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Giordano I, Pasolli E, Mauriello G. Transcriptomic analysis reveals differential gene expression patterns of Lacticaseibacillus casei ATCC 393 in response to ultrasound stress. ULTRASONICS SONOCHEMISTRY 2024; 107:106939. [PMID: 38843696 PMCID: PMC11214525 DOI: 10.1016/j.ultsonch.2024.106939] [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: 04/08/2024] [Revised: 05/14/2024] [Accepted: 05/30/2024] [Indexed: 06/19/2024]
Abstract
In recent years, there has been a growing interest in modulating the performance of probiotic, mainly Lactic Acid Bacteria (LAB), in the field of probiotic food. Attenuation, induced by sub-lethal stresses, delays the probiotic metabolism, and induces a metabolic shift as survival strategy. In this paper, RNA sequencing was used to uncover the transcriptional regulation in Lacticaseibacillus casei ATCC 393 after ultrasound-induced attenuation. Six (T) and 8 (ST) min of sonication induced a significant differential expression of 742 and 409 genes, respectively. We identified 198 up-regulated and 321 down-regulated genes in T, and similarly 321 up-regulated and 249 down-regulated in ST. These results revealed a strong defensive response at 6 min, followed by adaptation at 8 min. Ultrasound attenuation modified the expression of genes related to a series of crucial biomolecular processes including membrane transport, carbohydrate and purine metabolism, phage-related genes, and translation. Specifically, genes encoding PTS transporters and genes involved in the glycolytic pathway and pyruvate metabolism were up-regulated, indicating an increased need for energy supply, as also suggested by an increase in the transcription of purine biosynthetic genes. Instead, protein translation, a high-energy process, was inhibited with the down-regulation of ribosomal protein biosynthetic genes. Moreover, phage-related genes were down-regulated suggesting a tight transcriptional control on DNA structure. The observed phenomena highlight the cell need of ATP to cope with the multiple ultrasound stresses and the activation of processes to stabilize and preserve the DNA structure. Our work demonstrates that ultrasound has remarkable effects on the tested strain and elucidates the involvement of different pathways in its defensive stress-response and in the modification of its phenotype.
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Affiliation(s)
- Irene Giordano
- Department of Agricultural Sciences, University of Naples Federico II, 80049 Naples, Italy
| | - Edoardo Pasolli
- Department of Agricultural Sciences, University of Naples Federico II, 80049 Naples, Italy
| | - Gianluigi Mauriello
- Department of Agricultural Sciences, University of Naples Federico II, 80049 Naples, Italy.
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2
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Jackson DJ, Cerveau N, Posnien N. De novo assembly of transcriptomes and differential gene expression analysis using short-read data from emerging model organisms - a brief guide. Front Zool 2024; 21:17. [PMID: 38902827 PMCID: PMC11188175 DOI: 10.1186/s12983-024-00538-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 06/12/2024] [Indexed: 06/22/2024] Open
Abstract
Many questions in biology benefit greatly from the use of a variety of model systems. High-throughput sequencing methods have been a triumph in the democratization of diverse model systems. They allow for the economical sequencing of an entire genome or transcriptome of interest, and with technical variations can even provide insight into genome organization and the expression and regulation of genes. The analysis and biological interpretation of such large datasets can present significant challenges that depend on the 'scientific status' of the model system. While high-quality genome and transcriptome references are readily available for well-established model systems, the establishment of such references for an emerging model system often requires extensive resources such as finances, expertise and computation capabilities. The de novo assembly of a transcriptome represents an excellent entry point for genetic and molecular studies in emerging model systems as it can efficiently assess gene content while also serving as a reference for differential gene expression studies. However, the process of de novo transcriptome assembly is non-trivial, and as a rule must be empirically optimized for every dataset. For the researcher working with an emerging model system, and with little to no experience with assembling and quantifying short-read data from the Illumina platform, these processes can be daunting. In this guide we outline the major challenges faced when establishing a reference transcriptome de novo and we provide advice on how to approach such an endeavor. We describe the major experimental and bioinformatic steps, provide some broad recommendations and cautions for the newcomer to de novo transcriptome assembly and differential gene expression analyses. Moreover, we provide an initial selection of tools that can assist in the journey from raw short-read data to assembled transcriptome and lists of differentially expressed genes.
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Affiliation(s)
- Daniel J Jackson
- University of Göttingen, Department of Geobiology, Goldschmidtstr.3, Göttingen, 37077, Germany.
| | - Nicolas Cerveau
- University of Göttingen, Department of Geobiology, Goldschmidtstr.3, Göttingen, 37077, Germany
| | - Nico Posnien
- University of Göttingen, Department of Developmental Biology, GZMB, Justus-Von-Liebig-Weg 11, Göttingen, 37077, Germany.
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3
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Jose AM. Heritable epigenetic changes are constrained by the dynamics of regulatory architectures. eLife 2024; 12:RP92093. [PMID: 38717010 PMCID: PMC11078544 DOI: 10.7554/elife.92093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2024] Open
Abstract
Interacting molecules create regulatory architectures that can persist despite turnover of molecules. Although epigenetic changes occur within the context of such architectures, there is limited understanding of how they can influence the heritability of changes. Here, I develop criteria for the heritability of regulatory architectures and use quantitative simulations of interacting regulators parsed as entities, their sensors, and the sensed properties to analyze how architectures influence heritable epigenetic changes. Information contained in regulatory architectures grows rapidly with the number of interacting molecules and its transmission requires positive feedback loops. While these architectures can recover after many epigenetic perturbations, some resulting changes can become permanently heritable. Architectures that are otherwise unstable can become heritable through periodic interactions with external regulators, which suggests that mortal somatic lineages with cells that reproducibly interact with the immortal germ lineage could make a wider variety of architectures heritable. Differential inhibition of the positive feedback loops that transmit regulatory architectures across generations can explain the gene-specific differences in heritable RNA silencing observed in the nematode Caenorhabditis elegans. More broadly, these results provide a foundation for analyzing the inheritance of epigenetic changes within the context of the regulatory architectures implemented using diverse molecules in different living systems.
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4
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Lautert-Dutra W, M Melo C, Chaves LP, Crozier C, P Saggioro F, B Dos Reis R, Bayani J, Bonatto SL, Squire JA. Loss of heterozygosity impacts MHC expression on the immune microenvironment in CDK12-mutated prostate cancer. Mol Cytogenet 2024; 17:11. [PMID: 38704603 PMCID: PMC11070094 DOI: 10.1186/s13039-024-00680-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 04/24/2024] [Indexed: 05/06/2024] Open
Abstract
BACKGROUND In prostate cancer (PCa), well-established biomarkers such as MSI status, TMB high, and PDL1 expression serve as reliable indicators for favorable responses to immunotherapy. Recent studies have suggested a potential association between CDK12 mutations and immunotherapy response; however, the precise mechanisms through which CDK12 mutation may influence immune response remain unclear. A plausible explanation for immune evasion in this subset of CDK12-mutated PCa may be reduced MHC expression. RESULTS Using genomic data of CDK12-mutated PCa from 48 primary and 10 metastatic public domain samples and a retrospective cohort of 53 low-intermediate risk primary PCa, we investigated how variation in the expression of the MHC genes affected associated downstream pathways. We classified the patients based on gene expression quartiles of MHC-related genes and categorized the tumors into "High" and "Low" expression levels. CDK12-mutated tumors with higher MHC-expressed pathways were associated with the immune system and elevated PD-L1, IDO1, and TIM3 expression. Consistent with an inflamed tumor microenvironment (TME) phenotype, digital cytometric analyses identified increased CD8 + T cells, B cells, γδ T cells, and M1 Macrophages in this group. In contrast, CDK12-mutated tumors with lower MHC expression exhibited features consistent with an immune cold TME phenotype and immunoediting. Significantly, low MHC expression was also associated with chromosome 6 loss of heterozygosity (LOH) affecting the entire HLA gene cluster. These LOH events were observed in both major clonal and minor subclonal populations of tumor cells. In our retrospective study of 53 primary PCa cases from this Institute, we found a 4% (2/53) prevalence of CDK12 mutations, with the confirmation of this defect in one tumor through Sanger sequencing. In keeping with our analysis of public domain data this tumor exhibited low MHC expression at the RNA level. More extensive studies will be required to determine whether reduced HLA expression is generally associated with primary tumors or is a specific feature of CDK12 mutated PCa. CONCLUSIONS These data show that analysis of CDK12 alteration, in the context of MHC expression levels, and LOH status may offer improved predictive value for outcomes in this potentially actionable genomic subgroup of PCa. In addition, these findings highlight the need to explore novel therapeutic strategies to enhance MHC expression in CDK12-defective PCa to improve immunotherapy responses.
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Affiliation(s)
- William Lautert-Dutra
- Department of Genetics, Medical School of Ribeirao Preto, University of Sao Paulo - USP, Ribeirão Prêto, SP, 14048-900, Brazil
| | - Camila M Melo
- Department of Genetics, Medical School of Ribeirao Preto, University of Sao Paulo - USP, Ribeirão Prêto, SP, 14048-900, Brazil
| | - Luiz P Chaves
- Department of Genetics, Medical School of Ribeirao Preto, University of Sao Paulo - USP, Ribeirão Prêto, SP, 14048-900, Brazil
| | - Cheryl Crozier
- Diagnostic Development, Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Fabiano P Saggioro
- Department of Pathology, Ribeirao Preto Medical School, University of Sao Paulo - USP, Ribeirão Prêto, Brazil
| | - Rodolfo B Dos Reis
- Department of Pathology, Ribeirao Preto Medical School, University of Sao Paulo - USP, Ribeirão Prêto, Brazil
- Division of Urology, Department of Surgery and Anatomy, Medical School of Ribeirao Preto, University of Sao Paulo - USP, Ribeirão Prêto, Brazil
| | - Jane Bayani
- Diagnostic Development, Ontario Institute for Cancer Research, Toronto, ON, Canada
- Laboratory Medicine and Pathology, University of Toronto, Toronto, ON, Canada
| | - Sandro L Bonatto
- School of Health and Life Sciences, Pontifical Catholic University of Rio Grande Do Sul - PUCRS, Av. Ipiranga, 668, Porto Alegre, RS, 90619-900, Brazil
| | - Jeremy A Squire
- Department of Genetics, Medical School of Ribeirao Preto, University of Sao Paulo - USP, Ribeirão Prêto, SP, 14048-900, Brazil.
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON, K7L3N6, Canada.
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5
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Chen Y, Gustafsson J, Yang J, Nielsen J, Kerkhoven EJ. Single-cell omics analysis with genome-scale metabolic modeling. Curr Opin Biotechnol 2024; 86:103078. [PMID: 38359604 DOI: 10.1016/j.copbio.2024.103078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 01/19/2024] [Indexed: 02/17/2024]
Abstract
Single-cell technologies have been widely used in biological studies and generated a plethora of single-cell data to be interpreted. Due to the inclusion of the priori metabolic network knowledge as well as gene-protein-reaction associations, genome-scale metabolic models (GEMs) have been a powerful tool to integrate and thereby interpret various omics data mostly from bulk samples. Here, we first review two common ways to leverage bulk omics data with GEMs and then discuss advances on integrative analysis of single-cell omics data with GEMs. We end by presenting our views on current challenges and perspectives in this field.
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Affiliation(s)
- Yu Chen
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
| | - Johan Gustafsson
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, SE-405 30 Gothenburg, Sweden; Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Jingyu Yang
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Jens Nielsen
- Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden; BioInnovation Institute, DK-2200 Copenhagen, Denmark
| | - Eduard J Kerkhoven
- Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technology University of Denmark, DK-2800 Kgs. Lyngby, Denmark; SciLifeLab, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.
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6
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Jones EF, Haldar A, Oza VH, Lasseigne BN. Quantifying transcriptome diversity: a review. Brief Funct Genomics 2024; 23:83-94. [PMID: 37225889 DOI: 10.1093/bfgp/elad019] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 04/14/2023] [Accepted: 05/05/2023] [Indexed: 05/26/2023] Open
Abstract
Following the central dogma of molecular biology, gene expression heterogeneity can aid in predicting and explaining the wide variety of protein products, functions and, ultimately, heterogeneity in phenotypes. There is currently overlapping terminology used to describe the types of diversity in gene expression profiles, and overlooking these nuances can misrepresent important biological information. Here, we describe transcriptome diversity as a measure of the heterogeneity in (1) the expression of all genes within a sample or a single gene across samples in a population (gene-level diversity) or (2) the isoform-specific expression of a given gene (isoform-level diversity). We first overview modulators and quantification of transcriptome diversity at the gene level. Then, we discuss the role alternative splicing plays in driving transcript isoform-level diversity and how it can be quantified. Additionally, we overview computational resources for calculating gene-level and isoform-level diversity for high-throughput sequencing data. Finally, we discuss future applications of transcriptome diversity. This review provides a comprehensive overview of how gene expression diversity arises, and how measuring it determines a more complete picture of heterogeneity across proteins, cells, tissues, organisms and species.
