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Estrada LD, Ağaç Çobanoğlu D, Wise A, Maples RW, Çobanoğlu MC, Farrar JD. Adrenergic signaling controls early transcriptional programs during CD8+ T cell responses to viral infection. PLoS One 2022; 17:e0272017. [PMID: 35944008 PMCID: PMC9362915 DOI: 10.1371/journal.pone.0272017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 07/11/2022] [Indexed: 11/27/2022] Open
Abstract
Norepinephrine is a key sympathetic neurotransmitter, which acts to suppress CD8 + T cell cytokine secretion and lytic activity by signaling through the β2-adrenergic receptor (ADRB2). Although ADRB2 signaling is considered generally immunosuppressive, its role in regulating the differentiation of effector T cells in response to infection has not been investigated. Using an adoptive transfer approach, we compared the expansion and differentiation of wild type (WT) to Adrb2-/- CD8 + T cells throughout the primary response to vesicular stomatitis virus (VSV) infection in vivo. We measured the dynamic changes in transcriptome profiles of antigen-specific CD8 + T cells as they responded to VSV. Within the first 7 days of infection, WT cells out-paced the expansion of Adrb2-/- cells, which correlated with reduced expression of IL-2 and the IL-2Rα in the absence of ADRB2. RNASeq analysis identified over 300 differentially expressed genes that were both temporally regulated following infection and selectively regulated in WT vs Adrb2-/- cells. These genes contributed to major transcriptional pathways including cytokine receptor activation, signaling in cancer, immune deficiency, and neurotransmitter pathways. By parsing genes within groups that were either induced or repressed over time in response to infection, we identified three main branches of genes that were differentially regulated by the ADRB2. These gene sets were predicted to be regulated by specific transcription factors involved in effector T cell development, such as Tbx21 and Eomes. Collectively, these data demonstrate a significant role for ADRB2 signaling in regulating key transcriptional pathways during CD8 + T cells responses to infection that may dramatically impact their functional capabilities and downstream memory cell development.
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Affiliation(s)
- Leonardo D. Estrada
- Department of Immunology, UT Southwestern Medical Center, Dallas, TX, United States of America
| | - Didem Ağaç Çobanoğlu
- Department of Immunology, UT Southwestern Medical Center, Dallas, TX, United States of America
| | - Aaron Wise
- Encodia Inc., San Diego, CA, United States of America
| | - Robert W. Maples
- Department of Immunology, UT Southwestern Medical Center, Dallas, TX, United States of America
| | - Murat Can Çobanoğlu
- Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, United States of America
| | - J. David Farrar
- Department of Immunology, UT Southwestern Medical Center, Dallas, TX, United States of America
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2
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Zhou Y, Jia E, Shi H, Liu Z, Sheng Y, Pan M, Tu J, Ge Q, Lu Z. Prediction of Time-Series Transcriptomic Gene Expression Based on Long Short-Term Memory with Empirical Mode Decomposition. Int J Mol Sci 2022; 23:ijms23147532. [PMID: 35886880 PMCID: PMC9322773 DOI: 10.3390/ijms23147532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/03/2022] [Accepted: 07/04/2022] [Indexed: 02/01/2023] Open
Abstract
RNA degradation can significantly affect the results of gene expression profiling, with subsequent analysis failing to faithfully represent the initial gene expression level. It is urgent to have an artificial intelligence approach to better utilize the limited data to obtain meaningful and reliable analysis results in the case of data with missing destination time. In this study, we propose a method based on the signal decomposition technique and deep learning, named Multi-LSTM. It is divided into two main modules: One decomposes the collected gene expression data by an empirical mode decomposition (EMD) algorithm to obtain a series of sub-modules with different frequencies to improve data stability and reduce modeling complexity. The other is based on long short-term memory (LSTM) as the core predictor, aiming to deeply explore the temporal nonlinear relationships embedded in the sub-modules. Finally, the prediction results of sub-modules are reconstructed to obtain the final prediction results of time-series transcriptomic gene expression. The results show that EMD can efficiently reduce the nonlinearity of the original data, which provides reliable theoretical support to reduce the complexity and improve the robustness of LSTM models. Overall, the decomposition-combination prediction framework can effectively predict gene expression levels at unknown time points.
