | - Jiahao Yan
- School of Life Sciences and Hubei Key Laboratory of Genetic Regulation and Integrative Biology, Central China Normal University, Wuhan, Hubei 430079, People's Republic of China
| | - Jian Wan
- School of Life Sciences and Hubei Key Laboratory of Genetic Regulation and Integrative Biology, Central China Normal University, Wuhan, Hubei 430079, People's Republic of China
| | - Cuihong Wan
- School of Life Sciences and Hubei Key Laboratory of Genetic Regulation and Integrative Biology, Central China Normal University, Wuhan, Hubei 430079, People's Republic of China
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Tran DT, Might M. cdev: a ground-truth based measure to evaluate RNA-seq normalization performance. PeerJ 2021; 9:e12233. [PMID: 34707933 PMCID: PMC8496462 DOI: 10.7717/peerj.12233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 09/09/2021] [Indexed: 11/28/2022] Open
Abstract
Normalization of RNA-seq data has been an active area of research since the problem was first recognized a decade ago. Despite the active development of new normalizers, their performance measures have been given little attention. To evaluate normalizers, researchers have been relying on ad hoc measures, most of which are either qualitative, potentially biased, or easily confounded by parametric choices of downstream analysis. We propose a metric called condition-number based deviation, or cdev, to quantify normalization success. cdev measures how much an expression matrix differs from another. If a ground truth normalization is given, cdev can then be used to evaluate the performance of normalizers. To establish experimental ground truth, we compiled an extensive set of public RNA-seq assays with external spike-ins. This data collection, together with cdev, provides a valuable toolset for benchmarking new and existing normalization methods.
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Affiliation(s)
- Diem-Trang Tran
- School of Computing, University of Utah, Salt Lake City, UT, United States of America
| | - Matthew Might
- Hugh Kaul Precision Medicine Institute, University of Alabama at Birmingham, Birmingham, AL, United States of America
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Lahens NF, Brooks TG, Sarantopoulou D, Nayak S, Lawrence C, Mrčela A, Srinivasan A, Schug J, Hogenesch JB, Barash Y, Grant GR. CAMPAREE: a robust and configurable RNA expression simulator. BMC Genomics 2021; 22:692. [PMID: 34563123 PMCID: PMC8467241 DOI: 10.1186/s12864-021-07934-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 08/17/2021] [Indexed: 11/10/2022] Open
Abstract
Background The accurate interpretation of RNA-Seq data presents a moving target as scientists continue to introduce new experimental techniques and analysis algorithms. Simulated datasets are an invaluable tool to accurately assess the performance of RNA-Seq analysis methods. However, existing RNA-Seq simulators focus on modeling the technical biases and artifacts of sequencing, rather than on simulating the original RNA samples. A first step in simulating RNA-Seq is to simulate RNA. Results To fill this need, we developed the Configurable And Modular Program Allowing RNA Expression Emulation (CAMPAREE), a simulator using empirical data to simulate diploid RNA samples at the level of individual molecules. We demonstrated CAMPAREE’s use for generating idealized coverage plots from real data, and for adding the ability to generate allele-specific data to existing RNA-Seq simulators that do not natively support this feature. Conclusions Separating input sample modeling from library preparation/sequencing offers added flexibility for both users and developers to mix-and-match different sample and sequencing simulators to suit their specific needs. Furthermore, the ability to maintain sample and sequencing simulators independently provides greater agility to incorporate new biological findings about transcriptomics and new developments in sequencing technologies. Additionally, by simulating at the level of individual molecules, CAMPAREE has the potential to model molecules transcribed from the same genes as a heterogeneous population of transcripts with different states of degradation and processing (splicing, editing, etc.). CAMPAREE was developed in Python, is open source, and freely available at https://github.com/itmat/CAMPAREE. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-07934-2.
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Affiliation(s)
- Nicholas F Lahens
- The Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Thomas G Brooks
- The Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Dimitra Sarantopoulou
- The Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Present address: National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Soumyashant Nayak
- Statistics and Mathematics Unit, Indian Statistical Institute, Bengaluru, Karnataka, India
| | - Cris Lawrence
- The Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Antonijo Mrčela
- The Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Anand Srinivasan
- Perelman School of Medicine, Enterprise Research Applications and High Performance Computing, Penn Medicine Academic Computing Services, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jonathan Schug
- The Institute for Diabetes, Obesity and Metabolism, The Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - John B Hogenesch
- Division of Human Genetics, Department of Pediatrics, Center for Chronobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Yoseph Barash
- The Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Gregory R Grant
- The Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA. .,The Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
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22
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Tang J, Fu J, Wang Y, Li B, Li Y, Yang Q, Cui X, Hong J, Li X, Chen Y, Xue W, Zhu F. ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies. Brief Bioinform 2021; 21:621-636. [PMID: 30649171 PMCID: PMC7299298 DOI: 10.1093/bib/bby127] [Citation(s) in RCA: 131] [Impact Index Per Article: 43.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Revised: 11/19/2018] [Accepted: 12/06/2018] [Indexed: 12/13/2022] Open
Abstract
Label-free quantification (LFQ) with a specific and sequentially integrated workflow of acquisition technique, quantification tool and processing method has emerged as the popular technique employed in metaproteomic research to provide a comprehensive landscape of the adaptive response of microbes to external stimuli and their interactions with other organisms or host cells. The performance of a specific LFQ workflow is highly dependent on the studied data. Hence, it is essential to discover the most appropriate one for a specific data set. However, it is challenging to perform such discovery due to the large number of possible workflows and the multifaceted nature of the evaluation criteria. Herein, a web server ANPELA (https://idrblab.org/anpela/) was developed and validated as the first tool enabling performance assessment of whole LFQ workflow (collective assessment by five well-established criteria with distinct underlying theories), and it enabled the identification of the optimal LFQ workflow(s) by a comprehensive performance ranking. ANPELA not only automatically detects the diverse formats of data generated by all quantification tools but also provides the most complete set of processing methods among the available web servers and stand-alone tools. Systematic validation using metaproteomic benchmarks revealed ANPELA's capabilities in 1 discovering well-performing workflow(s), (2) enabling assessment from multiple perspectives and (3) validating LFQ accuracy using spiked proteins. ANPELA has a unique ability to evaluate the performance of whole LFQ workflow and enables the discovery of the optimal LFQs by the comprehensive performance ranking of all 560 workflows. Therefore, it has great potential for applications in metaproteomic and other studies requiring LFQ techniques, as many features are shared among proteomic studies.
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Affiliation(s)
- Jing Tang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Jianbo Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Bo Li
- School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Yinghong Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Qingxia Yang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Xuejiao Cui
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Jiajun Hong
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Xiaofeng Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Yuzong Chen
- Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Singapore, Singapore
| | - Weiwei Xue
- School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
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23
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Cui W, Xue H, Geng Y, Zhang J, Liang Y, Tian X, Wang Q. Effect of high variation in transcript expression on identifying differentially expressed genes in RNA-seq analysis. Ann Hum Genet 2021; 85:235-244. [PMID: 34341986 DOI: 10.1111/ahg.12441] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 07/04/2021] [Accepted: 07/15/2021] [Indexed: 12/13/2022]
Abstract
Great efforts have been made on the algorithms that deal with RNA-seq data to enhance the accuracy and efficiency of differential expression (DE) analysis. However, no consensus has been reached on the proper threshold values of fold change and adjusted p-value for filtering differentially expressed genes (DEGs). It is generally believed that the more stringent the filtering threshold, the more reliable the result of a DE analysis. Nevertheless, by analyzing the impact of both adjusted p-value and fold change thresholds on DE analyses, with RNA-seq data obtained for three different cancer types from the Cancer Genome Atlas (TCGA) database, we found that, for a given sample size, the reproducibility of DE results became poorer when more stringent thresholds were applied. No matter which threshold level was applied, the overlap rates of DEGs were generally lower for small sample sizes than for large sample sizes. The raw read count analysis demonstrated that the transcript expression of the same gene in different samples, whether in tumor groups or in normal groups, showed high variations, which resulted in a drastic fluctuation in fold change values and adjustedp-values when different sets of samples were used. Overall, more stringent thresholds did not yield more reliable DEGs due to high variations in transcript expression; the reliability of DEGs obtained with small sample sizes was more susceptible to these variations. Therefore, less stringent thresholds are recommended for screening DEGs. Moreover, large sample sizes should be considered in RNA-seq experimental designs to reduce the interfering effect of variations in transcript expression on DEG identification.