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Affiliation(s)
- Emma F Jones
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Anisha Haldar
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Vishal H Oza
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Brittany N Lasseigne
- The Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
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7
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Xue H, Gallopin M, Marchet C, Nguyen HN, Wang Y, Lainé A, Bessiere C, Gautheret D. KaMRaT: a C++ toolkit for k-mer count matrix dimension reduction. Bioinformatics 2024; 40:btae090. [PMID: 38444086 PMCID: PMC10942800 DOI: 10.1093/bioinformatics/btae090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 12/26/2023] [Accepted: 03/04/2024] [Indexed: 03/07/2024] Open
Abstract
MOTIVATION KaMRaT is designed for processing large k-mer count tables derived from multi-sample, RNA-seq data. Its primary objective is to identify condition-specific or differentially expressed sequences, regardless of gene or transcript annotation. RESULTS KaMRaT is implemented in C++. Major functions include scoring k-mers based on count statistics, merging overlapping k-mers into contigs and selecting k-mers based on their occurrence across specific samples. AVAILABILITY AND IMPLEMENTATION Source code and documentation are available via https://github.com/Transipedia/KaMRaT.
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Affiliation(s)
- Haoliang Xue
- I2BC, Université Paris-Saclay, CNRS, CEA, 91190 Gif-sur-Yvette, France
| | - Mélina Gallopin
- I2BC, Université Paris-Saclay, CNRS, CEA, 91190 Gif-sur-Yvette, France
| | - Camille Marchet
- Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, F-59000 Lille, France
| | - Ha N Nguyen
- I2BC, Université Paris-Saclay, CNRS, CEA, 91190 Gif-sur-Yvette, France
| | - Yunfeng Wang
- I2BC, Université Paris-Saclay, CNRS, CEA, 91190 Gif-sur-Yvette, France
| | - Antoine Lainé
- I2BC, Université Paris-Saclay, CNRS, CEA, 91190 Gif-sur-Yvette, France
| | - Chloé Bessiere
- IRMB, University of Montpellier, 34295 Montpellier, France
| | - Daniel Gautheret
- I2BC, Université Paris-Saclay, CNRS, CEA, 91190 Gif-sur-Yvette, France
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8
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Coussement L, Van Criekinge W, De Meyer T. Quantitative transcriptomic and epigenomic data analysis: a primer. BIOINFORMATICS ADVANCES 2024; 4:vbae019. [PMID: 38586118 PMCID: PMC10997052 DOI: 10.1093/bioadv/vbae019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 02/01/2024] [Accepted: 02/09/2024] [Indexed: 04/09/2024]
Abstract
The advent of microarray and second generation sequencing technology has revolutionized the field of molecular biology, allowing researchers to quantitatively assess transcriptomic and epigenomic features in a comprehensive and cost-efficient manner. Moreover, technical advancements have pushed the resolution of these sequencing techniques to the single cell level. As a result, the bottleneck of molecular biology research has shifted from the bench to the subsequent omics data analysis. Even though most methodologies share the same general strategy, state-of-the-art literature typically focuses on data type specific approaches and already assumes expert knowledge. Here, however, we aim at providing conceptual insight in the principles of genome-wide quantitative transcriptomic and epigenomic (including open chromatin assay) data analysis by describing a generic workflow. By starting from a general framework and its assumptions, the need for alternative or additional data-analytical solutions when working with specific data types becomes clear, and are hence introduced. Thus, we aim to enable readers with basic omics expertise to deepen their conceptual and statistical understanding of general strategies and pitfalls in omics data analysis and to facilitate subsequent progression to more specialized literature.
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Affiliation(s)
- Louis Coussement
- Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, 9000, Belgium
| | - Wim Van Criekinge
- Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, 9000, Belgium
| | - Tim De Meyer
- Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, 9000, Belgium
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9
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Baldoni PL, Chen Y, Hediyeh-zadeh S, Liao Y, Dong X, Ritchie ME, Shi W, Smyth GK. Dividing out quantification uncertainty allows efficient assessment of differential transcript expression with edgeR. Nucleic Acids Res 2024; 52:e13. [PMID: 38059347 PMCID: PMC10853777 DOI: 10.1093/nar/gkad1167] [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: 04/01/2023] [Revised: 11/12/2023] [Accepted: 11/21/2023] [Indexed: 12/08/2023] Open
Abstract
Differential expression analysis of RNA-seq is one of the most commonly performed bioinformatics analyses. Transcript-level quantifications are inherently more uncertain than gene-level read counts because of ambiguous assignment of sequence reads to transcripts. While sequence reads can usually be assigned unambiguously to a gene, reads are very often compatible with multiple transcripts for that gene, particularly for genes with many isoforms. Software tools designed for gene-level differential expression do not perform optimally on transcript counts because the read-to-transcript ambiguity (RTA) disrupts the mean-variance relationship normally observed for gene level RNA-seq data and interferes with the efficiency of the empirical Bayes dispersion estimation procedures. The pseudoaligners kallisto and Salmon provide bootstrap samples from which quantification uncertainty can be assessed. We show that the overdispersion arising from RTA can be elegantly estimated by fitting a quasi-Poisson model to the bootstrap counts for each transcript. The technical overdispersion arising from RTA can then be divided out of the transcript counts, leading to scaled counts that can be input for analysis by established gene-level software tools with full statistical efficiency. Comprehensive simulations and test data show that an edgeR analysis of the scaled counts is more powerful and efficient than previous differential transcript expression pipelines while providing correct control of the false discovery rate. Simulations explore a wide range of scenarios including the effects of paired vs single-end reads, different read lengths and different numbers of replicates.
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Affiliation(s)
- Pedro L Baldoni
- Bioinformatics Division, WEHI, Parkville, VIC 3052, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Yunshun Chen
- Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Australia
- ACRF Cancer Biology and Stem Cells Division, WEHI, Parkville, VIC 3052, Australia
| | - Soroor Hediyeh-zadeh
- Bioinformatics Division, WEHI, Parkville, VIC 3052, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Yang Liao
- Olivia Newton-John Cancer Research Institute, Heidelberg, VIC 3084, Australia
- School of Cancer Medicine, La Trobe University, Melbourne, VIC 3086, Australia
| | - Xueyi Dong
- Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Australia
- ACRF Cancer Biology and Stem Cells Division, WEHI, Parkville, VIC 3052, Australia
| | - Matthew E Ritchie
- Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Australia
- Epigenetics and Development Division, WEHI, Parkville, VIC 3052, Australia
| | - Wei Shi
- Olivia Newton-John Cancer Research Institute, Heidelberg, VIC 3084, Australia
- School of Cancer Medicine, La Trobe University, Melbourne, VIC 3086, Australia
| | - Gordon K Smyth
- Bioinformatics Division, WEHI, Parkville, VIC 3052, Australia
- School of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia
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Church SH, Mah JL, Wagner G, Dunn CW. Normalizing need not be the norm: count-based math for analyzing single-cell data. Theory Biosci 2024; 143:45-62. [PMID: 37947999 DOI: 10.1007/s12064-023-00408-x] [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: 06/05/2023] [Accepted: 10/13/2023] [Indexed: 11/12/2023]
Abstract
Counting transcripts of mRNA are a key method of observation in modern biology. With advances in counting transcripts in single cells (single-cell RNA sequencing or scRNA-seq), these data are routinely used to identify cells by their transcriptional profile, and to identify genes with differential cellular expression. Because the total number of transcripts counted per cell can vary for technical reasons, the first step of many commonly used scRNA-seq workflows is to normalize by sequencing depth, transforming counts into proportional abundances. The primary objective of this step is to reshape the data such that cells with similar biological proportions of transcripts end up with similar transformed measurements. But there is growing concern that normalization and other transformations result in unintended distortions that hinder both analyses and the interpretation of results. This has led to an intense focus on optimizing methods for normalization and transformation of scRNA-seq data. Here, we take an alternative approach, by avoiding normalization and transformation altogether. We abandon the use of distances to compare cells, and instead use a restricted algebra, motivated by measurement theory and abstract algebra, that preserves the count nature of the data. We demonstrate that this restricted algebra is sufficient to draw meaningful and practical comparisons of gene expression through the use of the dot product and other elementary operations. This approach sidesteps many of the problems with common transformations, and has the added benefit of being simpler and more intuitive. We implement our approach in the package countland, available in python and R.
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Affiliation(s)
- Samuel H Church
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA.
| | - Jasmine L Mah
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA
| | - Günter Wagner
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA
- Yale Systems Biology Institute, Yale University, New Haven, CT, USA
- Department of Obstetrics, Gynecology and Reproductive Sciences, Yale Medical School, New Haven, CT, USA
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI, USA
| | - Casey W Dunn
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA
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11
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Li Q, Hao M, Zhu J, Yi L, Cheng W, Xie Y, Zhao S. Comparison of differentially expressed genes in longissimus dorsi muscle of Diannan small ears, Wujin and landrace pigs using RNA-seq. Front Vet Sci 2024; 10:1296208. [PMID: 38249550 PMCID: PMC10796741 DOI: 10.3389/fvets.2023.1296208] [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: 09/18/2023] [Accepted: 12/05/2023] [Indexed: 01/23/2024] Open
Abstract
Introduction Pig growth is an important economic trait that involves the co-regulation of multiple genes and related signaling pathways. High-throughput sequencing has become a powerful technology for establishing the transcriptome profiles and can be used to screen genome-wide differentially expressed genes (DEGs). In order to elucidate the molecular mechanism underlying muscle growth, this study adopted RNA sequencing (RNA-seq) to identify and compare DEGs at the genetic level in the longissimus dorsi muscle (LDM) between two indigenous Chinese pig breeds (Diannan small ears [DSE] pig and Wujin pig [WJ]) and one introduced pig breed (Landrace pig [LP]). Methods Animals under study were from two Chinese indigenous pig breeds (DSE pig, n = 3; WJ pig, n = 3) and one introduced pig breed (LP, n = 3) were used for RNA sequencing (RNA-seq) to identify and compare the expression levels of DEGs in the LDM. Then, functional annotation, Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and Protein-Protein Interaction (PPI) network analysis were performed on these DEGs. Then, functional annotation, Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and Protein-Protein Interaction (PPI) network analysis were performed on these DEGs. Results The results revealed that for the DSE, WJ, and LP libraries, more than 66, 65, and 71 million clean reads were generated by transcriptome sequencing, respectively. A total of 11,213 genes were identified in the LDM tissue of these pig breeds, of which 7,127 were co-expressed in the muscle tissue of the three samples. In total, 441 and 339 DEGs were identified between DSE vs. WJ and LP vs. DSE in the study, with 254, 193 up-regulated genes and 187, 193 down-regulated genes in DSE compared to WJ and LP. GO analysis and KEGG signaling pathway analysis showed that DEGs are significantly related to contractile fiber, sarcolemma, and dystrophin-associated glycoprotein complex, myofibril, sarcolemma, and myosin II complex, Glycolysis/Gluconeogenesis, Propanoate metabolism, and Pyruvate metabolism, etc. In combination with functional annotation of DEGs, key genes such as ENO3 and JUN were identified by PPI network analysis. Discussion In conclusion, the present study revealed key genes including DES, FLNC, PSMD1, PSMD6, PSME4, PSMB4, RPL11, RPL13A, ROS23, RPS29, MYH1, MYL9, MYL12B, TPM1, TPM4, ENO3, PGK1, PKM2, GPI, and the unannotated new gene ENSSSCG00000020769 and related signaling pathways that influence the difference in muscle growth and could provide a theoretical basis for improving pig muscle growth traits in the future.