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Affiliation(s)
- Ying Zhou
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, China; (Y.Z.); (E.J.); (H.S.); (Z.L.); (Y.S.); (J.T.); (Z.L.)
| | - Erteng Jia
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, China; (Y.Z.); (E.J.); (H.S.); (Z.L.); (Y.S.); (J.T.); (Z.L.)
| | - Huajuan Shi
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, China; (Y.Z.); (E.J.); (H.S.); (Z.L.); (Y.S.); (J.T.); (Z.L.)
| | - Zhiyu Liu
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, China; (Y.Z.); (E.J.); (H.S.); (Z.L.); (Y.S.); (J.T.); (Z.L.)
| | - Yuqi Sheng
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, China; (Y.Z.); (E.J.); (H.S.); (Z.L.); (Y.S.); (J.T.); (Z.L.)
| | - Min Pan
- School of Medicine, Southeast University, Nanjing 210097, China;
| | - Jing Tu
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, China; (Y.Z.); (E.J.); (H.S.); (Z.L.); (Y.S.); (J.T.); (Z.L.)
| | - Qinyu Ge
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, China; (Y.Z.); (E.J.); (H.S.); (Z.L.); (Y.S.); (J.T.); (Z.L.)
- Correspondence:
| | - Zuhong Lu
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, China; (Y.Z.); (E.J.); (H.S.); (Z.L.); (Y.S.); (J.T.); (Z.L.)
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3
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Nguyen Y, Nettleton D. rmRNAseq: differential expression analysis for repeated-measures RNA-seq data. Bioinformatics 2021; 36:4432-4439. [PMID: 32449749 DOI: 10.1093/bioinformatics/btaa525] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2019] [Revised: 04/28/2020] [Accepted: 05/19/2020] [Indexed: 12/27/2022] Open
Abstract
MOTIVATION With the reduction in price of next-generation sequencing technologies, gene expression profiling using RNA-seq has increased the scope of sequencing experiments to include more complex designs, such as designs involving repeated measures. In such designs, RNA samples are extracted from each experimental unit at multiple time points. The read counts that result from RNA sequencing of the samples extracted from the same experimental unit tend to be temporally correlated. Although there are many methods for RNA-seq differential expression analysis, existing methods do not properly account for within-unit correlations that arise in repeated-measures designs. RESULTS We address this shortcoming by using normalized log-transformed counts and associated precision weights in a general linear model pipeline with continuous autoregressive structure to account for the correlation among observations within each experimental unit. We then utilize parametric bootstrap to conduct differential expression inference. Simulation studies show the advantages of our method over alternatives that do not account for the correlation among observations within experimental units. AVAILABILITY AND IMPLEMENTATION We provide an R package rmRNAseq implementing our proposed method (function TC_CAR1) at https://cran.r-project.org/web/packages/rmRNAseq/index.html. Reproducible R codes for data analysis and simulation are available at https://github.com/ntyet/rmRNAseq/tree/master/simulation.
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Affiliation(s)
- Yet Nguyen
- Department of Mathematics and Statistics, Old Dominion University, Norfolk, VA 23529, USA
| | - Dan Nettleton
- Department of Statistics, Iowa State University, Ames, IA 50011, USA
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4
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Majumder A, Sarkar M, Sharma P. A Composite Mode Differential Gene Regulatory Architecture based on Temporal Expression Profiles. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:1785-1793. [PMID: 29993888 DOI: 10.1109/tcbb.2018.2828418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Exploring the complex interactive mechanism in a Gene Regulatory Network (GRN) developed using transcriptome data obtained from standard microarray and/or RNA-seq experiments helps us to understand the triggering factors in cancer research. The Transcription Factor (TF) genes generate protein complexes which affect the transcription of various target genes. However, considering the mode of regulation in a time frame such transcriptional activities are dependent on some specific activation time points only. It is also crucial to check whether the regulating capabilities are uniform across varied stages, especially when periodicity is a big issue. In this context, we propose an algorithm called RIFT which helps to monitor the temporal differential regulatory pattern of a Differentially Expressed (DE) target gene either by a TF gene or a group of TF genes from a large time series (TS) data. We have tested our algorithm on HeLa cell cycle data and compared the result with its most advanced state of the art counterpart proposed so far. As our algorithm yields up stringent mode and target specific significant valid TF genes for a DE gene, we can expect to have new forms of genetic interactions.