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Affiliation(s)
- Weitong Cui
- Key Laboratory of Biomedical Engineering & Technology of Shandong High School, Qilu Medical University, Zibo, P. R. China
| | - Huaru Xue
- Key Laboratory of Biomedical Engineering & Technology of Shandong High School, Qilu Medical University, Zibo, P. R. China
| | - Yifan Geng
- Key Laboratory of Biomedical Engineering & Technology of Shandong High School, Qilu Medical University, Zibo, P. R. China.,Xuzhou Medical University, Xuzhou, P. R. China
| | - Jing Zhang
- Key Laboratory of Biomedical Engineering & Technology of Shandong High School, Qilu Medical University, Zibo, P. R. China
| | - Yajun Liang
- Key Laboratory of Biomedical Engineering & Technology of Shandong High School, Qilu Medical University, Zibo, P. R. China
| | - Xuewen Tian
- Shandong Sport University, Jinan, P. R. China
| | - Qinglu Wang
- Key Laboratory of Biomedical Engineering & Technology of Shandong High School, Qilu Medical University, Zibo, P. R. China.,Shandong Sport University, Jinan, P. R. China
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24
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Johnson LJ, Azari S, Webb A, Zhang X, Gavrilin MA, Marshall JM, Rood K, Seveau S. Human Placental Trophoblasts Infected by Listeria monocytogenes Undergo a Pro-Inflammatory Switch Associated With Poor Pregnancy Outcomes. Front Immunol 2021; 12:709466. [PMID: 34367171 PMCID: PMC8346206 DOI: 10.3389/fimmu.2021.709466] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 07/05/2021] [Indexed: 12/21/2022] Open
Abstract
The placenta controls the growth of the fetus and ensures its immune protection. Key to these functions, the syncytiotrophoblast (SYN) is a syncytium formed by fusion of underlying mononuclear trophoblasts. The SYN covers the placental surface and is bathed in maternal blood to mediate nutritional and waste exchanges between the mother and fetus. The bacterial pathogen Listeria monocytogenes breaches the trophoblast barrier and infects the placental/fetal unit resulting in poor pregnancy outcomes. In this work, we analyzed the L. monocytogenes intracellular lifecycle in primary human trophoblasts. In accordance with previous studies, we found that the SYN is 20-fold more resistant to infection compared to mononuclear trophoblasts, forming a protective barrier to infection at the maternal interface. We show for the first time that this is due to a significant reduction in L. monocytogenes uptake by the SYN rather than inhibition of the bacterial intracellular division or motility. We here report the first transcriptomic analysis of L. monocytogenes-infected trophoblasts (RNA sequencing). Pathway analysis showed that infection upregulated TLR2, NOD-like, and cytosolic DNA sensing pathways, as well as downstream pro-inflammatory circuitry (NF-κB, AP-1, IRF4, IRF7) leading to the production of mediators known to elicit the recruitment and activation of maternal leukocytes (IL8, IL6, TNFα, MIP-1). Signature genes associated with poor pregnancy outcomes were also upregulated upon infection. Measuring the release of 54 inflammatory mediators confirmed the transcriptomic data and revealed sustained production of tolerogenic factors (IL-27, IL-10, IL-1RA, TSLP) despite infection. Both the SYN and mononuclear trophoblasts produced cytokines, but surprisingly, some cytokines were predominantly produced by the SYN (IL-8, IL-6) or by non-fused trophoblasts (TNFα). Collectively, our data support that trophoblasts act as placental gatekeepers that limit and detect L. monocytogenes infection resulting in a pro-inflammatory response, which may contribute to the poor pregnancy outcomes if the pathogen persists.
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Affiliation(s)
- Lauren J Johnson
- Department of Microbial Infection and Immunity, The Ohio State University, Columbus, OH, United States.,Department of Microbiology, The Ohio State University, Columbus, OH, United States
| | - Siavash Azari
- Department of Microbial Infection and Immunity, The Ohio State University, Columbus, OH, United States.,Department of Microbiology, The Ohio State University, Columbus, OH, United States
| | - Amy Webb
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
| | - Xiaoli Zhang
- Department of Biomedical Informatics, Center for Biostatistics, The Ohio State University, Columbus, OH, United States
| | - Mikhail A Gavrilin
- Pulmonary, Critical Care and Sleep Medicine Division, Department of Internal Medicine, The Ohio State University, Columbus, OH, United States
| | - Joanna M Marshall
- Department of Microbial Infection and Immunity, The Ohio State University, Columbus, OH, United States
| | - Kara Rood
- Department of Obstetrics and Gynecology, Division of Maternal Fetal Medicine, The Ohio State University, Columbus, OH, United States
| | - Stephanie Seveau
- Department of Microbial Infection and Immunity, The Ohio State University, Columbus, OH, United States.,Department of Microbiology, The Ohio State University, Columbus, OH, United States.,Infectious Diseases Institute, The Ohio State University, Columbus, OH, United States
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25
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Zhang Y, Thompson KN, Branck T, Yan Yan, Nguyen LH, Franzosa EA, Huttenhower C. Metatranscriptomics for the Human Microbiome and Microbial Community Functional Profiling. Annu Rev Biomed Data Sci 2021; 4:279-311. [PMID: 34465175 DOI: 10.1146/annurev-biodatasci-031121-103035] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Shotgun metatranscriptomics (MTX) is an increasingly practical way to survey microbial community gene function and regulation at scale. This review begins by summarizing the motivations for community transcriptomics and the history of the field. We then explore the principles, best practices, and challenges of contemporary MTX workflows: beginning with laboratory methods for isolation and sequencing of community RNA, followed by informatics methods for quantifying RNA features, and finally statistical methods for detecting differential expression in a community context. In thesecond half of the review, we survey important biological findings from the MTX literature, drawing examples from the human microbiome, other (nonhuman) host-associated microbiomes, and the environment. Across these examples, MTX methods prove invaluable for probing microbe-microbe and host-microbe interactions, the dynamics of energy harvest and chemical cycling, and responses to environmental stresses. We conclude with a review of open challenges in the MTX field, including making assays and analyses more robust, accessible, and adaptable to new technologies; deciphering roles for millions of uncharacterized microbial transcripts; and solving applied problems such as biomarker discovery and development of microbial therapeutics.
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Affiliation(s)
- Yancong Zhang
- Harvard Chan Microbiome in Public Health Center and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts 02115, USA; , .,Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Kelsey N Thompson
- Harvard Chan Microbiome in Public Health Center and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts 02115, USA; , .,Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Tobyn Branck
- Harvard Chan Microbiome in Public Health Center and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts 02115, USA; , .,Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.,Department of Systems, Synthetic, and Quantitative Biology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Yan Yan
- Harvard Chan Microbiome in Public Health Center and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts 02115, USA; , .,Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Long H Nguyen
- Harvard Chan Microbiome in Public Health Center and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts 02115, USA; , .,Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA.,Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02108, USA
| | - Eric A Franzosa
- Harvard Chan Microbiome in Public Health Center and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts 02115, USA; , .,Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Curtis Huttenhower
- Harvard Chan Microbiome in Public Health Center and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts 02115, USA; , .,Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.,Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, Massachusetts 02115, USA
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26
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Zhang Y, Thompson KN, Huttenhower C, Franzosa EA. Statistical approaches for differential expression analysis in metatranscriptomics. Bioinformatics 2021; 37:i34-i41. [PMID: 34252963 PMCID: PMC8275336 DOI: 10.1093/bioinformatics/btab327] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Motivation Metatranscriptomics (MTX) has become an increasingly practical way to profile the functional activity of microbial communities in situ. However, MTX remains underutilized due to experimental and computational limitations. The latter are complicated by non-independent changes in both RNA transcript levels and their underlying genomic DNA copies (as microbes simultaneously change their overall abundance in the population and regulate individual transcripts), genetic plasticity (as whole loci are frequently gained and lost in microbial lineages) and measurement compositionality and zero-inflation. Here, we present a systematic evaluation of and recommendations for differential expression (DE) analysis in MTX. Results We designed and assessed six statistical models for DE discovery in MTX that incorporate different combinations of DNA and RNA normalization and assumptions about the underlying changes of gene copies or species abundance within communities. We evaluated these models on multiple simulated and real multi-omic datasets. Models adjusting transcripts relative to their encoding gene copies as a covariate were significantly more accurate in identifying DE from MTX in both simulated and real datasets. Moreover, we show that when paired DNA measurements (metagenomic data) are not available, models normalizing MTX measurements within-species while also adjusting for total-species RNA balance sensitivity, specificity and interpretability of DE detection, as does filtering likely technical zeros. The efficiency and accuracy of these models pave the way for more effective MTX-based DE discovery in microbial communities. Availability and implementation The analysis code and synthetic datasets used in this evaluation are available online at http://huttenhower.sph.harvard.edu/mtx2021. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yancong Zhang
- Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.,Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kelsey N Thompson
- Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.,Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Curtis Huttenhower
- Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.,Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.,Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Eric A Franzosa
- Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.,Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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27
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Grimes T, Datta S. SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data. J Stat Softw 2021; 98:10.18637/jss.v098.i12. [PMID: 34321962 PMCID: PMC8315007 DOI: 10.18637/jss.v098.i12] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Gene expression data provide an abundant resource for inferring connections in gene regulatory networks. While methodologies developed for this task have shown success, a challenge remains in comparing the performance among methods. Gold-standard datasets are scarce and limited in use. And while tools for simulating expression data are available, they are not designed to resemble the data obtained from RNA-seq experiments. SeqNet is an R package that provides tools for generating a rich variety of gene network structures and simulating RNA-seq data from them. This produces in silico RNA-seq data for benchmarking and assessing gene network inference methods. The package is available on CRAN and on GitHub at https://github.com/tgrimes/SeqNet.