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Affiliation(s)
- Qiuyan Li
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, China
| | - Meilin Hao
- College of Biology and Agriculture, Zunyi Normal University, Zunyi, China
| | - Junhong Zhu
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, China
| | - Lanlan Yi
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, China
| | - Wenjie Cheng
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, China
| | - Yuxiao Xie
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, China
- College of Biology and Agriculture, Zunyi Normal University, Zunyi, China
| | - Sumei Zhao
- Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, China
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Jose AM. Heritable epigenetic changes are constrained by the dynamics of regulatory architectures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.07.544138. [PMID: 37333369 PMCID: PMC10274868 DOI: 10.1101/2023.06.07.544138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Interacting molecules create regulatory architectures that can persist despite turnover of molecules. Although epigenetic changes occur within the context of such architectures, there is limited understanding of how they can influence the heritability of changes. Here I develop criteria for the heritability of regulatory architectures and use quantitative simulations of interacting regulators parsed as entities, their sensors and the sensed properties to analyze how architectures influence heritable epigenetic changes. Information contained in regulatory architectures grows rapidly with the number of interacting molecules and its transmission requires positive feedback loops. While these architectures can recover after many epigenetic perturbations, some resulting changes can become permanently heritable. Such stable changes can (1) alter steady-state levels while preserving the architecture, (2) induce different architectures that persist for many generations, or (3) collapse the entire architecture. Architectures that are otherwise unstable can become heritable through periodic interactions with external regulators, which suggests that the evolution of mortal somatic lineages with cells that reproducibly interact with the immortal germ lineage could make a wider variety of regulatory architectures heritable. Differential inhibition of the positive feedback loops that transmit regulatory architectures across generations can explain the gene-specific differences in heritable RNA silencing observed in the nematode C. elegans, which range from permanent silencing to recovery from silencing within a few generations and subsequent resistance to silencing. More broadly, these results provide a foundation for analyzing the inheritance of epigenetic changes within the context of the regulatory architectures implemented using diverse molecules in different living systems.
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Liehrmann A, Delannoy E, Launay-Avon A, Gilbault E, Loudet O, Castandet B, Rigaill G. DiffSegR: an RNA-seq data driven method for differential expression analysis using changepoint detection. NAR Genom Bioinform 2023; 5:lqad098. [PMID: 37954572 PMCID: PMC10632193 DOI: 10.1093/nargab/lqad098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/27/2023] [Accepted: 10/23/2023] [Indexed: 11/14/2023] Open
Abstract
To fully understand gene regulation, it is necessary to have a thorough understanding of both the transcriptome and the enzymatic and RNA-binding activities that shape it. While many RNA-Seq-based tools have been developed to analyze the transcriptome, most only consider the abundance of sequencing reads along annotated patterns (such as genes). These annotations are typically incomplete, leading to errors in the differential expression analysis. To address this issue, we present DiffSegR - an R package that enables the discovery of transcriptome-wide expression differences between two biological conditions using RNA-Seq data. DiffSegR does not require prior annotation and uses a multiple changepoints detection algorithm to identify the boundaries of differentially expressed regions in the per-base log2 fold change. In a few minutes of computation, DiffSegR could rightfully predict the role of chloroplast ribonuclease Mini-III in rRNA maturation and chloroplast ribonuclease PNPase in (3'/5')-degradation of rRNA, mRNA and tRNA precursors as well as intron accumulation. We believe DiffSegR will benefit biologists working on transcriptomics as it allows access to information from a layer of the transcriptome overlooked by the classical differential expression analysis pipelines widely used today. DiffSegR is available at https://aliehrmann.github.io/DiffSegR/index.html.
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Affiliation(s)
- Arnaud Liehrmann
- Institute of Plant Sciences Paris-Saclay (IPS2), Université Paris-Saclay, CNRS, INRAE, Université Evry, Gif sur Yvette, 91190, France
- Institute of Plant Sciences Paris-Saclay (IPS2), Université Paris Cité, CNRS, INRAE, Gif sur Yvette, 91190, France
- Laboratoire de Mathématiques et de Modélisation d’Evry (LaMME), Université d’Evry-Val-d’Essonne, UMR CNRS 8071, ENSIIE, USC INRAE, Evry,91037, France
| | - Etienne Delannoy
- Institute of Plant Sciences Paris-Saclay (IPS2), Université Paris-Saclay, CNRS, INRAE, Université Evry, Gif sur Yvette, 91190, France
- Institute of Plant Sciences Paris-Saclay (IPS2), Université Paris Cité, CNRS, INRAE, Gif sur Yvette, 91190, France
| | - Alexandra Launay-Avon
- Institute of Plant Sciences Paris-Saclay (IPS2), Université Paris-Saclay, CNRS, INRAE, Université Evry, Gif sur Yvette, 91190, France
- Institute of Plant Sciences Paris-Saclay (IPS2), Université Paris Cité, CNRS, INRAE, Gif sur Yvette, 91190, France
| | - Elodie Gilbault
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin (IJPB), 78000, Versailles, France
| | - Olivier Loudet
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin (IJPB), 78000, Versailles, France
| | - Benoît Castandet
- Institute of Plant Sciences Paris-Saclay (IPS2), Université Paris-Saclay, CNRS, INRAE, Université Evry, Gif sur Yvette, 91190, France
- Institute of Plant Sciences Paris-Saclay (IPS2), Université Paris Cité, CNRS, INRAE, Gif sur Yvette, 91190, France
| | - Guillem Rigaill
- Institute of Plant Sciences Paris-Saclay (IPS2), Université Paris-Saclay, CNRS, INRAE, Université Evry, Gif sur Yvette, 91190, France
- Institute of Plant Sciences Paris-Saclay (IPS2), Université Paris Cité, CNRS, INRAE, Gif sur Yvette, 91190, France
- Laboratoire de Mathématiques et de Modélisation d’Evry (LaMME), Université d’Evry-Val-d’Essonne, UMR CNRS 8071, ENSIIE, USC INRAE, Evry,91037, France
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14
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Kertz NC, Banerjee P, Dyce PW, Diniz WJS. Harnessing Genomics and Transcriptomics Approaches to Improve Female Fertility in Beef Cattle-A Review. Animals (Basel) 2023; 13:3284. [PMID: 37894009 PMCID: PMC10603720 DOI: 10.3390/ani13203284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/13/2023] [Accepted: 10/19/2023] [Indexed: 10/29/2023] Open
Abstract
Female fertility is the foundation of the cow-calf industry, impacting both efficiency and profitability. Reproductive failure is the primary reason why beef cows are sold in the U.S. and the cause of an estimated annual gross loss of USD 2.8 billion. In this review, we discuss the status of the genomics, transcriptomics, and systems genomics approaches currently applied to female fertility and the tools available to cow-calf producers to maximize genetic progress. We highlight the opportunities and limitations associated with using genomic and transcriptomic approaches to discover genes and regulatory mechanisms related to beef fertility. Considering the complex nature of fertility, significant advances in precision breeding will rely on holistic, multidisciplinary approaches to further advance our ability to understand, predict, and improve reproductive performance. While these technologies have advanced our knowledge, the next step is to translate research findings from bench to on-farm applications.
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15
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Li H, Khang TF. clrDV: a differential variability test for RNA-Seq data based on the skew-normal distribution. PeerJ 2023; 11:e16126. [PMID: 37790621 PMCID: PMC10544356 DOI: 10.7717/peerj.16126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 08/27/2023] [Indexed: 10/05/2023] Open
Abstract
Background Pathological conditions may result in certain genes having expression variance that differs markedly from that of the control. Finding such genes from gene expression data can provide invaluable candidates for therapeutic intervention. Under the dominant paradigm for modeling RNA-Seq gene counts using the negative binomial model, tests of differential variability are challenging to develop, owing to dependence of the variance on the mean. Methods Here, we describe clrDV, a statistical method for detecting genes that show differential variability between two populations. We present the skew-normal distribution for modeling gene-wise null distribution of centered log-ratio transformation of compositional RNA-seq data. Results Simulation results show that clrDV has false discovery rate and probability of Type II error that are on par with or superior to existing methodologies. In addition, its run time is faster than its closest competitors, and remains relatively constant for increasing sample size per group. Analysis of a large neurodegenerative disease RNA-Seq dataset using clrDV successfully recovers multiple gene candidates that have been reported to be associated with Alzheimer's disease.
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Affiliation(s)
- Hongxiang Li
- Institute of Mathematical Sciences, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Tsung Fei Khang
- Institute of Mathematical Sciences, Universiti Malaya, Kuala Lumpur, Malaysia
- Universiti Malaya Centre for Data Analytics, Universiti Malaya, Kuala Lumpur, Malaysia
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16
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Swapna LS, Huang M, Li Y. GTM-decon: guided-topic modeling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomes. Genome Biol 2023; 24:190. [PMID: 37596691 PMCID: PMC10436670 DOI: 10.1186/s13059-023-03034-4] [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: 12/22/2022] [Accepted: 08/09/2023] [Indexed: 08/20/2023] Open
Abstract
Cell-type composition is an important indicator of health. We present Guided Topic Model for deconvolution (GTM-decon) to automatically infer cell-type-specific gene topic distributions from single-cell RNA-seq data for deconvolving bulk transcriptomes. GTM-decon performs competitively on deconvolving simulated and real bulk data compared with the state-of-the-art methods. Moreover, as demonstrated in deconvolving disease transcriptomes, GTM-decon can infer multiple cell-type-specific gene topic distributions per cell type, which captures sub-cell-type variations. GTM-decon can also use phenotype labels from single-cell or bulk data to infer phenotype-specific gene distributions. In a nested-guided design, GTM-decon identified cell-type-specific differentially expressed genes from bulk breast cancer transcriptomes.
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Affiliation(s)
| | - Michael Huang
- School of Computer Science, McGill University, Montreal, QC, Canada
| | - Yue Li
- School of Computer Science, McGill University, Montreal, QC, Canada.
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17
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Vock IW, Simon MD. bakR: uncovering differential RNA synthesis and degradation kinetics transcriptome-wide with Bayesian hierarchical modeling. RNA (NEW YORK, N.Y.) 2023; 29:958-976. [PMID: 37028916 DOI: 10.1261/rna.079451.122] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 03/14/2023] [Indexed: 06/18/2023]
Abstract
Differential expression analysis of RNA sequencing (RNA-seq) data can identify changes in cellular RNA levels, but provides limited information about the kinetic mechanisms underlying such changes. Nucleotide recoding RNA-seq methods (NR-seq; e.g., TimeLapse-seq, SLAM-seq, etc.) address this shortcoming and are widely used approaches to identify changes in RNA synthesis and degradation kinetics. While advanced statistical models implemented in user-friendly software (e.g., DESeq2) have ensured the statistical rigor of differential expression analyses, no such tools that facilitate differential kinetic analysis with NR-seq exist. Here, we report the development of Bayesian analysis of the kinetics of RNA (bakR; https:// github.com/simonlabcode/bakR), an R package to address this need. bakR relies on Bayesian hierarchical modeling of NR-seq data to increase statistical power by sharing information across transcripts. Analyses of simulated data confirmed that bakR implementations of the hierarchical model outperform attempts to analyze differential kinetics with existing models. bakR also uncovers biological signals in real NR-seq data sets and provides improved analyses of existing data sets. This work establishes bakR as an important tool for identifying differential RNA synthesis and degradation kinetics.
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Affiliation(s)
- Isaac W Vock
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut 06536, USA
- Institute of Biomolecular Design and Discovery, Yale University, West Haven, Connecticut 06477, USA
| | - Matthew D Simon
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut 06536, USA
- Institute of Biomolecular Design and Discovery, Yale University, West Haven, Connecticut 06477, USA
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18
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Upton RN, Correr FH, Lile J, Reynolds GL, Falaschi K, Cook JP, Lachowiec J. Design, execution, and interpretation of plant RNA-seq analyses. FRONTIERS IN PLANT SCIENCE 2023; 14:1135455. [PMID: 37457354 PMCID: PMC10348879 DOI: 10.3389/fpls.2023.1135455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 06/12/2023] [Indexed: 07/18/2023]
Abstract
Genomics has transformed our understanding of the genetic architecture of traits and the genetic variation present in plants. Here, we present a review of how RNA-seq can be performed to tackle research challenges addressed by plant sciences. We discuss the importance of experimental design in RNA-seq, including considerations for sampling and replication, to avoid pitfalls and wasted resources. Approaches for processing RNA-seq data include quality control and counting features, and we describe common approaches and variations. Though differential gene expression analysis is the most common analysis of RNA-seq data, we review multiple methods for assessing gene expression, including detecting allele-specific gene expression and building co-expression networks. With the production of more RNA-seq data, strategies for integrating these data into genetic mapping pipelines is of increased interest. Finally, special considerations for RNA-seq analysis and interpretation in plants are needed, due to the high genome complexity common across plants. By incorporating informed decisions throughout an RNA-seq experiment, we can increase the knowledge gained.