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5
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Spies D, Renz PF, Beyer TA, Ciaudo C. Comparative analysis of differential gene expression tools for RNA sequencing time course data. Brief Bioinform 2019; 20:288-298. [PMID: 29028903 PMCID: PMC6357553 DOI: 10.1093/bib/bbx115] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2017] [Indexed: 02/05/2023] Open
Abstract
RNA sequencing (RNA-seq) has become a standard procedure to investigate transcriptional changes between conditions and is routinely used in research and clinics. While standard differential expression (DE) analysis between two conditions has been extensively studied, and improved over the past decades, RNA-seq time course (TC) DE analysis algorithms are still in their early stages. In this study, we compare, for the first time, existing TC RNA-seq tools on an extensive simulation data set and validated the best performing tools on published data. Surprisingly, TC tools were outperformed by the classical pairwise comparison approach on short time series (<8 time points) in terms of overall performance and robustness to noise, mostly because of high number of false positives, with the exception of ImpulseDE2. Overlapping of candidate lists between tools improved this shortcoming, as the majority of false-positive, but not true-positive, candidates were unique for each method. On longer time series, pairwise approach was less efficient on the overall performance compared with splineTC and maSigPro, which did not identify any false-positive candidate.
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Affiliation(s)
- Daniel Spies
- Swiss Federal Institute of Technology Zurich, Department of Biology, IMHS, Zurich, Switzerland.,Life Science Zurich Graduate School, Molecular Life Science program, University of Zürich, Switzerland
| | - Peter F Renz
- Swiss Federal Institute of Technology Zurich, Department of Biology, IMHS, Zurich, Switzerland.,Life Science Zurich Graduate School, Molecular Life Science program, University of Zürich, Switzerland
| | - Tobias A Beyer
- Swiss Federal Institute of Technology Zurich, Department of Biology, IMHS, Zurich, Switzerland
| | - Constance Ciaudo
- Swiss Federal Institute of Technology Zurich, Department of Biology, IMHS, Zurich, Switzerland
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6
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Bacher R, Leng N, Chu LF, Ni Z, Thomson JA, Kendziorski C, Stewart R. Trendy: segmented regression analysis of expression dynamics in high-throughput ordered profiling experiments. BMC Bioinformatics 2018; 19:380. [PMID: 30326833 PMCID: PMC6192113 DOI: 10.1186/s12859-018-2405-x] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Accepted: 10/01/2018] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND High-throughput expression profiling experiments with ordered conditions (e.g. time-course or spatial-course) are becoming more common for studying detailed differentiation processes or spatial patterns. Identifying dynamic changes at both the individual gene and whole transcriptome level can provide important insights about genes, pathways, and critical time points. RESULTS We present an R package, Trendy, which utilizes segmented regression models to simultaneously characterize each gene's expression pattern and summarize overall dynamic activity in ordered condition experiments. For each gene, Trendy finds the optimal segmented regression model and provides the location and direction of dynamic changes in expression. We demonstrate the utility of Trendy to provide biologically relevant results on both microarray and RNA-sequencing (RNA-seq) datasets. CONCLUSIONS Trendy is a flexible R package which characterizes gene-specific expression patterns and summarizes changes of global dynamics over ordered conditions. Trendy is freely available on Bioconductor with a full vignette at https://bioconductor.org/packages/release/bioc/html/Trendy.html .