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Affiliation(s)
- Tyler Grimes
- Univeristy of Florida, Department of Biostatistics
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28
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Zhao Y, Li MC, Konaté MM, Chen L, Das B, Karlovich C, Williams PM, Evrard YA, Doroshow JH, McShane LM. TPM, FPKM, or Normalized Counts? A Comparative Study of Quantification Measures for the Analysis of RNA-seq Data from the NCI Patient-Derived Models Repository. J Transl Med 2021; 19:269. [PMID: 34158060 PMCID: PMC8220791 DOI: 10.1186/s12967-021-02936-w] [Citation(s) in RCA: 142] [Impact Index Per Article: 47.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 06/10/2021] [Indexed: 12/18/2022] Open
Abstract
Background In order to correctly decode phenotypic information from RNA-sequencing (RNA-seq) data, careful selection of the RNA-seq quantification measure is critical for inter-sample comparisons and for downstream analyses, such as differential gene expression between two or more conditions. Several methods have been proposed and continue to be used. However, a consensus has not been reached regarding the best gene expression quantification method for RNA-seq data analysis. Methods In the present study, we used replicate samples from each of 20 patient-derived xenograft (PDX) models spanning 15 tumor types, for a total of 61 human tumor xenograft samples available through the NCI patient-derived model repository (PDMR). We compared the reproducibility across replicate samples based on TPM (transcripts per million), FPKM (fragments per kilobase of transcript per million fragments mapped), and normalized counts using coefficient of variation, intraclass correlation coefficient, and cluster analysis. Results Our results revealed that hierarchical clustering on normalized count data tended to group replicate samples from the same PDX model together more accurately than TPM and FPKM data. Furthermore, normalized count data were observed to have the lowest median coefficient of variation (CV), and highest intraclass correlation (ICC) values across all replicate samples from the same model and for the same gene across all PDX models compared to TPM and FPKM data. Conclusion We provided compelling evidence for a preferred quantification measure to conduct downstream analyses of PDX RNA-seq data. To our knowledge, this is the first comparative study of RNA-seq data quantification measures conducted on PDX models, which are known to be inherently more variable than cell line models. Our findings are consistent with what others have shown for human tumors and cell lines and add further support to the thesis that normalized counts are the best choice for the analysis of RNA-seq data across samples. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-021-02936-w.
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Affiliation(s)
- Yingdong Zhao
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Rockville, MD, USA
| | - Ming-Chung Li
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Rockville, MD, USA
| | - Mariam M Konaté
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Rockville, MD, USA
| | - Li Chen
- Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Biswajit Das
- Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Chris Karlovich
- Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - P Mickey Williams
- Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Yvonne A Evrard
- Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - James H Doroshow
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, USA
| | - Lisa M McShane
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Rockville, MD, USA.
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29
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Yang J, Wang D, Yang Y, Yang W, Jin W, Niu X, Gong J. A systematic comparison of normalization methods for eQTL analysis. Brief Bioinform 2021; 22:6278608. [PMID: 34015824 DOI: 10.1093/bib/bbab193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 04/14/2021] [Accepted: 04/28/2021] [Indexed: 11/15/2022] Open
Abstract
Expression quantitative trait loci (eQTL) analysis has been widely used in interpreting disease-associated loci through correlating genetic variant loci with the expression of specific genes. RNA-sequencing (RNA-Seq), which can quantify gene expression at the genome-wide level, is often used in eQTL identification. Since different normalization methods of gene expression have substantial impacts on RNA-seq downstream analysis, it is of great necessity to systematically compare the effects of these methods on eQTL identification. Here, by using RNA-seq and genotype data of four different cancers in The Cancer Genome Atlas (TCGA) database, we comprehensively evaluated the effect of eight commonly used normalization methods on eQTL identification. Our results showed that the application of different methods could cause 20-30% differences in the final results of eQTL identification. Among these methods, COUNT, Median of Ratio (MED) and Trimmed Mean of M-values (TMM) generated similar results for identifying eQTLs, while Fragments Per Kilobase Million (FPKM) or RANK produced more differential results compared with other methods. Based on the accuracy and receiver operating characteristic (ROC) curve, the TMM method was found to be the optimal method for normalizing gene expression data in eQTLs analysis. In addition, we also evaluated the performance of different pairwise combinations of these methods. As a result, compared with single normalization methods, the combination of methods can not only identify more cis-eQTLs, but also improve the performance of the ROC curve. Overall, this study provides a comprehensive comparison of normalization methods for identifying eQTLs from RNA-seq data, and proposes some practical recommendations for diverse scenarios.
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Affiliation(s)
- Jiajun Yang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P. R. China
| | - Dongyang Wang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P. R. China
| | - Yanbo Yang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P. R. China
| | - Wenqian Yang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P. R. China
| | - Weiwei Jin
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P. R. China
| | - Xiaohui Niu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P. R. China
| | - Jing Gong
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P. R. China.,College of Biomedicine and Health, Huazhong Agricultural University, Wuhan 430070, P. R. China
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Song Y, Hanner RH, Meng B. Probing into the Effects of Grapevine Leafroll-Associated Viruses on the Physiology, Fruit Quality and Gene Expression of Grapes. Viruses 2021; 13:v13040593. [PMID: 33807294 PMCID: PMC8066071 DOI: 10.3390/v13040593] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 03/23/2021] [Accepted: 03/26/2021] [Indexed: 12/19/2022] Open
Abstract
Grapevine leafroll is one of the most widespread and highly destructive grapevine diseases that is responsible for great economic losses to the grape and wine industries throughout the world. Six distinct viruses have been implicated in this disease complex. They belong to three genera, all in the family Closteroviridae. For the sake of convenience, these viruses are named as grapevine leafroll-associated viruses (GLRaV-1, -2, -3, -4, -7, and -13). However, their etiological role in the disease has yet to be established. Furthermore, how infections with each GLRaV induce the characteristic disease symptoms remains unresolved. Here, we first provide a brief overview on each of these GLRaVs with a focus on genome structure, expression strategies and gene functions, where available. We then provide a review on the effects of GLRaV infection on the physiology, fruit quality, fruit chemical composition, and gene expression of grapevine based on the limited information so far reported in the literature. We outline key methodologies that have been used to study how GLRaV infections alter gene expression in the grapevine host at the transcriptomic level. Finally, we present a working model as an initial attempt to explain how infections with GLRaVs lead to the characteristic symptoms of grapevine leafroll disease: leaf discoloration and downward rolling. It is our hope that this review will serve as a starting point for grapevine virology and the related research community to tackle this vastly important and yet virtually uncharted territory in virus-host interactions involving woody and perennial fruit crops.
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Affiliation(s)
- Yashu Song
- Department of Molecular and Cellular Biology, University of Guelph, Guelph, ON N1G 2W1, Canada;
| | - Robert H. Hanner
- Department of Integrative Biology and Biodiversity Institute of Ontario, University of Guelph, Guelph, ON N1G 2W1, Canada;
| | - Baozhong Meng
- Department of Molecular and Cellular Biology, University of Guelph, Guelph, ON N1G 2W1, Canada;
- Correspondence: ; Tel.: +1-519-824-4120 (ext. 53876)
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Mohamed RI, Bargal SA, Mekawy AS, El-Shiekh I, Tuncbag N, Ahmed AS, Badr E, Elserafy M. The overexpression of DNA repair genes in invasive ductal and lobular breast carcinomas: Insights on individual variations and precision medicine. PLoS One 2021; 16:e0247837. [PMID: 33662042 PMCID: PMC7932549 DOI: 10.1371/journal.pone.0247837] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 02/14/2021] [Indexed: 12/22/2022] Open
Abstract
In the era of precision medicine, analyzing the transcriptomic profile of patients is essential to tailor the appropriate therapy. In this study, we explored transcriptional differences between two invasive breast cancer subtypes; infiltrating ductal carcinoma (IDC) and lobular carcinoma (LC) using RNA-Seq data deposited in the TCGA-BRCA project. We revealed 3854 differentially expressed genes between normal ductal tissues and IDC. In addition, IDC to LC comparison resulted in 663 differentially expressed genes. We then focused on DNA repair genes because of their known effects on patients' response to therapy and resistance. We here report that 36 DNA repair genes are overexpressed in a significant number of both IDC and LC patients' samples. Despite the upregulation in a significant number of samples, we observed a noticeable variation in the expression levels of the repair genes across patients of the same cancer subtype. The same trend is valid for the expression of miRNAs, where remarkable variations between patients' samples of the same cancer subtype are also observed. These individual variations could lie behind the differential response of patients to treatment. The future of cancer diagnostics and therapy will inevitably depend on high-throughput genomic and transcriptomic data analysis. However, we propose that performing analysis on individual patients rather than a big set of patients' samples will be necessary to ensure that the best treatment is determined, and therapy resistance is reduced.
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Affiliation(s)
- Ruwaa I. Mohamed
- Center for Informatics Sciences (CIS), Nile University, Giza, Egypt
| | - Salma A. Bargal
- Center for Genomics, Helmy Institute for Medical Sciences, Zewail City of Science and Technology, Giza, Egypt
| | - Asmaa S. Mekawy
- Center for Genomics, Helmy Institute for Medical Sciences, Zewail City of Science and Technology, Giza, Egypt
- University of Science and Technology, Zewail City of Science and Technology, Giza, Egypt
| | - Iman El-Shiekh
- Center for Genomics, Helmy Institute for Medical Sciences, Zewail City of Science and Technology, Giza, Egypt
- University of Science and Technology, Zewail City of Science and Technology, Giza, Egypt
| | - Nurcan Tuncbag
- Graduate School of Informatics, Department of Health Informatics, Middle East Technical University, Ankara, Turkey
| | - Alaa S. Ahmed
- Center for Genomics, Helmy Institute for Medical Sciences, Zewail City of Science and Technology, Giza, Egypt
- University of Science and Technology, Zewail City of Science and Technology, Giza, Egypt
| | - Eman Badr
- University of Science and Technology, Zewail City of Science and Technology, Giza, Egypt
- Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt
- * E-mail: (EB); (ME)
| | - Menattallah Elserafy
- Center for Genomics, Helmy Institute for Medical Sciences, Zewail City of Science and Technology, Giza, Egypt
- University of Science and Technology, Zewail City of Science and Technology, Giza, Egypt
- * E-mail: (EB); (ME)
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32
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Fu J, Luo Y, Mou M, Zhang H, Tang J, Wang Y, Zhu F. Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection. Curr Drug Targets 2021; 21:34-54. [PMID: 31433754 DOI: 10.2174/1389450120666190821160207] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 07/17/2019] [Accepted: 07/24/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets. OBJECTIVE The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics. METHODS Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics. RESULTS In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed. CONCLUSION In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.