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19
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Yan Y, Tian J, Wang Y, Li Y, Zhang C, Zhang S, Lin P, Peng R, Zhao C, Zhuang L, Lai B, Zhou L, Zhang G, Li H. Transcriptomic Heterogeneity of Skin Across Different Anatomic Sites. J Invest Dermatol 2023; 143:398-407.e5. [PMID: 36122800 DOI: 10.1016/j.jid.2022.08.053] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 08/05/2022] [Accepted: 08/26/2022] [Indexed: 02/05/2023]
Abstract
Multiomic studies, including RNA sequencing, single-cell RNA sequencing, and epigenomics, can provide insight into the connection between anatomically heterogeneous gene expression profile of the skin and dermatoses-predisposed sites, in which RNA sequencing is essential. Therefore, in this study, 159 skin samples collected mainly from discarded normal skin tissue during surgical treatment for benign skin tumors were used for RNA sequencing. On the basis of cluster analysis, the skin was divided into four regions, with each region showing specific physiological characteristics through differentially expressed gene analysis. The results showed that the head and neck region, perineum, and palmoplantar area were closely associated with lipid metabolism, hormone metabolism, blood circulation, and related neural regulation, respectively. Transcription factor enrichment indicated that different regions were associated with the development of adjacent tissues. Specifically, the head and neck region, trunk and extremities, perineum, and palmoplantar area were associated with the central nervous, axial, urogenital, and vascular systems, respectively. The results were imported into an open website (https://dermvis.github.io/) for retrieval. Our transcriptomic data elucidated that human skin exhibits transcriptomic heterogeneity reflecting physiological and developmental variation at different anatomic sites and provided guidance for further studies on skin development and dermatoses predisposed sites.
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Affiliation(s)
- Yicen Yan
- Department of Dermatology and Venereology, Peking University First Hospital, Beijing, China; National Clinical Research Center for Skin and Immune Diseases, Beijing, China; Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Beijing, China; NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing, China
| | - Jie Tian
- Department of Dermatology and Venereology, Peking University First Hospital, Beijing, China; Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Yang Wang
- Department of Dermatology and Venereology, Peking University First Hospital, Beijing, China; National Clinical Research Center for Skin and Immune Diseases, Beijing, China; Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Beijing, China; NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing, China
| | - Yurong Li
- Department of Dermatology and Venereology, Peking University First Hospital, Beijing, China; National Clinical Research Center for Skin and Immune Diseases, Beijing, China; Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Beijing, China; NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing, China
| | - Chong Zhang
- Department of Dermatology and Venereology, Peking University First Hospital, Beijing, China; National Clinical Research Center for Skin and Immune Diseases, Beijing, China; Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Beijing, China; NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing, China
| | - Shenxi Zhang
- Department of Dermatology and Venereology, Peking University First Hospital, Beijing, China; National Clinical Research Center for Skin and Immune Diseases, Beijing, China; Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Beijing, China; NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing, China
| | - Pingping Lin
- Department of Dermatology and Venereology, Peking University First Hospital, Beijing, China; National Clinical Research Center for Skin and Immune Diseases, Beijing, China; Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Beijing, China; NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing, China
| | - Rui Peng
- Department of Dermatology and Venereology, Peking University First Hospital, Beijing, China; National Clinical Research Center for Skin and Immune Diseases, Beijing, China; Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Beijing, China; NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing, China
| | - Chunxia Zhao
- Department of Dermatology and Venereology, Peking University First Hospital, Beijing, China; National Clinical Research Center for Skin and Immune Diseases, Beijing, China; Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Beijing, China; NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing, China
| | - Le Zhuang
- Department of Dermatology and Venereology, Peking University First Hospital, Beijing, China; Department of Dermatology, Qilu Hospital of Shandong University, Jinan, China
| | - Binbin Lai
- Department of Dermatology and Venereology, Peking University First Hospital, Beijing, China; Institute of Medical Technology, Peking University Health Science Center, Beijing, China; Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, China
| | - Liang Zhou
- National Institute of Health Data Science, Peking University, Beijing, China
| | - Guohong Zhang
- Department of Dermatology and Venereology, Peking University First Hospital, Beijing, China; Pathology Department, Shantou University Medical College, Guangdong, China
| | - Hang Li
- Department of Dermatology and Venereology, Peking University First Hospital, Beijing, China; National Clinical Research Center for Skin and Immune Diseases, Beijing, China; Beijing Key Laboratory of Molecular Diagnosis on Dermatoses, Beijing, China; NMPA Key Laboratory for Quality Control and Evaluation of Cosmetics, Beijing, China.
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20
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Juan H, Huang H. Quantitative analysis of high‐throughput biological data. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2023. [DOI: 10.1002/wcms.1658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
- Hsueh‐Fen Juan
- Department of Life Science, Institute of Biomedical Electronics and Bioinformatics, and Center for Systems Biology National Taiwan University Taipei Taiwan
- Taiwan AI Labs Taipei Taiwan
| | - Hsuan‐Cheng Huang
- Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan
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21
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Jeon H, Xie J, Jeon Y, Jung KJ, Gupta A, Chang W, Chung D. Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives. Biomolecules 2023; 13:biom13020221. [PMID: 36830591 PMCID: PMC9952882 DOI: 10.3390/biom13020221] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 01/20/2023] [Accepted: 01/21/2023] [Indexed: 01/26/2023] Open
Abstract
Gene expression profiling technologies have been used in various applications such as cancer biology. The development of gene expression profiling has expanded the scope of target discovery in transcriptomic studies, and each technology produces data with distinct characteristics. In order to guarantee biologically meaningful findings using transcriptomic experiments, it is important to consider various experimental factors in a systematic way through statistical power analysis. In this paper, we review and discuss the power analysis for three types of gene expression profiling technologies from a practical standpoint, including bulk RNA-seq, single-cell RNA-seq, and high-throughput spatial transcriptomics. Specifically, we describe the existing power analysis tools for each research objective for each of the bulk RNA-seq and scRNA-seq experiments, along with recommendations. On the other hand, since there are no power analysis tools for high-throughput spatial transcriptomics at this point, we instead investigate the factors that can influence power analysis.
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Affiliation(s)
- Hyeongseon Jeon
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Juan Xie
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
- The Interdisciplinary Ph.D. Program in Biostatistics, The Ohio State University, Columbus, OH 43210, USA
| | - Yeseul Jeon
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
- Department of Statistics and Data Science, Yonsei University, Seoul 03722, Republic of Korea
- Department of Applied Statistics, Yonsei University, Seoul 03722, Republic of Korea
| | - Kyeong Joo Jung
- Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - Arkobrato Gupta
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
- The Interdisciplinary Ph.D. Program in Biostatistics, The Ohio State University, Columbus, OH 43210, USA
| | - Won Chang
- Division of Statistics and Data Science, University of Cincinnati, Cincinnati, OH 45221, USA
| | - Dongjun Chung
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
- The Interdisciplinary Ph.D. Program in Biostatistics, The Ohio State University, Columbus, OH 43210, USA
- Correspondence:
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22
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Chen JW, Shrestha L, Green G, Leier A, Marquez-Lago TT. The hitchhikers' guide to RNA sequencing and functional analysis. Brief Bioinform 2023; 24:bbac529. [PMID: 36617463 PMCID: PMC9851315 DOI: 10.1093/bib/bbac529] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 10/18/2022] [Accepted: 11/07/2022] [Indexed: 01/10/2023] Open
Abstract
DNA and RNA sequencing technologies have revolutionized biology and biomedical sciences, sequencing full genomes and transcriptomes at very high speeds and reasonably low costs. RNA sequencing (RNA-Seq) enables transcript identification and quantification, but once sequencing has concluded researchers can be easily overwhelmed with questions such as how to go from raw data to differential expression (DE), pathway analysis and interpretation. Several pipelines and procedures have been developed to this effect. Even though there is no unique way to perform RNA-Seq analysis, it usually follows these steps: 1) raw reads quality check, 2) alignment of reads to a reference genome, 3) aligned reads' summarization according to an annotation file, 4) DE analysis and 5) gene set analysis and/or functional enrichment analysis. Each step requires researchers to make decisions, and the wide variety of options and resulting large volumes of data often lead to interpretation challenges. There also seems to be insufficient guidance on how best to obtain relevant information and derive actionable knowledge from transcription experiments. In this paper, we explain RNA-Seq steps in detail and outline differences and similarities of different popular options, as well as advantages and disadvantages. We also discuss non-coding RNA analysis, multi-omics, meta-transcriptomics and the use of artificial intelligence methods complementing the arsenal of tools available to researchers. Lastly, we perform a complete analysis from raw reads to DE and functional enrichment analysis, visually illustrating how results are not absolute truths and how algorithmic decisions can greatly impact results and interpretation.
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Affiliation(s)
- Jiung-Wen Chen
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Lisa Shrestha
- Department of Genetics, University of Alabama at Birmingham, School of Medicine, Birmingham, AL, USA
| | - George Green
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - André Leier
- Department of Genetics, University of Alabama at Birmingham, School of Medicine, Birmingham, AL, USA
- Department of Cell, Developmental and Integrative Biology, University of Alabama at Birmingham, School of Medicine, Birmingham, AL, USA
| | - Tatiana T Marquez-Lago
- Department of Genetics, University of Alabama at Birmingham, School of Medicine, Birmingham, AL, USA
- Department of Cell, Developmental and Integrative Biology, University of Alabama at Birmingham, School of Medicine, Birmingham, AL, USA
- Department of Microbiology, University of Alabama at Birmingham, School of Medicine, Birmingham, AL, USA
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23
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Bastide P, Soneson C, Stern DB, Lespinet O, Gallopin M. A Phylogenetic Framework to Simulate Synthetic Interspecies RNA-Seq Data. Mol Biol Evol 2023; 40:6889356. [PMID: 36508357 DOI: 10.1093/molbev/msac269] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 11/14/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
Interspecies RNA-Seq datasets are increasingly common, and have the potential to answer new questions about the evolution of gene expression. Single-species differential expression analysis is now a well-studied problem that benefits from sound statistical methods. Extensive reviews on biological or synthetic datasets have provided the community with a clear picture on the relative performances of the available methods in various settings. However, synthetic dataset simulation tools are still missing in the interspecies gene expression context. In this work, we develop and implement a new simulation framework. This tool builds on both the RNA-Seq and the phylogenetic comparative methods literatures to generate realistic count datasets, while taking into account the phylogenetic relationships between the samples. We illustrate the usefulness of this new framework through a targeted simulation study, that reproduces the features of a recently published dataset, containing gene expression data in adult eye tissue across blind and sighted freshwater crayfish species. Using our simulated datasets, we perform a fair comparison of several approaches used for differential expression analysis. This benchmark reveals some of the strengths and weaknesses of both the classical and phylogenetic approaches for interspecies differential expression analysis, and allows for a reanalysis of the crayfish dataset. The tool has been integrated in the R package compcodeR, freely available on Bioconductor.
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Affiliation(s)
- Paul Bastide
- IMAG, Université de Montpellier, CNRS, Montpellier, France
| | - Charlotte Soneson
- Friedrich Miescher Institute for Biomedical Research, 4058 Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland
| | - David B Stern
- Department of Integrative Biology, University of Wisconsin-Madison, 430 Lincoln Drive, Madison, WI 53706, USA
| | - Olivier Lespinet
- Institute for Integrative Biology of the Cell (I2BC), Université Paris-Saclay, CEA, CNRS, 91198 Gif-sur-Yvette, France
| | - Mélina Gallopin
- Institute for Integrative Biology of the Cell (I2BC), Université Paris-Saclay, CEA, CNRS, 91198 Gif-sur-Yvette, France
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24
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Pauza AG, Murphy D, Paton JFR. Transcriptomics of the Carotid Body. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1427:1-11. [PMID: 37322330 DOI: 10.1007/978-3-031-32371-3_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The carotid body (CB) has emerged as a potential therapeutic target for treating sympathetically mediated cardiovascular, respiratory, and metabolic diseases. In adjunct to its classical role as an arterial O2 sensor, the CB is a multimodal sensor activated by a range of stimuli in the circulation. However, consensus on how CB multimodality is achieved is lacking; even the best studied O2-sensing appears to involve multiple convergent mechanisms. A strategy to understand multimodal sensing is to adopt a hypothesis-free, high-throughput transcriptomic approach. This has proven instrumental for understanding fundamental mechanisms of CB response to hypoxia and other stimulants, its developmental niche, cellular heterogeneity, laterality, and pathophysiological remodeling in disease states. Herein, we review this published work that reveals novel molecular mechanisms underpinning multimodal sensing and reveals numerous gaps in knowledge that require experimental testing.