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Affiliation(s)
- Rhonda Bacher
- Department of Biostatistics, University of Florida, Gainesville, FL, USA.
| | - Ning Leng
- Morgridge Institute for Research, Madison, WI, USA
| | - Li-Fang Chu
- Morgridge Institute for Research, Madison, WI, USA
| | - Zijian Ni
- Department of Statistics, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Christina Kendziorski
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| | - Ron Stewart
- Morgridge Institute for Research, Madison, WI, USA.
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7
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Mishra B, Sun Y, Howton TC, Kumar N, Mukhtar MS. Dynamic modeling of transcriptional gene regulatory network uncovers distinct pathways during the onset of Arabidopsis leaf senescence. NPJ Syst Biol Appl 2018; 4:35. [PMID: 30181903 PMCID: PMC6119185 DOI: 10.1038/s41540-018-0071-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2017] [Revised: 08/10/2018] [Accepted: 08/14/2018] [Indexed: 11/08/2022] Open
Abstract
Age-dependent senescence is a multifaceted and highly coordinated developmental phase in the life of plants that is manifested with genetic, biochemical and phenotypic continuum. Thus, elucidating the dynamic network modeling and simulation of molecular events, in particular gene regulatory network during the onset of senescence is essential. Here, we constructed a computational pipeline that integrates senescence-related co-expression networks with transcription factor (TF)-promoter relationships and microRNA (miR)-target interactions. Network structural and functional analyses revealed important nodes within each module of these co-expression networks. Subsequently, we inferred significant dynamic transcriptional regulatory models in leaf senescence using time-course gene expression datasets. Dynamic simulations and predictive network perturbation analyses followed by experimental dataset illustrated the kinetic relationships among TFs and their downstream targets. In conclusion, our network science framework discovers cohorts of TFs and their paths with previously unrecognized roles in leaf senescence and provides a comprehensive landscape of dynamic transcriptional circuitry.
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Affiliation(s)
- Bharat Mishra
- Department of Biology, University of Alabama at Birmingham, 1300 University Blvd., Birmingham, AL 35294 USA
| | - Yali Sun
- Department of Biology, University of Alabama at Birmingham, 1300 University Blvd., Birmingham, AL 35294 USA
| | - TC Howton
- Department of Biology, University of Alabama at Birmingham, 1300 University Blvd., Birmingham, AL 35294 USA
| | - Nilesh Kumar
- Department of Biology, University of Alabama at Birmingham, 1300 University Blvd., Birmingham, AL 35294 USA
| | - M. Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, 1300 University Blvd., Birmingham, AL 35294 USA
- Nutrition Obesity Research Center, University of Alabama at Birmingham, 1675 University Blvd., Birmingham, AL 35294 USA
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8
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Stein-O’Brien G, Kagohara LT, Li S, Thakar M, Ranaweera R, Ozawa H, Cheng H, Considine M, Schmitz S, Favorov AV, Danilova LV, Califano JA, Izumchenko E, Gaykalova DA, Chung CH, Fertig EJ. Integrated time course omics analysis distinguishes immediate therapeutic response from acquired resistance. Genome Med 2018; 10:37. [PMID: 29792227 PMCID: PMC5966898 DOI: 10.1186/s13073-018-0545-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Accepted: 05/01/2018] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Targeted therapies specifically act by blocking the activity of proteins that are encoded by genes critical for tumorigenesis. However, most cancers acquire resistance and long-term disease remission is rarely observed. Understanding the time course of molecular changes responsible for the development of acquired resistance could enable optimization of patients' treatment options. Clinically, acquired therapeutic resistance can only be studied at a single time point in resistant tumors. METHODS To determine the dynamics of these molecular changes, we obtained high throughput omics data (RNA-sequencing and DNA methylation) weekly during the development of cetuximab resistance in a head and neck cancer in vitro model. The CoGAPS unsupervised algorithm was used to determine the dynamics of the molecular changes associated with resistance during the time course of resistance development. RESULTS CoGAPS was used to quantify the evolving transcriptional and epigenetic changes. Applying a PatternMarker statistic to the results from CoGAPS enabled novel heatmap-based visualization of the dynamics in these time course omics data. We demonstrate that transcriptional changes result from immediate therapeutic response or resistance, whereas epigenetic alterations only occur with resistance. Integrated analysis demonstrates delayed onset of changes in DNA methylation relative to transcription, suggesting that resistance is stabilized epigenetically. CONCLUSIONS Genes with epigenetic alterations associated with resistance that have concordant expression changes are hypothesized to stabilize the resistant phenotype. These genes include FGFR1, which was associated with EGFR inhibitors resistance previously. Thus, integrated omics analysis distinguishes the timing of molecular drivers of resistance. This understanding of the time course progression of molecular changes in acquired resistance is important for the development of alternative treatment strategies that would introduce appropriate selection of new drugs to treat cancer before the resistant phenotype develops.