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Affiliation(s)
- Jianbo Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Hongning Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jing Tang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,School of Pharmaceutical Sciences and Innovative Drug Research Centre, Chongqing University, Chongqing 401331, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,School of Pharmaceutical Sciences and Innovative Drug Research Centre, Chongqing University, Chongqing 401331, China
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Wallace MA, Coffman KA, Gilbert C, Ravindran S, Albery GF, Abbott J, Argyridou E, Bellosta P, Betancourt AJ, Colinet H, Eric K, Glaser-Schmitt A, Grath S, Jelic M, Kankare M, Kozeretska I, Loeschcke V, Montchamp-Moreau C, Ometto L, Onder BS, Orengo DJ, Parsch J, Pascual M, Patenkovic A, Puerma E, Ritchie MG, Rota-Stabelli O, Schou MF, Serga SV, Stamenkovic-Radak M, Tanaskovic M, Veselinovic MS, Vieira J, Vieira CP, Kapun M, Flatt T, González J, Staubach F, Obbard DJ. The discovery, distribution, and diversity of DNA viruses associated with Drosophila melanogaster in Europe. Virus Evol 2021; 7:veab031. [PMID: 34408913 PMCID: PMC8363768 DOI: 10.1093/ve/veab031] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Drosophila melanogaster is an important model for antiviral immunity in arthropods, but very few DNA viruses have been described from the family Drosophilidae. This deficiency limits our opportunity to use natural host-pathogen combinations in experimental studies, and may bias our understanding of the Drosophila virome. Here, we report fourteen DNA viruses detected in a metagenomic analysis of 6668 pool-sequenced Drosophila, sampled from forty-seven European locations between 2014 and 2016. These include three new nudiviruses, a new and divergent entomopoxvirus, a virus related to Leptopilina boulardi filamentous virus, and a virus related to Musca domestica salivary gland hypertrophy virus. We also find an endogenous genomic copy of galbut virus, a double-stranded RNA partitivirus, segregating at very low frequency. Remarkably, we find that Drosophila Vesanto virus, a small DNA virus previously described as a bidnavirus, may be composed of up to twelve segments and thus represent a new lineage of segmented DNA viruses. Two of the DNA viruses, Drosophila Kallithea nudivirus and Drosophila Vesanto virus are relatively common, found in 2 per cent or more of wild flies. The others are rare, with many likely to be represented by a single infected fly. We find that virus prevalence in Europe reflects the prevalence seen in publicly available datasets, with Drosophila Kallithea nudivirus and Drosophila Vesanto virus the only ones commonly detectable in public data from wild-caught flies and large population cages, and the other viruses being rare or absent. These analyses suggest that DNA viruses are at lower prevalence than RNA viruses in D.melanogaster, and may be less likely to persist in laboratory cultures. Our findings go some way to redressing an earlier bias toward RNA virus studies in Drosophila, and lay the foundation needed to harness the power of Drosophila as a model system for the study of DNA viruses.
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Affiliation(s)
- Megan A Wallace
- The European Drosophila Population Genomics Consortium (DrosEU)
- Ashworth Laboratories, Institute of Evolutionary Biology, University of Edinburgh, Charlotte Auerbach Road, Edinburgh EH9 3FL, UK
| | - Kelsey A Coffman
- Department of Entomology, University of Georgia, Athens, GA, USA
| | - Clément Gilbert
- The European Drosophila Population Genomics Consortium (DrosEU)
- Université Paris-Saclay, CNRS, IRD, UMR Évolution, Génomes, Comportement et Écologie, 91198 Gif-sur-Yvette, France
| | - Sanjana Ravindran
- Ashworth Laboratories, Institute of Evolutionary Biology, University of Edinburgh, Charlotte Auerbach Road, Edinburgh EH9 3FL, UK
| | - Gregory F Albery
- Department of Biology, Georgetown University, Washington, DC, USA
| | - Jessica Abbott
- The European Drosophila Population Genomics Consortium (DrosEU)
- Department of Biology, Section for Evolutionary Ecology, Lund University, Sölvegatan 37, Lund 223 62, Sweden
| | - Eliza Argyridou
- The European Drosophila Population Genomics Consortium (DrosEU)
- Division of Evolutionary Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg, Germany
| | - Paola Bellosta
- The European Drosophila Population Genomics Consortium (DrosEU)
- Department of Cellular, Computational and Integrative Biology, CIBIO University of Trento, Via Sommarive 9, Trento 38123, Italy
- Department of Medicine & Endocrinology, NYU Langone Medical Center, 550 First Avenue, New York, NY 10016, USA
| | - Andrea J Betancourt
- The European Drosophila Population Genomics Consortium (DrosEU)
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK
| | - Hervé Colinet
- The European Drosophila Population Genomics Consortium (DrosEU)
- UMR CNRS 6553 ECOBIO, Université de Rennes1, Rennes, France
| | - Katarina Eric
- The European Drosophila Population Genomics Consortium (DrosEU)
- Institute for Biological Research “Sinisa Stankovic”, National Institute of Republic of Serbia, University of Belgrade, Bulevar despota Stefana 142, Belgrade, Serbia
| | - Amanda Glaser-Schmitt
- The European Drosophila Population Genomics Consortium (DrosEU)
- Division of Evolutionary Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg, Germany
| | - Sonja Grath
- The European Drosophila Population Genomics Consortium (DrosEU)
- Division of Evolutionary Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg, Germany
| | - Mihailo Jelic
- The European Drosophila Population Genomics Consortium (DrosEU)
- Faculty of Biology, University of Belgrade, Studentski trg 16, Belgrade, Serbia
| | - Maaria Kankare
- The European Drosophila Population Genomics Consortium (DrosEU)
- Department of Biological and Environmental Science, University of Jyväskylä, Finland
| | - Iryna Kozeretska
- The European Drosophila Population Genomics Consortium (DrosEU)
- National Antarctic Scientific Center of Ukraine, 16 Shevchenko Avenue, Kyiv, 01601, Ukraine
| | - Volker Loeschcke
- The European Drosophila Population Genomics Consortium (DrosEU)
- Department of Biology, Genetics, Ecology and Evolution, Aarhus University, Ny Munkegade 116, Aarhus C DK-8000, Denmark
| | - Catherine Montchamp-Moreau
- The European Drosophila Population Genomics Consortium (DrosEU)
- Université Paris-Saclay, CNRS, IRD, UMR Évolution, Génomes, Comportement et Écologie, 91198 Gif-sur-Yvette, France
| | - Lino Ometto
- The European Drosophila Population Genomics Consortium (DrosEU)
- Department of Biology and Biotechnology, University of Pavia, Pavia 27100, Italy
| | - Banu Sebnem Onder
- The European Drosophila Population Genomics Consortium (DrosEU)
- Department of Biology, Faculty of Science, Hacettepe University, Ankara, Turkey
| | - Dorcas J Orengo
- The European Drosophila Population Genomics Consortium (DrosEU)
- Departament de Genètica, Microbiologia i Estadística and Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Spain
| | - John Parsch
- The European Drosophila Population Genomics Consortium (DrosEU)
- Division of Evolutionary Biology, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg, Germany
| | - Marta Pascual
- The European Drosophila Population Genomics Consortium (DrosEU)
- Departament de Genètica, Microbiologia i Estadística and Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Spain
| | - Aleksandra Patenkovic
- The European Drosophila Population Genomics Consortium (DrosEU)
- Institute for Biological Research “Sinisa Stankovic”, National Institute of Republic of Serbia, University of Belgrade, Bulevar despota Stefana 142, Belgrade, Serbia
| | - Eva Puerma
- The European Drosophila Population Genomics Consortium (DrosEU)
- Departament de Genètica, Microbiologia i Estadística and Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Spain
| | - Michael G Ritchie
- The European Drosophila Population Genomics Consortium (DrosEU)
- Centre for Biological Diversity, St Andrews University, St Andrews HY15 4SS, UK
| | - Omar Rota-Stabelli
- The European Drosophila Population Genomics Consortium (DrosEU)
- Research and Innovation Center, Fondazione E. Mach, San Michele all’Adige (TN) 38010, Italy
- Centre Agriculture Food Environment, University of Trento, San Michele all’Adige (TN) 38010, Italy
| | - Mads Fristrup Schou
- The European Drosophila Population Genomics Consortium (DrosEU)
- Department of Biology, Section for Evolutionary Ecology, Lund University, Sölvegatan 37, Lund 223 62, Sweden
- Department of Bioscience, Aarhus University, Aarhus, Denmark
| | - Svitlana V Serga
- The European Drosophila Population Genomics Consortium (DrosEU)
- National Antarctic Scientific Center of Ukraine, 16 Shevchenko Avenue, Kyiv, 01601, Ukraine
- Taras Shevchenko National University of Kyiv, 64 Volodymyrska str, Kyiv 01601, Ukraine
| | - Marina Stamenkovic-Radak
- The European Drosophila Population Genomics Consortium (DrosEU)
- Faculty of Biology, University of Belgrade, Studentski trg 16, Belgrade, Serbia
| | - Marija Tanaskovic
- The European Drosophila Population Genomics Consortium (DrosEU)
- Institute for Biological Research “Sinisa Stankovic”, National Institute of Republic of Serbia, University of Belgrade, Bulevar despota Stefana 142, Belgrade, Serbia