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Affiliation(s)
- Audrys G Pauza
- Manaaki Manawa - The Centre for Heart Research, Department of Physiology, Faculty of Medical & Health Sciences, University of Auckland, Auckland, New Zealand.
| | - David Murphy
- Molecular Neuroendocrinology Research Group, Bristol Medical School, Translational Health Sciences, University of Bristol, Bristol, UK
| | - Julian F R Paton
- Manaaki Manawa - The Centre for Heart Research, Department of Physiology, Faculty of Medical & Health Sciences, University of Auckland, Auckland, New Zealand
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Tomkins M, Hoerbst F, Gupta S, Apelt F, Kehr J, Kragler F, Morris RJ. Exact Bayesian inference for the detection of graft-mobile transcripts from sequencing data. J R Soc Interface 2022; 19:20220644. [PMID: 36514890 PMCID: PMC9748499 DOI: 10.1098/rsif.2022.0644] [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] [Indexed: 12/15/2022] Open
Abstract
The long-distance transport of messenger RNAs (mRNAs) has been shown to be important for several developmental processes in plants. A popular method for identifying travelling mRNAs is to perform RNA-Seq on grafted plants. This approach depends on the ability to correctly assign sequenced mRNAs to the genetic background from which they originated. The assignment is often based on the identification of single-nucleotide polymorphisms (SNPs) between otherwise identical sequences. A major challenge is therefore to distinguish SNPs from sequencing errors. Here, we show how Bayes factors can be computed analytically using RNA-Seq data over all the SNPs in an mRNA. We used simulations to evaluate the performance of the proposed framework and demonstrate how Bayes factors accurately identify graft-mobile transcripts. The comparison with other detection methods using simulated data shows how not taking the variability in read depth, error rates and multiple SNPs per transcript into account can lead to incorrect classification. Our results suggest experimental design criteria for successful graft-mobile mRNA detection and show the pitfalls of filtering for sequencing errors or focusing on single SNPs within an mRNA.
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Affiliation(s)
- Melissa Tomkins
- Computational and Systems Biology, John Innes Centre, Norwich Research Park, Norwich NR47UH, UK
| | - Franziska Hoerbst
- Computational and Systems Biology, John Innes Centre, Norwich Research Park, Norwich NR47UH, UK
| | - Saurabh Gupta
- Max Planck Institute of Molecular Plant Physiology, Max Planck Institute, Am Mühlenberg 1, Potsdam-Golm 14476, Germany
| | - Federico Apelt
- Max Planck Institute of Molecular Plant Physiology, Max Planck Institute, Am Mühlenberg 1, Potsdam-Golm 14476, Germany
| | - Julia Kehr
- Institute of Plant Science and Microbiology, Universität Hamburg, Ohnhorststrasse 18, Hamburg 22609, Germany
| | - Friedrich Kragler
- Max Planck Institute of Molecular Plant Physiology, Max Planck Institute, Am Mühlenberg 1, Potsdam-Golm 14476, Germany
| | - Richard J. Morris
- Computational and Systems Biology, John Innes Centre, Norwich Research Park, Norwich NR47UH, UK
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Abstract
The immune system is highly complex and distributed throughout an organism, with hundreds to thousands of cell states existing in parallel with diverse molecular pathways interacting in a highly dynamic and coordinated fashion. Although the characterization of individual genes and molecules is of the utmost importance for understanding immune-system function, high-throughput, high-resolution omics technologies combined with sophisticated computational modeling and machine-learning approaches are creating opportunities to complement standard immunological methods with new insights into immune-system dynamics. Like systems immunology itself, immunology researchers must take advantage of these technologies and form their own diverse networks, connecting with researchers from other disciplines. This Review is an introduction and 'how-to guide' for immunologists with no particular experience in the field of omics but with the intention to learn about and apply these systems-level approaches, and for immunologists who want to make the most of interdisciplinary networks.
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27
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Courvan MCS, Niederer RO, Vock IW, Kiefer L, Gilbert WV, Simon MD. Internally controlled RNA sequencing comparisons using nucleoside recoding chemistry. Nucleic Acids Res 2022; 50:e110. [PMID: 36018791 PMCID: PMC9638901 DOI: 10.1093/nar/gkac693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 08/23/2022] [Indexed: 11/30/2022] Open
Abstract
Quantitative comparisons of RNA levels from different samples can lead to new biological understanding if they are able to distinguish biological variation from variable sample preparation. These challenges are pronounced in comparisons that require complex biochemical manipulations (e.g. isolating polysomes to study translation). Here, we present Transcript Regulation Identified by Labeling with Nucleoside Analogues in Cell Culture (TILAC), an internally controlled approach for quantitative comparisons of RNA content. TILAC uses two metabolic labels, 4-thiouridine (s4U) and 6-thioguanosine (s6G), to differentially label RNAs in cells, allowing experimental and control samples to be pooled prior to downstream biochemical manipulations. TILAC leverages nucleoside recoding chemistry to generate characteristic sequencing signatures for each label and uses statistical modeling to compare the abundance of RNA transcripts between samples. We verified the performance of TILAC in transcriptome-scale experiments involving RNA polymerase II inhibition and heat shock. We then applied TILAC to quantify changes in mRNA association with actively translating ribosomes during sodium arsenite stress and discovered a set of transcripts that are translationally upregulated, including MCM2 and DDX5. TILAC is broadly applicable to uncover differences between samples leading to improved biological insights.
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Affiliation(s)
- Meaghan C S Courvan
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT06536, USA.,Institute of Biomolecular Design and Discovery, Yale University, West Haven, CT06477, USA
| | - Rachel O Niederer
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT06536, USA
| | - Isaac W Vock
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT06536, USA.,Institute of Biomolecular Design and Discovery, Yale University, West Haven, CT06477, USA
| | - Lea Kiefer
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT06536, USA.,Institute of Biomolecular Design and Discovery, Yale University, West Haven, CT06477, USA
| | - Wendy V Gilbert
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT06536, USA
| | - Matthew D Simon
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT06536, USA.,Institute of Biomolecular Design and Discovery, Yale University, West Haven, CT06477, USA
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28
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Pinseel E, Nakov T, Van den Berge K, Downey KM, Judy KJ, Kourtchenko O, Kremp A, Ruck EC, Sjöqvist C, Töpel M, Godhe A, Alverson AJ. Strain-specific transcriptional responses overshadow salinity effects in a marine diatom sampled along the Baltic Sea salinity cline. THE ISME JOURNAL 2022; 16:1776-1787. [PMID: 35383290 PMCID: PMC9213524 DOI: 10.1038/s41396-022-01230-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 03/16/2022] [Accepted: 03/21/2022] [Indexed: 05/01/2023]
Abstract
The salinity gradient separating marine and freshwater environments represents a major ecological divide for microbiota, yet the mechanisms by which marine microbes have adapted to and ultimately diversified in freshwater environments are poorly understood. Here, we take advantage of a natural evolutionary experiment: the colonization of the brackish Baltic Sea by the ancestrally marine diatom Skeletonema marinoi. To understand how diatoms respond to low salinity, we characterized transcriptomic responses of acclimated S. marinoi grown in a common garden. Our experiment included eight strains from source populations spanning the Baltic Sea salinity cline. Gene expression analysis revealed that low salinities induced changes in the cellular metabolism of S. marinoi, including upregulation of photosynthesis and storage compound biosynthesis, increased nutrient demand, and a complex response to oxidative stress. However, the strain effect overshadowed the salinity effect, as strains differed significantly in their response, both regarding the strength and the strategy (direction of gene expression) of their response. The high degree of intraspecific variation in gene expression observed here highlights an important but often overlooked source of biological variation associated with how diatoms respond to environmental change.
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Affiliation(s)
- Eveline Pinseel
- Department of Biological Sciences, University of Arkansas, Fayetteville, AR, USA.
| | - Teofil Nakov
- Department of Biological Sciences, University of Arkansas, Fayetteville, AR, USA
| | - Koen Van den Berge
- Department of Statistics, University of California, Berkeley, CA, USA
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
- Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
| | - Kala M Downey
- Department of Biological Sciences, University of Arkansas, Fayetteville, AR, USA
| | - Kathryn J Judy
- Department of Biological Sciences, University of Arkansas, Fayetteville, AR, USA
| | - Olga Kourtchenko
- Department of Marine Sciences, University of Gothenburg, Gothenburg, Sweden
| | - Anke Kremp
- Leibniz-Institute for Baltic Sea Research Warnemünde, Rostock, Germany
| | - Elizabeth C Ruck
- Department of Biological Sciences, University of Arkansas, Fayetteville, AR, USA
| | - Conny Sjöqvist
- Environmental and Marine Biology, Åbo Akademi University, Åbo, Finland
| | - Mats Töpel
- Department of Marine Sciences, University of Gothenburg, Gothenburg, Sweden
| | - Anna Godhe
- Department of Marine Sciences, University of Gothenburg, Gothenburg, Sweden
| | - Andrew J Alverson
- Department of Biological Sciences, University of Arkansas, Fayetteville, AR, USA.
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Lehmann U, Stenzinger A. [The molecular pathology breviary: What do "WGS, WES, transcriptome, RNAseq" mean?]. PATHOLOGIE (HEIDELBERG, GERMANY) 2022; 43:317-318. [PMID: 35258653 DOI: 10.1007/s00292-022-01058-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/02/2022] [Indexed: 12/14/2022]
Affiliation(s)
- Ulrich Lehmann
- Molekularpathologie, Institut für Pathologie, Medizinische Hochschule Hannover, OE5110, Carl-Neuberg-Str. 1, 30625, Hannover, Deutschland.
| | - Albrecht Stenzinger
- Molekularpathologisches Zentrum, Pathologisches Institut, Universität Heidelberg, Im Neuenheimer Feld 224, 69120, Heidelberg, Deutschland.
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30
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Daunesse M, Legendre R, Varet H, Pain A, Chica C. ePeak: from replicated chromatin profiling data to epigenomic dynamics. NAR Genom Bioinform 2022; 4:lqac041. [PMID: 35664802 PMCID: PMC9154330 DOI: 10.1093/nargab/lqac041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 04/05/2022] [Accepted: 05/05/2022] [Indexed: 11/14/2022] Open
Abstract
We present ePeak, a Snakemake-based pipeline for the identification and quantification of reproducible peaks from raw ChIP-seq, CUT&RUN and CUT&Tag epigenomic profiling techniques. It also includes a statistical module to perform tailored differential marking and binding analysis with state of the art methods. ePeak streamlines critical steps like the quality assessment of the immunoprecipitation, spike-in calibration and the selection of reproducible peaks between replicates for both narrow and broad peaks. It generates complete reports for data quality control assessment and optimal interpretation of the results. We advocate for a differential analysis that accounts for the biological dynamics of each chromatin factor. Thus, ePeak provides linear and nonlinear methods for normalisation as well as conservative and stringent models for variance estimation and significance testing of the observed marking/binding differences. Using a published ChIP-seq dataset, we show that distinct populations of differentially marked/bound peaks can be identified. We study their dynamics in terms of read coverage and summit position, as well as the expression of the neighbouring genes. We propose that ePeak can be used to measure the richness of the epigenomic landscape underlying a biological process by identifying diverse regulatory regimes.