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Affiliation(s)
- Genevieve Stein-O’Brien
- Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD USA
| | - Luciane T. Kagohara
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD USA
| | - Sijia Li
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD USA
| | - Manjusha Thakar
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD USA
| | - Ruchira Ranaweera
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD USA
- Department of Head and Neck-Endocrine Oncology, Moffitt Cancer Center, Tampa, FL USA
| | - Hiroyuki Ozawa
- Department of Otorhinolaryngology-Head and Neck Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Haixia Cheng
- Department of Surgery - Otolaryngology–Head and Neck Surgery, University of Utah, |Salt Lake City, UT USA
| | - Michael Considine
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD USA
| | - Sandra Schmitz
- Head and Neck Surgery Unit, St Luc University Hospital, Brussels, Belgium
| | - Alexander V. Favorov
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD USA
- Laboratory of Systems Biology and Computational Genetics, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia
| | - Ludmila V. Danilova
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD USA
- Laboratory of Systems Biology and Computational Genetics, Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia
| | - Joseph A. Califano
- Department of Surgery, UC San Diego Moores Cancer Center, La Jolla, CA USA
| | - Evgeny Izumchenko
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University, Baltimore, MD USA
| | - Daria A. Gaykalova
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University, Baltimore, MD USA
| | - Christine H. Chung
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD USA
- Department of Head and Neck-Endocrine Oncology, Moffitt Cancer Center, Tampa, FL USA
| | - Elana J. Fertig
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD USA
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9
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Insight into Genes Regulating Postharvest Aflatoxin Contamination of Tetraploid Peanut from Transcriptional Profiling. Genetics 2018; 209:143-156. [PMID: 29545468 DOI: 10.1534/genetics.118.300478] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2017] [Accepted: 03/07/2018] [Indexed: 11/18/2022] Open
Abstract
Postharvest aflatoxin contamination is a challenging issue that affects peanut quality. Aflatoxin is produced by fungi belonging to the Aspergilli group, and is known as an acutely toxic, carcinogenic, and immune-suppressing class of mycotoxins. Evidence for several host genetic factors that may impact aflatoxin contamination has been reported, e.g., genes for lipoxygenase (PnLOX1 and PnLOX2/PnLOX3 that showed either positive or negative regulation with Aspergillus infection), reactive oxygen species, and WRKY (highly associated with or differentially expressed upon infection of maize with Aspergillus flavus); however, their roles remain unclear. Therefore, we conducted an RNA-sequencing experiment to differentiate gene response to the infection by A. flavus between resistant (ICG 1471) and susceptible (Florida-07) cultivated peanut genotypes. The gene expression profiling analysis was designed to reveal differentially expressed genes in response to the infection (infected vs. mock-treated seeds). In addition, the differential expression of the fungal genes was profiled. The study revealed the complexity of the interaction between the fungus and peanut seeds as the expression of a large number of genes was altered, including some in the process of plant defense to aflatoxin accumulation. Analysis of the experimental data with "keggseq," a novel designed tool for Kyoto Encyclopedia of Genes and Genomes enrichment analysis, showed the importance of α-linolenic acid metabolism, protein processing in the endoplasmic reticulum, spliceosome, and carbon fixation and metabolism pathways in conditioning resistance to aflatoxin accumulation. In addition, coexpression network analysis was carried out to reveal the correlation of gene expression among peanut and fungal genes. The results showed the importance of WRKY, toll/Interleukin1 receptor-nucleotide binding site leucine-rich repeat (TIR-NBS-LRR), ethylene, and heat shock proteins in the resistance mechanism.