| | - Marija Savic Veselinovic
- The European Drosophila Population Genomics Consortium (DrosEU)
- Faculty of Biology, University of Belgrade, Studentski trg 16, Belgrade, Serbia
| | - Jorge Vieira
- The European Drosophila Population Genomics Consortium (DrosEU)
- Instituto de Biologia Molecular e Celular (IBMC), University of Porto, Porto, Portugal
- Instituto de Investigação e Inovação em Saúde, University of Porto, i3S, Porto, Portugal
| | - Cristina P Vieira
- The European Drosophila Population Genomics Consortium (DrosEU)
- Instituto de Biologia Molecular e Celular (IBMC), University of Porto, Porto, Portugal
- Instituto de Investigação e Inovação em Saúde, University of Porto, i3S, Porto, Portugal
| | - Martin Kapun
- The European Drosophila Population Genomics Consortium (DrosEU)
- Department of Evolutionary Biology and Environmental Studies, University of Zürich, Zürich, Switzerland
- Division of Cell & Developmental Biology, Medical University of Vienna, Vienna, Austria
| | - Thomas Flatt
- The European Drosophila Population Genomics Consortium (DrosEU)
- Department of Biology, University of Fribourg, Fribourg CH-1700, Switzerland
| | - Josefa González
- The European Drosophila Population Genomics Consortium (DrosEU)
- Institute of Evolutionary Biology (CSIC-UPF), Barcelona, Spain
| | - Fabian Staubach
- The European Drosophila Population Genomics Consortium (DrosEU)
- Department of Evolution and Ecology, University of Freiburg, Freiburg 79104, Germany
| | - Darren J Obbard
- The European Drosophila Population Genomics Consortium (DrosEU)
- Ashworth Laboratories, Institute of Evolutionary Biology, University of Edinburgh, Charlotte Auerbach Road, Edinburgh EH9 3FL, UK
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Kumar S, Tunc I, Tansey TR, Pirooznia M, Harbison ST. Identification of Genes Contributing to a Long Circadian Period in Drosophila Melanogaster. J Biol Rhythms 2020; 36:239-253. [PMID: 33274675 DOI: 10.1177/0748730420975946] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The endogenous circadian period of animals and humans is typically very close to 24 h. Individuals with much longer circadian periods have been observed, however, and in the case of humans, these deviations have health implications. Previously, we observed a line of Drosophila with a very long average period of 31.3 h for locomotor activity behavior. Preliminary mapping indicated that the long period did not map to known canonical clock genes but instead mapped to multiple chromosomes. Using RNA-Seq, we surveyed the whole transcriptome of fly heads from this line across time and compared it with a wild-type control. A three-way generalized linear model revealed that approximately two-thirds of the genes were expressed differentially among the two genotypes, while only one quarter of the genes varied across time. Using these results, we applied algorithms to search for genes that oscillated over 24 h, identifying genes not previously known to cycle. We identified 166 differentially expressed genes that overlapped with a previous Genome-wide Association Study (GWAS) of circadian behavior, strongly implicating them in the long-period phenotype. We tested mutations in 45 of these genes for their effect on the circadian period. Mutations in Alk, alph, CG10089, CG42540, CG6034, Kairos (CG6123), CG8768, klg, Lar, sick, and tinc had significant effects on the circadian period, with seven of these mutations increasing the circadian period of locomotor activity behavior. Genetic rescue of mutant Kairos restored the circadian period to wild-type levels, suggesting it has a critical role in determining period length in constant darkness.
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Affiliation(s)
- Shailesh Kumar
- Laboratory of Systems Genetics, Systems Biology Center, National Heart, Lung, and Blood Institute, Bethesda, Maryland
| | - Ilker Tunc
- Bioinformatics and Computational Biology Core, National Heart, Lung, and Blood Institute, Bethesda, Maryland
| | - Terry R Tansey
- Laboratory of Systems Genetics, Systems Biology Center, National Heart, Lung, and Blood Institute, Bethesda, Maryland
| | - Mehdi Pirooznia
- Bioinformatics and Computational Biology Core, National Heart, Lung, and Blood Institute, Bethesda, Maryland
| | - Susan T Harbison
- Laboratory of Systems Genetics, Systems Biology Center, National Heart, Lung, and Blood Institute, Bethesda, Maryland
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Systematic comparison and assessment of RNA-seq procedures for gene expression quantitative analysis. Sci Rep 2020; 10:19737. [PMID: 33184454 PMCID: PMC7665074 DOI: 10.1038/s41598-020-76881-x] [Citation(s) in RCA: 93] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 11/03/2020] [Indexed: 01/16/2023] Open
Abstract
RNA-seq is currently considered the most powerful, robust and adaptable technique for measuring gene expression and transcription activation at genome-wide level. As the analysis of RNA-seq data is complex, it has prompted a large amount of research on algorithms and methods. This has resulted in a substantial increase in the number of options available at each step of the analysis. Consequently, there is no clear consensus about the most appropriate algorithms and pipelines that should be used to analyse RNA-seq data. In the present study, 192 pipelines using alternative methods were applied to 18 samples from two human cell lines and the performance of the results was evaluated. Raw gene expression signal was quantified by non-parametric statistics to measure precision and accuracy. Differential gene expression performance was estimated by testing 17 differential expression methods. The procedures were validated by qRT-PCR in the same samples. This study weighs up the advantages and disadvantages of the tested algorithms and pipelines providing a comprehensive guide to the different methods and procedures applied to the analysis of RNA-seq data, both for the quantification of the raw expression signal and for the differential gene expression.
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Duarte-Delgado D, Dadshani S, Schoof H, Oyiga BC, Schneider M, Mathew B, Léon J, Ballvora A. Transcriptome profiling at osmotic and ionic phases of salt stress response in bread wheat uncovers trait-specific candidate genes. BMC PLANT BIOLOGY 2020; 20:428. [PMID: 32938380 PMCID: PMC7493341 DOI: 10.1186/s12870-020-02616-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 08/19/2020] [Indexed: 05/17/2023]
Abstract
BACKGROUND Bread wheat is one of the most important crops for the human diet, but the increasing soil salinization is causing yield reductions worldwide. Improving salt stress tolerance in wheat requires the elucidation of the mechanistic basis of plant response to this abiotic stress factor. Although several studies have been performed to analyze wheat adaptation to salt stress, there are still some gaps to fully understand the molecular mechanisms from initial signal perception to the onset of responsive tolerance pathways. The main objective of this study is to exploit the dynamic salt stress transcriptome in underlying QTL regions to uncover candidate genes controlling salt stress tolerance in bread wheat. The massive analysis of 3'-ends sequencing protocol was used to analyze leave samples at osmotic and ionic phases. Afterward, stress-responsive genes overlapping QTL for salt stress-related traits in two mapping populations were identified. RESULTS Among the over-represented salt-responsive gene categories, the early up-regulation of calcium-binding and cell wall synthesis genes found in the tolerant genotype are presumably strategies to cope with the salt-related osmotic stress. On the other hand, the down-regulation of photosynthesis-related and calcium-binding genes, and the increased oxidative stress response in the susceptible genotype are linked with the greater photosynthesis inhibition at the osmotic phase. The specific up-regulation of some ABC transporters and Na+/Ca2+ exchangers in the tolerant genotype at the ionic stage indicates their involvement in mechanisms of sodium exclusion and homeostasis. Moreover, genes related to protein synthesis and breakdown were identified at both stress phases. Based on the linkage disequilibrium blocks, salt-responsive genes within QTL intervals were identified as potential components operating in pathways leading to salt stress tolerance. Furthermore, this study conferred evidence of novel regions with transcription in bread wheat. CONCLUSION The dynamic transcriptome analysis allowed the comparison of osmotic and ionic phases of the salt stress response and gave insights into key molecular mechanisms involved in the salt stress adaptation of contrasting bread wheat genotypes. The leveraging of the highly contiguous chromosome-level reference genome sequence assembly facilitated the QTL dissection by targeting novel candidate genes for salt tolerance.
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Affiliation(s)
| | - Said Dadshani
- INRES-Plant Breeding, University of Bonn, Bonn, Germany
| | - Heiko Schoof
- INRES-Crop Bioinformatics, University of Bonn, Bonn, Germany
| | | | | | - Boby Mathew
- INRES-Plant Breeding, University of Bonn, Bonn, Germany
| | - Jens Léon
- INRES-Plant Breeding, University of Bonn, Bonn, Germany
| | - Agim Ballvora
- INRES-Plant Breeding, University of Bonn, Bonn, Germany.