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Affiliation(s)
- Maëlle Daunesse
- Bioinformatics and Biostatistics Hub, Institut Pasteur, Université de Paris, Paris F-75015, France
| | - Rachel Legendre
- Bioinformatics and Biostatistics Hub, Institut Pasteur, Université de Paris, Paris F-75015, France
| | - Hugo Varet
- Bioinformatics and Biostatistics Hub, Institut Pasteur, Université de Paris, Paris F-75015, France
| | - Adrien Pain
- Bioinformatics and Biostatistics Hub, Institut Pasteur, Université de Paris, Paris F-75015, France
| | - Claudia Chica
- Bioinformatics and Biostatistics Hub, Institut Pasteur, Université de Paris, Paris F-75015, France
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31
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Ludt A, Ustjanzew A, Binder H, Strauch K, Marini F. Interactive and Reproducible Workflows for Exploring and Modeling RNA-seq Data with pcaExplorer, Ideal, and GeneTonic. Curr Protoc 2022; 2:e411. [PMID: 35467799 DOI: 10.1002/cpz1.411] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The generation and interpretation of results from transcriptome profiling experiments via RNA sequencing (RNA-seq) can be a complex task. While raw data quality control, alignment, and quantification can be streamlined via efficient algorithms that can deliver the preprocessed expression matrix, a common bottleneck in the analysis of such large datasets is the subsequent in-depth, iterative processes of data exploration, statistical testing, visualization, and interpretation. Specific tools for these workflow steps are available but require a level of technical expertise which might be prohibitive for life and clinical scientists, who are left with essential pieces of information distributed among different tabular and list formats. Our protocols are centered on the joint use of our Bioconductor packages (pcaExplorer, ideal, GeneTonic) for interactive and reproducible workflows. All our packages provide an interactive and accessible experience via Shiny web applications, while still documenting the steps performed with RMarkdown as a framework to guarantee the reproducibility of the analyses, reducing the overall time to generate insights from the data at hand. These protocols guide readers through the essential steps of Exploratory Data Analysis, statistical testing, and functional enrichment analyses, followed by integration and contextualization of results. In our packages, the core elements are linked together in interactive widgets that make drill-down tasks efficient by viewing the data at a level of increased detail. Thanks to their interoperability with essential classes and gold-standard pipelines implemented in the open-source Bioconductor project and community, these protocols will permit complex tasks in RNA-seq data analysis, combining interactivity and reproducibility for following modern best scientific practices and helping to streamline the discovery process for transcriptome data. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Exploratory Data Analysis with pcaExplorer Basic Protocol 2: Differential Expression Analysis with ideal Basic Protocol 3: Interpretation of RNA-seq results with GeneTonic Support Protocol: Downloading and installing pcaExplorer, ideal, and GeneTonic Alternate Protocol: Using functions from pcaExplorer, ideal, and GeneTonic in custom analyses.
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Affiliation(s)
- Annekathrin Ludt
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), Division Statistical Genomics and Bioinformatics, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Arsenij Ustjanzew
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), Division Statistical Genomics and Bioinformatics, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Harald Binder
- Institute of Medical Biometry and Statistics (IMBI), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg im Breisgau, Germany
| | - Konstantin Strauch
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), Division Statistical Genomics and Bioinformatics, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Federico Marini
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), Division Statistical Genomics and Bioinformatics, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.,Center for Thrombosis and Hemostasis Mainz (CTH), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
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32
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CyVerse for Reproducible Research: RNA-Seq Analysis. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2443:57-79. [PMID: 35037200 DOI: 10.1007/978-1-0716-2067-0_3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Posing complex research questions poses complex reproducibility challenges. Datasets may need to be managed over long periods of time. Reliable and secure repositories are needed for data storage. Sharing big data requires advance planning and becomes complex when collaborators are spread across institutions and countries. Many complex analyses require the larger compute resources only provided by cloud and high-performance computing infrastructure. Finally at publication, funder and publisher requirements must be met for data availability and accessibility and computational reproducibility. For all of these reasons, cloud-based cyberinfrastructures are an important component for satisfying the needs of data-intensive research. Learning how to incorporate these technologies into your research skill set will allow you to work with data analysis challenges that are often beyond the resources of individual research institutions. One of the advantages of CyVerse is that there are many solutions for high-powered analyses that do not require knowledge of command line (i.e., Linux) computing. In this chapter we will highlight CyVerse capabilities by analyzing RNA-Seq data. The lessons learned will translate to doing RNA-Seq in other computing environments and will focus on how CyVerse infrastructure supports reproducibility goals (e.g., metadata management, containers), team science (e.g., data sharing features), and flexible computing environments (e.g., interactive computing, scaling).
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Behavioral Neuroscience in the Era of Genomics: Tools and Lessons for Analyzing High-Dimensional Datasets. Int J Mol Sci 2022; 23:ijms23073811. [PMID: 35409169 PMCID: PMC8998543 DOI: 10.3390/ijms23073811] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/26/2022] [Accepted: 03/29/2022] [Indexed: 12/10/2022] Open
Abstract
Behavioral neuroscience underwent a technology-driven revolution with the emergence of machine-vision and machine-learning technologies. These technological advances facilitated the generation of high-resolution, high-throughput capture and analysis of complex behaviors. Therefore, behavioral neuroscience is becoming a data-rich field. While behavioral researchers use advanced computational tools to analyze the resulting datasets, the search for robust and standardized analysis tools is still ongoing. At the same time, the field of genomics exploded with a plethora of technologies which enabled the generation of massive datasets. This growth of genomics data drove the emergence of powerful computational approaches to analyze these data. Here, we discuss the composition of a large behavioral dataset, and the differences and similarities between behavioral and genomics data. We then give examples of genomics-related tools that might be of use for behavioral analysis and discuss concepts that might emerge when considering the two fields together.
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34
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Raghavan V, Kraft L, Mesny F, Rigerte L. A simple guide to de novo transcriptome assembly and annotation. Brief Bioinform 2022; 23:6514404. [PMID: 35076693 PMCID: PMC8921630 DOI: 10.1093/bib/bbab563] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 12/03/2021] [Accepted: 12/09/2021] [Indexed: 12/13/2022] Open
Abstract
A transcriptome constructed from short-read RNA sequencing (RNA-seq) is an easily attainable proxy catalog of protein-coding genes when genome assembly is unnecessary, expensive or difficult. In the absence of a sequenced genome to guide the reconstruction process, the transcriptome must be assembled de novo using only the information available in the RNA-seq reads. Subsequently, the sequences must be annotated in order to identify sequence-intrinsic and evolutionary features in them (for example, protein-coding regions). Although straightforward at first glance, de novo transcriptome assembly and annotation can quickly prove to be challenging undertakings. In addition to familiarizing themselves with the conceptual and technical intricacies of the tasks at hand and the numerous pre- and post-processing steps involved, those interested must also grapple with an overwhelmingly large choice of tools. The lack of standardized workflows, fast pace of development of new tools and techniques and paucity of authoritative literature have served to exacerbate the difficulty of the task even further. Here, we present a comprehensive overview of de novo transcriptome assembly and annotation. We discuss the procedures involved, including pre- and post-processing steps, and present a compendium of corresponding tools.
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Affiliation(s)
- Venket Raghavan
- Corresponding authors: Venket Raghavan, Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, 37077 Göttingen, Germany. E-mail: ; Louis Kraft, Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, 37077 Göttingen, Germany. E-mail:
| | - Louis Kraft
- Corresponding authors: Venket Raghavan, Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, 37077 Göttingen, Germany. E-mail: ; Louis Kraft, Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, 37077 Göttingen, Germany. E-mail:
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Signal B, Kahlke T. how_are_we_stranded_here: quick determination of RNA-Seq strandedness. BMC Bioinformatics 2022; 23:49. [PMID: 35065593 PMCID: PMC8783475 DOI: 10.1186/s12859-022-04572-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 01/10/2022] [Indexed: 11/07/2022] Open
Abstract
Background Quality control checks are the first step in RNA-Sequencing analysis, which enable the identification of common issues that occur in the sequenced reads. Checks for sequence quality, contamination, and complexity are commonplace, and allow users to implement steps downstream which can account for these issues. Strand-specificity of reads is frequently overlooked and is often unavailable even in published data, yet when unknown or incorrectly specified can have detrimental effects on the reproducibility and accuracy of downstream analyses. Results To address these issues, we developed how_are_we_stranded_here, a Python library that helps to quickly infer strandedness of paired-end RNA-Sequencing data. Testing on both simulated and real RNA-Sequencing reads showed that it correctly measures strandedness, and measures outside the normal range may indicate sample contamination. Conclusions how_are_we_stranded_here is fast and user friendly, making it easy to implement in quality control pipelines prior to analysing RNA-Sequencing data. how_are_we_stranded_here is freely available at https://github.com/betsig/how_are_we_stranded_here. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04572-7.
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36
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Qu H, Qu M, Wang S, Yu L, Jia Q, Wang X, Jia Z. Differential Expression Analysis: Simple Pair, Interaction, Time-series. Bio Protoc 2022. [DOI: 10.21769/bioprotoc.4455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
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37
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Artificial Intelligence in Blood Transcriptomics. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Abstract
Changes in the surrounding environment are mirrored by changes in the transcript profile of an organism. In the case of a plant pathogen, host colonization would be a challenge that triggers changes in transcript expression patterns. Determining the transcriptional profile could provide valuable clues on how an organism responds to defined stimuli, in this case, how a pathogen colonizes its host. Several robust data analysis methods and pipelines are available that can identify these differentially expressed transcripts. In this chapter we outline the steps and other caveats that are needed to run one such pipeline.
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Affiliation(s)
- Navdeep Gill
- Department of Crop, Soil and Environmental Sciences, University of Arkansas, Fayetteville, AR, USA
| | - Braham Dhillon
- Department of Plant Pathology, University of Florida, Fort Lauderdale Research and Education Center, Davie, FL, USA.
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Shawky AEM, Dondeti M, Mourelatos Z, Vourekas A. Solid-Support Directional (SSD) RNA-Seq as a Companion Method to CLIP-Seq. Methods Mol Biol 2022; 2509:251-268. [PMID: 35796968 DOI: 10.1007/978-1-0716-2380-0_15] [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: 06/15/2023]
Abstract
CLIP-Seq (Deep Sequencing after in vivo Crosslinking and Immunoprecipitation, HITS-CLIP) has emerged as a key method for the study of RNA-binding proteins (RBPs), as it can scrutinize the RNAs bound by an RBP in vivo, with minimum manipulation of biological samples. CLIP-Seq is best used to reveal changes of the RNA cargo of an RBP and differences on binding patterns of the bound RNAs in living cells in different genetic backgrounds or after experimental treatment, rather than simply identifying RNA species. It is therefore crucial that a reference of the steady state levels of the RNAs present in the samples used for the CLIP-Seq experiment is included in the bioinformatic analysis. A simple directional RNA-Seq method was developed that uses the same oligonucleotides and the same PCR amplification steps as our CLIP-Seq method, which therefore can be analyzed using the same bioinformatic pipeline as the CLIP-Seq data. This greatly simplifies and streamlines the analysis process, and at the same time reduces the chances of protocol-specific artifacts and biases interfering with data interpretation. Some considerations on ways to integrate CLIP-Seq and RNA-Seq analyses are also provided herein.
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Affiliation(s)
- Abd-El Monsif Shawky
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA
| | - Mahmoud Dondeti
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA
| | - Zissimos Mourelatos
- Division of Neuropathology, Department of Pathology and Laboratory Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | - Anastasios Vourekas
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA.
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Marini F, Ludt A, Linke J, Strauch K. GeneTonic: an R/Bioconductor package for streamlining the interpretation of RNA-seq data. BMC Bioinformatics 2021; 22:610. [PMID: 34949163 PMCID: PMC8697502 DOI: 10.1186/s12859-021-04461-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 10/26/2021] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND The interpretation of results from transcriptome profiling experiments via RNA sequencing (RNA-seq) can be a complex task, where the essential information is distributed among different tabular and list formats-normalized expression values, results from differential expression analysis, and results from functional enrichment analyses. A number of tools and databases are widely used for the purpose of identification of relevant functional patterns, yet often their contextualization within the data and results at hand is not straightforward, especially if these analytic components are not combined together efficiently. RESULTS We developed the GeneTonic software package, which serves as a comprehensive toolkit for streamlining the interpretation of functional enrichment analyses, by fully leveraging the information of expression values in a differential expression context. GeneTonic is implemented in R and Shiny, leveraging packages that enable HTML-based interactive visualizations for executing drilldown tasks seamlessly, viewing the data at a level of increased detail. GeneTonic is integrated with the core classes of existing Bioconductor workflows, and can accept the output of many widely used tools for pathway analysis, making this approach applicable to a wide range of use cases. Users can effectively navigate interlinked components (otherwise available as flat text or spreadsheet tables), bookmark features of interest during the exploration sessions, and obtain at the end a tailored HTML report, thus combining the benefits of both interactivity and reproducibility. CONCLUSION GeneTonic is distributed as an R package in the Bioconductor project ( https://bioconductor.org/packages/GeneTonic/ ) under the MIT license. Offering both bird's-eye views of the components of transcriptome data analysis and the detailed inspection of single genes, individual signatures, and their relationships, GeneTonic aims at simplifying the process of interpretation of complex and compelling RNA-seq datasets for many researchers with different expertise profiles.