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10
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Straube J, Huang BE, Cao KAL. DynOmics to identify delays and co-expression patterns across time course experiments. Sci Rep 2017; 7:40131. [PMID: 28065937 PMCID: PMC5220332 DOI: 10.1038/srep40131] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Accepted: 12/02/2016] [Indexed: 12/16/2022] Open
Abstract
Dynamic changes in biological systems can be captured by measuring molecular expression from different levels (e.g., genes and proteins) across time. Integration of such data aims to identify molecules that show similar expression changes over time; such molecules may be co-regulated and thus involved in similar biological processes. Combining data sources presents a systematic approach to study molecular behaviour. It can compensate for missing data in one source, and can reduce false positives when multiple sources highlight the same pathways. However, integrative approaches must accommodate the challenges inherent in ‘omics’ data, including high-dimensionality, noise, and timing differences in expression. As current methods for identification of co-expression cannot cope with this level of complexity, we developed a novel algorithm called DynOmics. DynOmics is based on the fast Fourier transform, from which the difference in expression initiation between trajectories can be estimated. This delay can then be used to realign the trajectories and identify those which show a high degree of correlation. Through extensive simulations, we demonstrate that DynOmics is efficient and accurate compared to existing approaches. We consider two case studies highlighting its application, identifying regulatory relationships across ‘omics’ data within an organism and for comparative gene expression analysis across organisms.
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Affiliation(s)
- Jasmin Straube
- QFAB@QCIF Bioinformatics, Institute for Molecular Biosciences, The University of Queensland, Queensland Bioscience Precinct, St Lucia, QLD, Australia.,The University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, QLD, Australia
| | - Bevan Emma Huang
- Janssen Research &Development, LLC, Discovery Sciences, Menlo Park, USA
| | - Kim-Anh Lê Cao
- The University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, QLD, Australia
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11
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Spies D, Ciaudo C. Dynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream Analysis. Comput Struct Biotechnol J 2015; 13:469-77. [PMID: 26430493 PMCID: PMC4564389 DOI: 10.1016/j.csbj.2015.08.004] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2015] [Revised: 08/05/2015] [Accepted: 08/07/2015] [Indexed: 12/17/2022] Open
Abstract
Analysis of gene expression has contributed to a plethora of biological and medical research studies. Microarrays have been intensively used for the profiling of gene expression during diverse developmental processes, treatments and diseases. New massively parallel sequencing methods, often named as RNA-sequencing (RNA-seq) are extensively improving our understanding of gene regulation and signaling networks. Computational methods developed originally for microarrays analysis can now be optimized and applied to genome-wide studies in order to have access to a better comprehension of the whole transcriptome. This review addresses current challenges on RNA-seq analysis and specifically focuses on new bioinformatics tools developed for time series experiments. Furthermore, possible improvements in analysis, data integration as well as future applications of differential expression analysis are discussed.
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Affiliation(s)
- Daniel Spies
- Swiss Federal Institute of Technology Zurich, Department of Biology, Institute of Molecular Health Sciences, Zurich, Otto-Stern Weg 7, 8093 Zurich, Switzerland
- Life Science Zurich Graduate School, Molecular Life Science Program, University of Zurich, Institute of Molecular Life Sciences, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| | - Constance Ciaudo
- Swiss Federal Institute of Technology Zurich, Department of Biology, Institute of Molecular Health Sciences, Zurich, Otto-Stern Weg 7, 8093 Zurich, Switzerland
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