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Fanidis D, Moulos P. Integrative, normalization-insusceptible statistical analysis of RNA-Seq data, with improved differential expression and unbiased downstream functional analysis. Brief Bioinform 2020; 22:5890504. [PMID: 32778872 DOI: 10.1093/bib/bbaa156] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 06/04/2020] [Accepted: 06/19/2020] [Indexed: 12/11/2022] Open
Abstract
The study of differential gene expression patterns through RNA-Seq comprises a routine task in the daily lives of molecular bioscientists, who produce vast amounts of data requiring proper management and analysis. Despite widespread use, there are still no widely accepted golden standards for the normalization and statistical analysis of RNA-Seq data, and critical biases, such as gene lengths and problems in the detection of certain types of molecules, remain largely unaddressed. Stimulated by these unmet needs and the lack of in-depth research into the potential of combinatorial methods to enhance the analysis of differential gene expression, we had previously introduced the PANDORA P-value combination algorithm while presenting evidence for PANDORA's superior performance in optimizing the tradeoff between precision and sensitivity. In this article, we present the next generation of the algorithm along with a more in-depth investigation of its capabilities to effectively analyze RNA-Seq data. In particular, we show that PANDORA-reported lists of differentially expressed genes are unaffected by biases introduced by different normalization methods, while, at the same time, they comprise a reliable input option for downstream pathway analysis. Additionally, PANDORA outperforms other methods in detecting differential expression patterns in certain transcript types, including long non-coding RNAs.
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38
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Bushel PR, Ferguson SS, Ramaiahgari SC, Paules RS, Auerbach SS. Comparison of Normalization Methods for Analysis of TempO-Seq Targeted RNA Sequencing Data. Front Genet 2020; 11:594. [PMID: 32655620 PMCID: PMC7325690 DOI: 10.3389/fgene.2020.00594] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 05/15/2020] [Indexed: 11/30/2022] Open
Abstract
Analysis of bulk RNA sequencing (RNA-Seq) data is a valuable tool to understand transcription at the genome scale. Targeted sequencing of RNA has emerged as a practical means of assessing the majority of the transcriptomic space with less reliance on large resources for consumables and bioinformatics. TempO-Seq is a templated, multiplexed RNA-Seq platform that interrogates a panel of sentinel genes representative of genome-wide transcription. Nuances of the technology require proper preprocessing of the data. Various methods have been proposed and compared for normalizing bulk RNA-Seq data, but there has been little to no investigation of how the methods perform on TempO-Seq data. We simulated count data into two groups (treated vs. untreated) at seven-fold change (FC) levels (including no change) using control samples from human HepaRG cells run on TempO-Seq and normalized the data using seven normalization methods. Upper Quartile (UQ) performed the best with regard to maintaining FC levels as detected by a limma contrast between treated vs. untreated groups. For all FC levels, specificity of the UQ normalization was greater than 0.84 and sensitivity greater than 0.90 except for the no change and +1.5 levels. Furthermore, K-means clustering of the simulated genes normalized by UQ agreed the most with the FC assignments [adjusted Rand index (ARI) = 0.67]. Despite having an assumption of the majority of genes being unchanged, the DESeq2 scaling factors normalization method performed reasonably well as did simple normalization procedures counts per million (CPM) and total counts (TCs). These results suggest that for two class comparisons of TempO-Seq data, UQ, CPM, TC, or DESeq2 normalization should provide reasonably reliable results at absolute FC levels ≥2.0. These findings will help guide researchers to normalize TempO-Seq gene expression data for more reliable results.
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Affiliation(s)
- Pierre R Bushel
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences of National Institutes of Health, Durham, NC, United States.,Massive Genome Informatics Group, National Institute of Environmental Health Sciences of National Institutes of Health, Durham, NC, United States.,Biomolecular Screening Branch, National Institute of Environmental Health Sciences of National Institutes of Health, Durham, NC, United States
| | - Stephen S Ferguson
- Biomolecular Screening Branch, National Institute of Environmental Health Sciences of National Institutes of Health, Durham, NC, United States
| | - Sreenivasa C Ramaiahgari
- Biomolecular Screening Branch, National Institute of Environmental Health Sciences of National Institutes of Health, Durham, NC, United States
| | - Richard S Paules
- Biomolecular Screening Branch, National Institute of Environmental Health Sciences of National Institutes of Health, Durham, NC, United States
| | - Scott S Auerbach
- Biomolecular Screening Branch, National Institute of Environmental Health Sciences of National Institutes of Health, Durham, NC, United States
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Shi MW, Zhang NA, Shi CP, Liu CJ, Luo ZH, Wang DY, Guo AY, Chen ZX. SAGD: a comprehensive sex-associated gene database from transcriptomes. Nucleic Acids Res 2020; 47:D835-D840. [PMID: 30380119 PMCID: PMC6323940 DOI: 10.1093/nar/gky1040] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 10/16/2018] [Indexed: 12/19/2022] Open
Abstract
Many animal species present sex differences. Sex-associated genes (SAGs), which have female-biased or male-biased expression, have major influences on the remarkable sex differences in important traits such as growth, reproduction, disease resistance and behaviors. However, the SAGs resulting in the vast majority of phenotypic sex differences are still unknown. To provide a useful resource for the functional study of SAGs, we manually curated public RNA-seq datasets with paired female and male biological replicates from the same condition and systematically re-analyzed the datasets using standardized methods. We identified 27,793 female-biased SAGs and 64,043 male-biased SAGs from 2,828 samples of 21 species, including human, chimpanzee, macaque, mouse, rat, cow, horse, chicken, zebrafish, seven fly species and five worm species. All these data were cataloged into SAGD, a user-friendly database of SAGs (http://bioinfo.life.hust.edu.cn/SAGD) where users can browse SAGs by gene, species, drug and dataset. In SAGD, the expression, annotation, targeting drugs, homologs, ontology and related RNA-seq datasets of SAGs are provided to help researchers to explore their functions and potential applications in agriculture and human health.
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Affiliation(s)
- Meng-Wei Shi
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Na-An Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Chuan-Ping Shi
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Chun-Jie Liu
- Department of Bioinformatics and Systems Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Zhi-Hui Luo
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - Dan-Yang Wang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
| | - An-Yuan Guo
- Department of Bioinformatics and Systems Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China
| | - Zhen-Xia Chen
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, PR China
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Baik B, Yoon S, Nam D. Benchmarking RNA-seq differential expression analysis methods using spike-in and simulation data. PLoS One 2020; 15:e0232271. [PMID: 32353015 PMCID: PMC7192453 DOI: 10.1371/journal.pone.0232271] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 04/10/2020] [Indexed: 12/27/2022] Open
Abstract
Benchmarking RNA-seq differential expression analysis methods using spike-in and simulated RNA-seq data has often yielded inconsistent results. The spike-in data, which were generated from the same bulk RNA sample, only represent technical variability, making the test results less reliable. We compared the performance of 12 differential expression analysis methods for RNA-seq data, including recent variants in widely used software packages, using both RNA spike-in and simulation data for negative binomial (NB) model. Performance of edgeR, DESeq2, and ROTS was particularly different between the two benchmark tests. Then, each method was tested under most extensive simulation conditions especially demonstrating the large impacts of proportion, dispersion, and balance of differentially expressed (DE) genes. DESeq2, a robust version of edgeR (edgeR.rb), voom with TMM normalization (voom.tmm) and sample weights (voom.sw) showed an overall good performance regardless of presence of outliers and proportion of DE genes. The performance of RNA-seq DE gene analysis methods substantially depended on the benchmark used. Based on the simulation results, suitable methods were suggested under various test conditions.
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Affiliation(s)
- Bukyung Baik
- Department of Biological Sciences, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
| | - Sora Yoon
- Department of Biological Sciences, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
| | - Dougu Nam
- Department of Biological Sciences, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
- Department of Mathematical Sciences, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
- * E-mail:
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41
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May JP, Johnson PZ, Ilyas M, Gao F, Simon AE. The Multifunctional Long-Distance Movement Protein of Pea Enation Mosaic Virus 2 Protects Viral and Host Transcripts from Nonsense-Mediated Decay. mBio 2020; 11:e00204-20. [PMID: 32156817 PMCID: PMC7064760 DOI: 10.1128/mbio.00204-20] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 02/03/2020] [Indexed: 02/07/2023] Open
Abstract
The nonsense-mediated decay (NMD) pathway presents a challenge for RNA viruses with termination codons that precede extended 3' untranslated regions (UTRs). The umbravirus Pea enation mosaic virus 2 (PEMV2) is a nonsegmented, positive-sense RNA virus with an unusually long 3' UTR that is susceptible to NMD. To establish a systemic infection, the PEMV2 long-distance movement protein p26 was previously shown to both stabilize viral RNAs and bind them for transport through the plant's vascular system. The current study demonstrated that p26 protects both viral and nonviral messenger RNAs from NMD. Although p26 localizes to both the cytoplasm and nucleolus, p26 exerts its anti-NMD effects exclusively in the cytoplasm independently of long-distance movement. Using a transcriptome-wide approach in the model plant Nicotiana benthamiana, p26 protected a subset of cellular NMD target transcripts, particularly those containing long, structured, GC-rich 3' UTRs. Furthermore, transcriptome sequencing (RNA-seq) revealed that the NMD pathway is highly dysfunctional during PEMV2 infection, with 1,820 (48%) of NMD targets increasing in abundance. Widespread changes in the host transcriptome are common during plant RNA virus infections, and these results suggest that, in at least some instances, virus-mediated NMD inhibition may be a major contributing factor.IMPORTANCE Nonsense-mediated decay (NMD) represents an RNA regulatory pathway that degrades both natural and faulty messenger RNAs with long 3' untranslated regions. NMD targets diverse families of RNA viruses, requiring that viruses counteract the NMD pathway for successful amplification in host cells. A protein required for long-distance movement of Pea enation mosaic virus 2 (PEMV2) is shown to also protect both viral and host mRNAs from NMD. RNA-seq analyses of the Nicotiana benthamiana transcriptome revealed that PEMV2 infection significantly impairs the host NMD pathway. RNA viruses routinely induce large-scale changes in host gene expression, and, like PEMV2, may use NMD inhibition to alter the host transcriptome in an effort to increase virus amplification.