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Affiliation(s)
- Federico Marini
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center of the Johannes Gutenberg University Mainz, Obere Zahlbacher Str. 69, 55131 Mainz, Germany
- Center for Thrombosis and Hemostasis (CTH), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, 55131 Mainz, Germany
| | - Annekathrin Ludt
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center of the Johannes Gutenberg University Mainz, Obere Zahlbacher Str. 69, 55131 Mainz, Germany
| | - Jan Linke
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center of the Johannes Gutenberg University Mainz, Obere Zahlbacher Str. 69, 55131 Mainz, Germany
- Center for Thrombosis and Hemostasis (CTH), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, 55131 Mainz, Germany
| | - Konstantin Strauch
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center of the Johannes Gutenberg University Mainz, Obere Zahlbacher Str. 69, 55131 Mainz, Germany
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Abstract
Studying individual mammalian oocytes has been extremely valuable for the understanding of the molecular composition of oocytes including RNA storage. Here, a detailed protocol for isolation of oocytes, extraction of total RNA from single oocytes followed by full-length cDNA amplification, and library preparation is presented. The procedure permits the production of cost-effective and high-quality sequencing libraries. This protocol can be adapted for transcriptome analysis of oocytes from other species and be used to generate high-quality data from single embryos. For complete details on the use and execution of this protocol, please refer to Biase and Kimble (2018). Isolation of high-quality total RNA from single bovine oocytes Detailed procedures for amplification of complementary DNA for library preparation A bioinformatic pipeline for the quantitation of transcript abundance The protocol also enables high-quality data production from single embryos
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Affiliation(s)
- Fernando H Biase
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
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42
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Shields EJ, Sorida M, Sheng L, Sieriebriennikov B, Ding L, Bonasio R. Genome annotation with long RNA reads reveals new patterns of gene expression and improves single-cell analyses in an ant brain. BMC Biol 2021; 19:254. [PMID: 34838024 PMCID: PMC8626913 DOI: 10.1186/s12915-021-01188-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 11/10/2021] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Functional genomic analyses rely on high-quality genome assemblies and annotations. Highly contiguous genome assemblies have become available for a variety of species, but accurate and complete annotation of gene models, inclusive of alternative splice isoforms and transcription start and termination sites, remains difficult with traditional approaches. RESULTS Here, we utilized full-length isoform sequencing (Iso-Seq), a long-read RNA sequencing technology, to obtain a comprehensive annotation of the transcriptome of the ant Harpegnathos saltator. The improved genome annotations include additional splice isoforms and extended 3' untranslated regions for more than 4000 genes. Reanalysis of RNA-seq experiments using these annotations revealed several genes with caste-specific differential expression and tissue- or caste-specific splicing patterns that were missed in previous analyses. The extended 3' untranslated regions afforded great improvements in the analysis of existing single-cell RNA-seq data, resulting in the recovery of the transcriptomes of 18% more cells. The deeper single-cell transcriptomes obtained with these new annotations allowed us to identify additional markers for several cell types in the ant brain, as well as genes differentially expressed across castes in specific cell types. CONCLUSIONS Our results demonstrate that Iso-Seq is an efficient and effective approach to improve genome annotations and maximize the amount of information that can be obtained from existing and future genomic datasets in Harpegnathos and other organisms.
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Affiliation(s)
- Emily J Shields
- Epigenetics Institute and Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Urology and Institute of Neuropathology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Masato Sorida
- Epigenetics Institute and Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Lihong Sheng
- Epigenetics Institute and Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Bogdan Sieriebriennikov
- Department of Biology, New York University, New York, NY, USA
- Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY, USA
| | - Long Ding
- Department of Biology, New York University, New York, NY, USA
| | - Roberto Bonasio
- Epigenetics Institute and Department of Cell and Developmental Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
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43
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Puła A, Robak P, Mikulski D, Robak T. The Significance of mRNA in the Biology of Multiple Myeloma and Its Clinical Implications. Int J Mol Sci 2021; 22:12070. [PMID: 34769503 PMCID: PMC8584466 DOI: 10.3390/ijms222112070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 10/28/2021] [Accepted: 11/04/2021] [Indexed: 11/16/2022] Open
Abstract
Multiple myeloma (MM) is a genetically complex disease that results from a multistep transformation of normal to malignant plasma cells in the bone marrow. However, the molecular mechanisms responsible for the initiation and heterogeneous evolution of MM remain largely unknown. A fundamental step needed to understand the oncogenesis of MM and its response to therapy is the identification of driver mutations. The introduction of gene expression profiling (GEP) in MM is an important step in elucidating the molecular heterogeneity of MM and its clinical relevance. Since some mutations in myeloma occur in non-coding regions, studies based on the analysis of mRNA provide more comprehensive information on the oncogenic pathways and mechanisms relevant to MM biology. In this review, we discuss the role of gene expression profiling in understanding the biology of multiple myeloma together with the clinical manifestation of the disease, as well as its impact on treatment decisions and future directions.
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Affiliation(s)
- Anna Puła
- Department of Hematology, Medical University of Lodz, 93-510 Lodz, Poland;
| | - Paweł Robak
- Department of Experimental Hematology, Medical University of Lodz, 93-510 Lodz, Poland;
| | - Damian Mikulski
- Department of Biostatistics and Translational Medicine, Medical University of Lodz, 92-215 Lodz, Poland;
| | - Tadeusz Robak
- Department of Hematology, Medical University of Lodz, 93-510 Lodz, Poland;
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44
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Maehara K, Tomimatsu K, Harada A, Tanaka K, Sato S, Fukuoka M, Okada S, Handa T, Kurumizaka H, Saitoh N, Kimura H, Ohkawa Y. Modeling population size independent tissue epigenomes by ChIL-seq with single thin sections. Mol Syst Biol 2021; 17:e10323. [PMID: 34730297 PMCID: PMC8564819 DOI: 10.15252/msb.202110323] [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: 03/01/2021] [Revised: 09/29/2021] [Accepted: 10/01/2021] [Indexed: 11/25/2022] Open
Abstract
Recent advances in genome‐wide technologies have enabled analyses using small cell numbers of even single cells. However, obtaining tissue epigenomes with cell‐type resolution from large organs and tissues still remains challenging, especially when the available material is limited. Here, we present a ChIL‐based approach for analyzing the diverse cellular dynamics at the tissue level using high‐depth epigenomic data. “ChIL for tissues” allows the analysis of a single tissue section and can reproducibly generate epigenomic profiles from several tissue types, based on the distribution of target epigenomic states, tissue morphology, and number of cells. The proposed method enabled the independent evaluation of changes in cell populations and gene activation in cells from regenerating skeletal muscle tissues, using a statistical model of RNA polymerase II distribution on gene loci. Thus, the integrative analyses performed using ChIL can elucidate in vivo cell‐type dynamics of tissues.
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Affiliation(s)
- Kazumitsu Maehara
- Division of Transcriptomics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Kosuke Tomimatsu
- Division of Transcriptomics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Akihito Harada
- Division of Transcriptomics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Kaori Tanaka
- Division of Transcriptomics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Shoko Sato
- Laboratory of Chromatin Structure and Function, Institute for Quantitative Biosciences, The University of Tokyo, Tokyo, Japan
| | - Megumi Fukuoka
- Division of Cancer Biology, The Cancer Institute of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Seiji Okada
- Division of Pathophysiology, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Tetsuya Handa
- Cell Biology Center, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan
| | - Hitoshi Kurumizaka
- Laboratory of Chromatin Structure and Function, Institute for Quantitative Biosciences, The University of Tokyo, Tokyo, Japan
| | - Noriko Saitoh
- Division of Cancer Biology, The Cancer Institute of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Hiroshi Kimura
- Cell Biology Center, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan
| | - Yasuyuki Ohkawa
- Division of Transcriptomics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
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45
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Jia L, Li Y, Huang F, Jiang Y, Li H, Wang Z, Chen T, Li J, Zhang Z, Yao W. LIRBase: a comprehensive database of long inverted repeats in eukaryotic genomes. Nucleic Acids Res 2021; 50:D174-D182. [PMID: 34643715 PMCID: PMC8728187 DOI: 10.1093/nar/gkab912] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 09/20/2021] [Accepted: 09/25/2021] [Indexed: 11/14/2022] Open
Abstract
Small RNAs (sRNAs) constitute a large portion of functional elements in eukaryotic genomes. Long inverted repeats (LIRs) can be transcribed into long hairpin RNAs (hpRNAs), which can further be processed into small interfering RNAs (siRNAs) with vital biological roles. In this study, we systematically identified a total of 6 619 473 LIRs in 424 eukaryotic genomes and developed LIRBase (https://venyao.xyz/lirbase/), a specialized database of LIRs across different eukaryotic genomes aiming to facilitate the annotation and identification of LIRs encoding long hpRNAs and siRNAs. LIRBase houses a comprehensive collection of LIRs identified in a wide range of eukaryotic genomes. In addition, LIRBase not only allows users to browse and search the identified LIRs in any eukaryotic genome(s) of interest available in GenBank, but also provides friendly web functionalities to facilitate users to identify LIRs in user-uploaded sequences, align sRNA sequencing data to LIRs, perform differential expression analysis of LIRs, predict mRNA targets for LIR-derived siRNAs, and visualize the secondary structure of candidate long hpRNAs encoded by LIRs. As demonstrated by two case studies, collectively, LIRBase bears the great utility for systematic investigation and characterization of LIRs and functional exploration of potential roles of LIRs and their derived siRNAs in diverse species.
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Affiliation(s)
- Lihua Jia
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China.,National Key Laboratory of Wheat and Maize Crop Science, College of Agronomy, Henan Agricultural University, Zhengzhou 450002, China
| | - Yang Li
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Fangfang Huang
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Yingru Jiang
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Haoran Li
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Zhizhan Wang
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Tiantian Chen
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Jiaming Li
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Zhang Zhang
- China National Center for Bioinformation, Beijing 100101, China.,National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100101, China
| | - Wen Yao
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
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46
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Dankó B, Szikora P, Pór T, Szeifert A, Sebestyén E. SplicingFactory-splicing diversity analysis for transcriptome data. Bioinformatics 2021; 38:384-390. [PMID: 34499147 PMCID: PMC8722757 DOI: 10.1093/bioinformatics/btab648] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 07/31/2021] [Accepted: 09/06/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Alternative splicing contributes to the diversity of RNA found in biological samples. Current tools investigating patterns of alternative splicing check for coordinated changes in the expression or relative ratio of RNA isoforms where specific isoforms are up- or down-regulated in a condition. However, the molecular process of splicing is stochastic and changes in RNA isoform diversity for a gene might arise between samples or conditions. A specific condition can be dominated by a single isoform, while multiple isoforms with similar expression levels can be present in a different condition. These changes might be the result of mutations, drug treatments or differences in the cellular or tissue environment. Here, we present a tool for the characterization and analysis of RNA isoform diversity using isoform level expression measurements. RESULTS We developed an R package called SplicingFactory, to calculate various RNA isoform diversity metrics, and compare them across conditions. Using the package, we tested the effect of RNA-seq quantification tools, quantification uncertainty, gene expression levels and isoform numbers on the isoform diversity calculation. We analyzed a set of CD34+ hematopoietic stem cells and myelodysplastic syndrome samples and found a set of genes whose isoform diversity change is associated with SF3B1 mutations. AVAILABILITY AND IMPLEMENTATION The SplicingFactory package is freely available under the GPL-3.0 license from Bioconductor for the Windows, MacOS and Linux operating systems (https://www.bioconductor.org/packages/release/bioc/html/SplicingFactory.html). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Benedek Dankó
- Department of Genetics, Eötvös Loránd University, Budapest H-1053, Hungary
| | - Péter Szikora
- 1st Department of Pathology and Experimental Cancer Research, Semmelweis University, Budapest H-1085, Hungary
| | - Tamás Pór
- 1st Department of Pathology and Experimental Cancer Research, Semmelweis University, Budapest H-1085, Hungary
| | - Alexa Szeifert
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest H-1083, Hungary
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47
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Gene expression profile analysis of gallic acid-induced cell death process. Sci Rep 2021; 11:16743. [PMID: 34408198 PMCID: PMC8373985 DOI: 10.1038/s41598-021-96174-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 07/28/2021] [Indexed: 12/25/2022] Open
Abstract
Gallic acid is a natural phenolic compound that displays anti-cancer properties in clinically relevant cell culture and rodent models. To date, the molecular mechanism governing the gallic acid-induced cancer cell death process is largely unclear, thus hindering development of novel therapeutics. Therefore, we performed time-course RNA-sequencing to reveal the gene expression profiles at the early (2nd hour), middle (4th and 6th hour), and late (9th hour) stages of the gallic acid-induced cell death process in HeLa cells. By Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, we found significant changes in transcription of the genes in different types of cell death pathways. This involved the ferroptotic cell death pathway at the early stage, apoptotic pathway at the middle stage, and necroptotic pathway at the late stage. Metabolic pathways were identified at all the stages, indicating that this is an active cell death process. Interestingly, the initiation and execution of gallic acid-induced cell death were mediated by multiple biological processes, including iron and amino acid metabolism, and the biosynthesis of glutathione, as targeting on these pathways suppressed cell death. In summary, our work provides a dataset with differentially expressed genes across different stages of cell death process during the gallic acid induction, which is important for further study on the control of this cell death mechanism.
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48
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Krappinger JC, Bonstingl L, Pansy K, Sallinger K, Wreglesworth NI, Grinninger L, Deutsch A, El-Heliebi A, Kroneis T, Mcfarlane RJ, Sensen CW, Feichtinger J. Non-coding Natural Antisense Transcripts: Analysis and Application. J Biotechnol 2021; 340:75-101. [PMID: 34371054 DOI: 10.1016/j.jbiotec.2021.08.005] [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: 01/29/2021] [Revised: 06/30/2021] [Accepted: 08/04/2021] [Indexed: 12/12/2022]
Abstract
Non-coding natural antisense transcripts (ncNATs) are regulatory RNA sequences that are transcribed in the opposite direction to protein-coding or non-coding transcripts. These transcripts are implicated in a broad variety of biological and pathological processes, including tumorigenesis and oncogenic progression. With this complex field still in its infancy, annotations, expression profiling and functional characterisations of ncNATs are far less comprehensive than those for protein-coding genes, pointing out substantial gaps in the analysis and characterisation of these regulatory transcripts. In this review, we discuss ncNATs from an analysis perspective, in particular regarding the use of high-throughput sequencing strategies, such as RNA-sequencing, and summarize the unique challenges of investigating the antisense transcriptome. Finally, we elaborate on their potential as biomarkers and future targets for treatment, focusing on cancer.
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Affiliation(s)
- Julian C Krappinger
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Center for Cell Signalling, Metabolism and Aging, Medical University of Graz, Neue Stiftingtalstraße 6/II, 8010 Graz, Austria; Christian Doppler Laboratory for innovative Pichia pastoris host and vector systems, Division of Cell Biology, Histology and Embryology, Medical University of Graz, Neue Stiftingtalstraße 6/II, 8010 Graz, Austria
| | - Lilli Bonstingl
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Center for Cell Signalling, Metabolism and Aging, Medical University of Graz, Neue Stiftingtalstraße 6/II, 8010 Graz, Austria; Center for Biomarker Research in Medicine, Stiftingtalstraße 5, 8010 Graz, Austria
| | - Katrin Pansy
- Division of Haematology, Medical University of Graz, Stiftingtalstrasse 24, 8010 Graz, Austria
| | - Katja Sallinger
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Center for Cell Signalling, Metabolism and Aging, Medical University of Graz, Neue Stiftingtalstraße 6/II, 8010 Graz, Austria; Center for Biomarker Research in Medicine, Stiftingtalstraße 5, 8010 Graz, Austria
| | - Nick I Wreglesworth
- North West Cancer Research Institute, School of Medical Sciences, Bangor University, LL57 2UW Bangor, United Kingdom
| | - Lukas Grinninger
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Center for Cell Signalling, Metabolism and Aging, Medical University of Graz, Neue Stiftingtalstraße 6/II, 8010 Graz, Austria; Austrian Biotech University of Applied Sciences, Konrad Lorenz-Straße 10, 3430 Tulln an der Donau, Austria
| | - Alexander Deutsch
- Division of Haematology, Medical University of Graz, Stiftingtalstrasse 24, 8010 Graz, Austria; BioTechMed-Graz, Mozartgasse 12/II, 8010 Graz, Austria
| | - Amin El-Heliebi
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Center for Cell Signalling, Metabolism and Aging, Medical University of Graz, Neue Stiftingtalstraße 6/II, 8010 Graz, Austria; Center for Biomarker Research in Medicine, Stiftingtalstraße 5, 8010 Graz, Austria
| | - Thomas Kroneis
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Center for Cell Signalling, Metabolism and Aging, Medical University of Graz, Neue Stiftingtalstraße 6/II, 8010 Graz, Austria; Center for Biomarker Research in Medicine, Stiftingtalstraße 5, 8010 Graz, Austria
| | - Ramsay J Mcfarlane
- North West Cancer Research Institute, School of Medical Sciences, Bangor University, LL57 2UW Bangor, United Kingdom
| | - Christoph W Sensen
- BioTechMed-Graz, Mozartgasse 12/II, 8010 Graz, Austria; Institute of Computational Biotechnology, Graz University of Technology, Petersgasse 14/V, 8010 Graz, Austria; HCEMM Kft., Római blvd. 21, 6723 Szeged, Hungary
| | - Julia Feichtinger
- Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Center for Cell Signalling, Metabolism and Aging, Medical University of Graz, Neue Stiftingtalstraße 6/II, 8010 Graz, Austria; Christian Doppler Laboratory for innovative Pichia pastoris host and vector systems, Division of Cell Biology, Histology and Embryology, Medical University of Graz, Neue Stiftingtalstraße 6/II, 8010 Graz, Austria; BioTechMed-Graz, Mozartgasse 12/II, 8010 Graz, Austria.
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49
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Feltes BC, Poloni JDF, Dorn M. Benchmarking and Testing Machine Learning Approaches with BARRA:CuRDa, a Curated RNA-Seq Database for Cancer Research. J Comput Biol 2021; 28:931-944. [PMID: 34264745 DOI: 10.1089/cmb.2020.0463] [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] [Indexed: 12/13/2022] Open
Abstract
RNA-seq is gradually becoming the dominating technique employed to access the global gene expression in biological samples, allowing more flexible protocols and robust analysis. However, the nature of RNA-seq results imposes new data-handling challenges when it comes to computational analysis. With the increasing employment of machine learning (ML) techniques in biomedical sciences, databases that could provide curated data sets treated with state-of-the-art approaches already adapted to ML protocols, become essential for testing new algorithms. In this study, we present the Benchmarking of ARtificial intelligence Research: Curated RNA-seq Database (BARRA:CuRDa). BARRA:CuRDa was built exclusively for cancer research and is composed of 17 handpicked RNA-seq data sets for Homo sapiens that were gathered from the Gene Expression Omnibus, using rigorous filtering criteria. All data sets were individually submitted to sample quality analysis, removal of low-quality bases and artifacts from the experimental process, removal of ribosomal RNA, and estimation of transcript-level abundance. Moreover, all data sets were tested using standard approaches in the field, which allows them to be used as benchmark to new ML approaches. A feature selection analysis was also performed on each data set to investigate the biological accuracy of basic techniques. Results include genes already related to their specific tumoral tissue a large amount of long noncoding RNA and pseudogenes. BARRA:CuRDa is available at http://sbcb.inf.ufrgs.br/barracurda.
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Affiliation(s)
- Bruno César Feltes
- Institute of Informatics, Department of Theoretical Computer Science, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.,Institute of Biosciences, Department of Biophysics, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Joice De Faria Poloni
- Institute of Informatics, Department of Theoretical Computer Science, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.,EMBRAPA Agroenergy, Distrito Federal, Brasília, Brazil
| | - Márcio Dorn
- Institute of Informatics, Department of Theoretical Computer Science, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.,Center of Biotechnology, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.,National Institute of Science and Technology, Forensic Science, Porto Alegre, Brazil
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50
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Badimon L, Robinson EL, Jusic A, Carpusca I, deWindt LJ, Emanueli C, Ferdinandy P, Gu W, Gyöngyösi M, Hackl M, Karaduzovic-Hadziabdic K, Lustrek M, Martelli F, Nham E, Potočnjak I, Satagopam V, Schneider R, Thum T, Devaux Y. Cardiovascular RNA markers and artificial intelligence may improve COVID-19 outcome: a position paper from the EU-CardioRNA COST Action CA17129. Cardiovasc Res 2021; 117:1823-1840. [PMID: 33839767 PMCID: PMC8083253 DOI: 10.1093/cvr/cvab094] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 04/08/2021] [Indexed: 02/06/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has been as unprecedented as unexpected, affecting more than 105 million people worldwide as of 8 February 2020 and causing more than 2.3 million deaths according to the World Health Organization (WHO). Not only affecting the lungs but also provoking acute respiratory distress, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is able to infect multiple cell types including cardiac and vascular cells. Hence a significant proportion of infected patients develop cardiac events, such as arrhythmias and heart failure. Patients with cardiovascular comorbidities are at highest risk of cardiac death. To face the pandemic and limit its burden, health authorities have launched several fast-track calls for research projects aiming to develop rapid strategies to combat the disease, as well as longer-term projects to prepare for the future. Biomarkers have the possibility to aid in clinical decision-making and tailoring healthcare in order to improve patient quality of life. The biomarker potential of circulating RNAs has been recognized in several disease conditions, including cardiovascular disease. RNA biomarkers may be useful in the current COVID-19 situation. The discovery, validation, and marketing of novel biomarkers, including RNA biomarkers, require multi-centre studies by large and interdisciplinary collaborative networks, involving both the academia and the industry. Here, members of the EU-CardioRNA COST Action CA17129 summarize the current knowledge about the strain that COVID-19 places on the cardiovascular system and discuss how RNA biomarkers can aid to limit this burden. They present the benefits and challenges of the discovery of novel RNA biomarkers, the need for networking efforts, and the added value of artificial intelligence to achieve reliable advances.
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Affiliation(s)
- Lina Badimon
- Cardiovascular Science Program-ICCC, IR-Hospital de la Santa Creu i Santa Pau, Ciber CV, Autonomous University of Barcelona, Barcelona, Spain
| | - Emma L Robinson
- Department of Cardiology, School for Cardiovascular Diseases, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
- Division of Cardiology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Amela Jusic
- Cardiovascular Research Unit, Department of Population Health, Luxembourg Institute of Health, 1A-B rue Edison, L-1445 Strassen, Luxembourg
| | - Irina Carpusca
- Cardiovascular Research Unit, Department of Population Health, Luxembourg Institute of Health, 1A-B rue Edison, L-1445 Strassen, Luxembourg
| | - Leon J deWindt
- Department of Molecular Genetics, Faculty of Science and Engineering, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Costanza Emanueli
- National Heart & Lung Institute, Faculty of Medicine, Imperial College London, London, UK
| | - Péter Ferdinandy
- Cardiometabolic Research Group and MTA-SE System Pharmacology Research Group, Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest,Hungary
- Pharmahungary Group, Szeged, Hungary
| | - Wei Gu
- Luxembourg Center for Systems Biomedicine, University of Luxembourg, Esch sur Alzette, Luxembourg
| | - Mariann Gyöngyösi
- Department of Cardiology, Medical University of Vienna, Vienna, Austria
| | | | | | - Mitja Lustrek
- Department of Intelligent Systems, Jozef Stefan Institute, Ljubljana, Slovenia
| | - Fabio Martelli
- Molecular Cardiology Laboratory, IRCCS Policlinico San Donato, San Donato Milanese, Milan 20097, Italy
| | - Eric Nham
- University of Zagreb School of Medicine, Zagreb, Croatia
| | - Ines Potočnjak
- Institute for Clinical Medical Research and Education, University Hospital Centre Sisters of Charity, Zagreb, Croatia
| | - Venkata Satagopam
- Luxembourg Center for Systems Biomedicine, University of Luxembourg, Esch sur Alzette, Luxembourg
| | - Reinhard Schneider
- Luxembourg Center for Systems Biomedicine, University of Luxembourg, Esch sur Alzette, Luxembourg
| | - Thomas Thum
- Institute of Molecular and Translational Therapeutic Strategies (IMTTS), Fraunhofer Institute for Toxicology and Experimental Medicine, Hannover,Germany
- REBIRTH Center for Translational Regenerative Medicine, Hannover Medical School, Hannover, Germany
| | - Yvan Devaux
- Cardiovascular Research Unit, Department of Population Health, Luxembourg Institute of Health, 1A-B rue Edison, L-1445 Strassen, Luxembourg
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