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Affiliation(s)
- Jared P May
- Department of Cell Biology and Molecular Genetics, University of Maryland-College Park, College Park, Maryland, USA
| | - Philip Z Johnson
- Department of Cell Biology and Molecular Genetics, University of Maryland-College Park, College Park, Maryland, USA
| | - Muhammad Ilyas
- Department of Cell Biology and Molecular Genetics, University of Maryland-College Park, College Park, Maryland, USA
| | - Feng Gao
- Department of Cell Biology and Molecular Genetics, University of Maryland-College Park, College Park, Maryland, USA
| | - Anne E Simon
- Department of Cell Biology and Molecular Genetics, University of Maryland-College Park, College Park, Maryland, USA
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Evaluation of Seven Different RNA-Seq Alignment Tools Based on Experimental Data from the Model Plant Arabidopsis thaliana. Int J Mol Sci 2020; 21:ijms21051720. [PMID: 32138290 PMCID: PMC7084517 DOI: 10.3390/ijms21051720] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 02/28/2020] [Accepted: 02/29/2020] [Indexed: 01/15/2023] Open
Abstract
Quantification of gene expression is crucial to connect genome sequences with phenotypic and physiological data. RNA-Sequencing (RNA-Seq) has taken a prominent role in the study of transcriptomic reactions of plants to various environmental and genetic perturbations. However, comparative tests of different tools for RNA-Seq read mapping and quantification have been mainly performed on data from animals or humans, which necessarily neglect, for example, the large genetic variability among natural accessions within plant species. Here, we compared seven computational tools for their ability to map and quantify Illumina single-end reads from the Arabidopsis thaliana accessions Columbia-0 (Col-0) and N14. Between 92.4% and 99.5% of all reads were mapped to the reference genome or transcriptome and the raw count distributions obtained from the different mappers were highly correlated. Using the software DESeq2 to determine differential gene expression (DGE) between plants exposed to 20 °C or 4 °C from these read counts showed a large pairwise overlap between the mappers. Interestingly, when the commercial CLC software was used with its own DGE module instead of DESeq2, strongly diverging results were obtained. All tested mappers provided highly similar results for mapping Illumina reads of two polymorphic Arabidopsis accessions to the reference genome or transcriptome and for the determination of DGE when the same software was used for processing.
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43
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Yang S, Wachtel MS, Wu J. DFseq: Distribution-Free Method to Detect Differential Gene Expression for RNA-Sequencing Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:558-565. [PMID: 30176602 DOI: 10.1109/tcbb.2018.2866994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Many current RNA-sequencing data analysis methods compare expressions one gene at a time, taking little consideration of the correlations among genes. In this study, we propose a method to convert such an one-dimensional comparison approach into a two-dimensional evaluation of the ratio of standard deviations (SD) of two constructed random variables. This method allows the identification of differentially expressed genes while controlling a preset significance level conditional on the read count mean-variance relationship. Meanwhile, correlations among genes are naturally accommodated due to the clustering of genes with similar distribution in the proposed σ-σ plot. The proposed distribution-free method is designated as DFseq, because it does not depend on a parametric distribution to fit read count. As a result, compared with parametric methods, DFseq can effectively handle genes with a bimodal-like distribution and/or genes with excessive 0 read counts, as well as genes with outlying observations. Besides, DFseq is an ideal platform for comparing performance of different differential gene expression detection methods.
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44
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Li X, Cooper NGF, O'Toole TE, Rouchka EC. Choice of library size normalization and statistical methods for differential gene expression analysis in balanced two-group comparisons for RNA-seq studies. BMC Genomics 2020; 21:75. [PMID: 31992223 PMCID: PMC6986029 DOI: 10.1186/s12864-020-6502-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 01/16/2020] [Indexed: 12/20/2022] Open
Abstract
Background High-throughput RNA sequencing (RNA-seq) has evolved as an important analytical tool in molecular biology. Although the utility and importance of this technique have grown, uncertainties regarding the proper analysis of RNA-seq data remain. Of primary concern, there is no consensus regarding which normalization and statistical methods are the most appropriate for analyzing this data. The lack of standardized analytical methods leads to uncertainties in data interpretation and study reproducibility, especially with studies reporting high false discovery rates. In this study, we compared a recently developed normalization method, UQ-pgQ2, with three of the most frequently used alternatives including RLE (relative log estimate), TMM (Trimmed-mean M values) and UQ (upper quartile normalization) in the analysis of RNA-seq data. We evaluated the performance of these methods for gene-level differential expression analysis by considering the factors, including: 1) normalization combined with the choice of a Wald test from DESeq2 and an exact test/QL (Quasi-likelihood) F-Test from edgeR; 2) sample sizes in two balanced two-group comparisons; and 3) sequencing read depths. Results Using the MAQC RNA-seq datasets with small sample replicates, we found that UQ-pgQ2 normalization combined with an exact test can achieve better performance in term of power and specificity in differential gene expression analysis. However, using an intra-group analysis of false positives from real and simulated data, we found that a Wald test performs better than an exact test when the number of sample replicates is large and that a QL F-test performs the best given sample sizes of 5, 10 and 15 for any normalization. The RLE, TMM and UQ methods performed similarly given a desired sample size. Conclusion We found the UQ-pgQ2 method combined with an exact test/QL F-test is the best choice in order to control false positives when the sample size is small. When the sample size is large, UQ-pgQ2 with a QL F-test is a better choice for the type I error control in an intra-group analysis. We observed read depths have a minimal impact for differential gene expression analysis based on the simulated data.
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Affiliation(s)
- Xiaohong Li
- Department of Anatomical Sciences and Neurobiology, University of Louisville, Louisville, KY, USA.
| | - Nigel G F Cooper
- Department of Anatomical Sciences and Neurobiology, University of Louisville, Louisville, KY, USA
| | | | - Eric C Rouchka
- Department of Computer Science and Engineering, University of Louisville, Louisville, KY, USA
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Giraddi RR, Chung CY, Heinz RE, Balcioglu O, Novotny M, Trejo CL, Dravis C, Hagos BM, Mehrabad EM, Rodewald LW, Hwang JY, Fan C, Lasken R, Varley KE, Perou CM, Wahl GM, Spike BT. Single-Cell Transcriptomes Distinguish Stem Cell State Changes and Lineage Specification Programs in Early Mammary Gland Development. Cell Rep 2020; 24:1653-1666.e7. [PMID: 30089273 PMCID: PMC6301014 DOI: 10.1016/j.celrep.2018.07.025] [Citation(s) in RCA: 103] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2017] [Revised: 05/29/2018] [Accepted: 07/06/2018] [Indexed: 01/23/2023] Open
Abstract
The mammary gland consists of cells with gene expression patterns
reflecting their cellular origins, function, and spatiotemporal context.
However, knowledge of developmental kinetics and mechanisms of lineage
specification is lacking. We address this significant knowledge gap by
generating a single-cell transcriptome atlas encompassing embryonic, postnatal,
and adult mouse mammary development. From these data, we map the chronology of
transcriptionally and epigenetically distinct cell states and distinguish fetal
mammary stem cells (fMaSCs) from their precursors and progeny. fMaSCs show
balanced co-expression of factors associated with discrete adult lineages and a
metabolic gene signature that subsides during maturation but reemerges in some
human breast cancers and metastases. These data provide a useful resource for
illuminating mammary cell heterogeneity, the kinetics of differentiation, and
developmental correlates of tumorigenesis. Single-cell RNA sequencing of developing mouse mammary epithelia reveals
the timing of lineage specification. Giraddi et al. find that fetal mammary stem
cells co-express factors that define distinct lineages in their progeny and bear
functionally relevant metabolic program signatures that change with
differentiation and are resurrected in human breast cancers and metastases.
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Affiliation(s)
- Rajshekhar R Giraddi
- Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Chi-Yeh Chung
- Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Richard E Heinz
- Huntsman Cancer Institute, Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Ozlen Balcioglu
- Huntsman Cancer Institute, Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Mark Novotny
- J. Craig Venter Institute, La Jolla, CA 92037, USA
| | - Christy L Trejo
- Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Christopher Dravis
- Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Berhane M Hagos
- Huntsman Cancer Institute, Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Elnaz Mirzaei Mehrabad
- Huntsman Cancer Institute, Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Luo Wei Rodewald
- Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Jae Y Hwang
- Huntsman Cancer Institute, Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Cheng Fan
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Roger Lasken
- J. Craig Venter Institute, La Jolla, CA 92037, USA
| | - Katherine E Varley
- Huntsman Cancer Institute, Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Charles M Perou
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Geoffrey M Wahl
- Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA.
| | - Benjamin T Spike
- Huntsman Cancer Institute, Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA.
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Tran DT, Bhaskara A, Kuberan B, Might M. A graph-based algorithm for RNA-seq data normalization. PLoS One 2020; 15:e0227760. [PMID: 31978105 PMCID: PMC6980396 DOI: 10.1371/journal.pone.0227760] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2019] [Accepted: 12/28/2019] [Indexed: 12/16/2022] Open
Abstract
The use of RNA-sequencing has garnered much attention in recent years for characterizing and understanding various biological systems. However, it remains a major challenge to gain insights from a large number of RNA-seq experiments collectively, due to the normalization problem. Normalization has been challenging due to an inherent circularity, requiring that RNA-seq data be normalized before any pattern of differential (or non-differential) expression can be ascertained; meanwhile, the prior knowledge of non-differential transcripts is crucial to the normalization process. Some methods have successfully overcome this problem by the assumption that most transcripts are not differentially expressed. However, when RNA-seq profiles become more abundant and heterogeneous, this assumption fails to hold, leading to erroneous normalization. We present a normalization procedure that does not rely on this assumption, nor prior knowledge about the reference transcripts. This algorithm is based on a graph constructed from intrinsic correlations among RNA-seq transcripts and seeks to identify a set of densely connected vertices as references. Application of this algorithm on our synthesized validation data showed that it could recover the reference transcripts with high precision, thus resulting in high-quality normalization. On a realistic data set from the ENCODE project, this algorithm gave good results and could finish in a reasonable time. These preliminary results imply that we may be able to break the long persisting circularity problem in RNA-seq normalization.
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Affiliation(s)
- Diem-Trang Tran
- School of Computing, University of Utah, Salt Lake City, Utah, United States of America
- * E-mail:
| | - Aditya Bhaskara
- School of Computing, University of Utah, Salt Lake City, Utah, United States of America
| | - Balagurunathan Kuberan
- Department of Medicinal Chemistry, University of Utah, Salt Lake City, Utah, United States of America
- Department of Biology, University of Utah, Salt Lake City, Utah, United States of America
| | - Matthew Might
- Hugh Kaul Precision Medicine Institute, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
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47
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Wright Muelas M, Mughal F, O'Hagan S, Day PJ, Kell DB. The role and robustness of the Gini coefficient as an unbiased tool for the selection of Gini genes for normalising expression profiling data. Sci Rep 2019; 9:17960. [PMID: 31784565 PMCID: PMC6884504 DOI: 10.1038/s41598-019-54288-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Accepted: 11/08/2019] [Indexed: 12/13/2022] Open
Abstract
We recently introduced the Gini coefficient (GC) for assessing the expression variation of a particular gene in a dataset, as a means of selecting improved reference genes over the cohort ('housekeeping genes') typically used for normalisation in expression profiling studies. Those genes (transcripts) that we determined to be useable as reference genes differed greatly from previous suggestions based on hypothesis-driven approaches. A limitation of this initial study is that a single (albeit large) dataset was employed for both tissues and cell lines. We here extend this analysis to encompass seven other large datasets. Although their absolute values differ a little, the Gini values and median expression levels of the various genes are well correlated with each other between the various cell line datasets, implying that our original choice of the more ubiquitously expressed low-Gini-coefficient genes was indeed sound. In tissues, the Gini values and median expression levels of genes showed a greater variation, with the GC of genes changing with the number and types of tissues in the data sets. In all data sets, regardless of whether this was derived from tissues or cell lines, we also show that the GC is a robust measure of gene expression stability. Using the GC as a measure of expression stability we illustrate its utility to find tissue- and cell line-optimised housekeeping genes without any prior bias, that again include only a small number of previously reported housekeeping genes. We also independently confirmed this experimentally using RT-qPCR with 40 candidate GC genes in a panel of 10 cell lines. These were termed the Gini Genes. In many cases, the variation in the expression levels of classical reference genes is really quite huge (e.g. 44 fold for GAPDH in one data set), suggesting that the cure (of using them as normalising genes) may in some cases be worse than the disease (of not doing so). We recommend the present data-driven approach for the selection of reference genes by using the easy-to-calculate and robust GC.
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Affiliation(s)
- Marina Wright Muelas
- Department of Biochemistry, Institute of Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown Street, Liverpool, L69 7ZB, UK.
| | - Farah Mughal
- Department of Biochemistry, Institute of Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown Street, Liverpool, L69 7ZB, UK
| | - Steve O'Hagan
- School of Chemistry, Department of Chemistry, The Manchester Institute of Biotechnology 131, Princess Street, Manchester, M1 7DN, UK
- The Manchester Institute of Biotechnology, 131, Princess Street, Manchester, M1 7DN, UK
| | - Philip J Day
- The Manchester Institute of Biotechnology, 131, Princess Street, Manchester, M1 7DN, UK.
- Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PL, UK.
| | - Douglas B Kell
- Department of Biochemistry, Institute of Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown Street, Liverpool, L69 7ZB, UK.
- Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, 10 Building 220, Kemitorvet, 2800, Kgs. Lyngby, Denmark.
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Abstract
Genome-wide gene expression studies have become a routine approach due to the advances in sequencing technologies, their ease of use, and increasing affordability. Simultaneous investigation of small RNA expression adds further valuable information but is not adopted as widely yet. Both RNA-seq and small RNA-seq benefit from the use of specific cell types. Here, we describe a protocol for the isolation of male meiotic cells from maize or wheat plants, along with the application of downstream RNA sequencing, extendable to other -omics approaches.
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49
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Quinn TP, Erb I, Richardson MF, Crowley TM. Understanding sequencing data as compositions: an outlook and review. Bioinformatics 2019; 34:2870-2878. [PMID: 29608657 PMCID: PMC6084572 DOI: 10.1093/bioinformatics/bty175] [Citation(s) in RCA: 152] [Impact Index Per Article: 30.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Accepted: 03/26/2018] [Indexed: 12/30/2022] Open
Abstract
Motivation Although seldom acknowledged explicitly, count data generated by sequencing platforms exist as compositions for which the abundance of each component (e.g. gene or transcript) is only coherently interpretable relative to other components within that sample. This property arises from the assay technology itself, whereby the number of counts recorded for each sample is constrained by an arbitrary total sum (i.e. library size). Consequently, sequencing data, as compositional data, exist in a non-Euclidean space that, without normalization or transformation, renders invalid many conventional analyses, including distance measures, correlation coefficients and multivariate statistical models. Results The purpose of this review is to summarize the principles of compositional data analysis (CoDA), provide evidence for why sequencing data are compositional, discuss compositionally valid methods available for analyzing sequencing data, and highlight future directions with regard to this field of study. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Thomas P Quinn
- Bioinformatics Core Research Group, Deakin University, Geelong, Australia
| | - Ionas Erb
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Mark F Richardson
- Bioinformatics Core Research Group, Deakin University, Geelong, Australia.,Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Geelong, Australia
| | - Tamsyn M Crowley
- Bioinformatics Core Research Group, Deakin University, Geelong, Australia.,Poultry Hub Australia, University of New England, Armidale, Australia
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Akter S, Xu D, Nagel SC, Bromfield JJ, Pelch K, Wilshire GB, Joshi T. Machine Learning Classifiers for Endometriosis Using Transcriptomics and Methylomics Data. Front Genet 2019; 10:766. [PMID: 31552087 PMCID: PMC6737999 DOI: 10.3389/fgene.2019.00766] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 07/19/2019] [Indexed: 12/29/2022] Open
Abstract
Endometriosis is a complex and common gynecological disorder yet a poorly understood disease affecting about 176 million women worldwide and causing significant impact on their quality of life and economic burden. Neither a definitive clinical symptom nor a minimally invasive diagnostic method is available, thus leading to an average of 4 to 11 years of diagnostic latency. Discovery of relevant biological patterns from microarray expression or next generation sequencing (NGS) data has been advanced over the last several decades by applying various machine learning tools. We performed machine learning analysis using 38 RNA-seq and 80 enrichment-based DNA methylation (MBD-seq) datasets. We experimented how well various supervised machine learning methods such as decision tree, partial least squares discriminant analysis (PLSDA), support vector machine, and random forest perform in classifying endometriosis from the control samples trained on both transcriptomics and methylomics data. The assessment was done from two different perspectives for improving classification performances: a) implication of three different normalization techniques and b) implication of differential analysis using the generalized linear model (GLM). Several candidate biomarker genes were identified by multiple machine learning experiments including NOTCH3, SNAPC2, B4GALNT1, SMAP2, DDB2, GTF3C5, and PTOV1 from the transcriptomics data analysis and TRPM6, RASSF2, TNIP2, RP3-522J7.6, FGD3, and MFSD14B from the methylomics data analysis. We concluded that an appropriate machine learning diagnostic pipeline for endometriosis should use TMM normalization for transcriptomics data, and quantile or voom normalization for methylomics data, GLM for feature space reduction and classification performance maximization.
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Affiliation(s)
- Sadia Akter
- Informatics Institute, University of Missouri, Columbia, MO, United States
| | - Dong Xu
- Informatics Institute, University of Missouri, Columbia, MO, United States
- Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, United States
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, United States
| | - Susan C. Nagel
- OB/GYN and Women’s Health, University of Missouri School of Medicine, Columbia, MO, United States
| | - John J. Bromfield
- OB/GYN and Women’s Health, University of Missouri School of Medicine, Columbia, MO, United States
| | - Katherine Pelch
- OB/GYN and Women’s Health, University of Missouri School of Medicine, Columbia, MO, United States
| | | | - Trupti Joshi
- Informatics Institute, University of Missouri, Columbia, MO, United States
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, United States
- Health Management and Informatics, University of Missouri, Columbia, MO, United States
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