Studying and modelling dynamic biological processes using time-series gene expression data (original) (raw)

• Barrett, T. et al. NCBI GEO: archive for functional genomics data sets-10 years on. Nucleic Acids Res. 39, D1005–D1010 (2011).
  Article CAS PubMed Google Scholar
• Gasch, A. P. et al. Genomic expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell 11, 4241–4257 (2000). An extensive study of the yeast response to various stresses, which revealed a large set of general environmental stress response genes.
  Article CAS PubMed PubMed Central Google Scholar
• Chechik, G. et al. Activity motifs reveal principles of timing in transcriptional control of the yeast metabolic network. Nature Biotech. 26, 1251–1259 (2008).
  Article CAS Google Scholar
• Amit, I. et al. A module of negative feedback regulators defines growth factor signaling. Nature Genet. 39, 503–512 (2007). This study used a clever design strategy to sample and integrate temporal immune response data. It identified several cascades that are activated as part of mammalian immune response.
  Article CAS PubMed Google Scholar
• Zinman, G. et al. Large scale comparison of innate responses to viral and bacterial pathogens in mouse and macaque. PLoS ONE 6, e22401 (2011).
  Article CAS PubMed PubMed Central Google Scholar
• Tang, F., Lao, K. & Surani, M. A. Development and applications of single-cell transcriptome analysis. Nature Methods 8, S6–S11 (2011).
  Article CAS PubMed PubMed Central Google Scholar
• Locke, J. C. W. & Elowitz, M. B. Using movies to analyse gene circuit dynamics in single cells. Nature Rev. Microbiol. 7, 383–392 (2009).
  Article CAS Google Scholar
• Amit, I. et al. Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses. Science 326, 257–263 (2009).
  Article CAS PubMed PubMed Central Google Scholar
• Huang, Y. et al. Temporal dynamics of host molecular responses differentiate symptomatic and asymptomatic influenza A infection. PLoS Genet. 7, e1002234 (2011). An experimental and computational analysis of influenza infection in human volunteers that identified dynamic expression patterns that discriminate symptomatic and asymptomatic reactions.
  Article CAS PubMed PubMed Central Google Scholar
• Arbeitman, M. N. et al. Gene expression during the life cycle of Drosophila melanogaster. Science 297, 2270–2275 (2002). One of the first developmental studies using time-series expression data. It identified several cascades of genes that are expressed during fly development.
  Article CAS PubMed Google Scholar
• Mathavan, S. et al. Transcriptome analysis of zebrafish embryogenesis using microarrays. PLoS Genet. 1, 260–276 (2005).
  Article CAS PubMed Google Scholar
• Gerstein, M. B. et al. Integrative analysis of the Caenorhabditis elegans genome by the modENCODE project. Science 330, 1775–1787 (2010).
  Article CAS PubMed PubMed Central Google Scholar
• Roy, S. et al. Identification of functional elements and regulatory circuits by Drosophila modENCODE. Science 330, 1787–1797 (2010). A comprehensive study of fly development that collected and integrated several types of temporal and static omics data.
  Article CAS PubMed PubMed Central Google Scholar
• Spellman, P. T. et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell 9, 3273–3297 (1998). Among the first studies of cyclic responses using time-series experiments. It identified a large set of yeast cycling genes and it served as the basis for several computational methods that have been developed to study such data.
  Article CAS PubMed PubMed Central Google Scholar
• Menges, M., Hennig, L., Gruissem, W. & Murray, J. A. H. Cell cycle-regulated gene expression in Arabidopsis. J. Biol. Chem. 277, 41987–42002 (2002).
  Article CAS PubMed Google Scholar
• Whitfield, M. L. et al. Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol. Biol. Cell 13, 1977–2000 (2002).
  Article CAS PubMed PubMed Central Google Scholar
• Rustici, G. et al. Periodic gene expression program of the fission yeast cell cycle. Nature Genet. 36, 809–817 (2004).
  Article CAS PubMed Google Scholar
• Bar-Joseph, Z. et al. Genome-wide transcriptional analysis of the human cell cycle identifies genes differentially regulated in normal and cancer cells. Proc. Natl Acad. Sci. USA 105, 955–960 (2008).
  Article CAS PubMed PubMed Central Google Scholar
• McDonald, M. J. & Rosbash, M. Microarray analysis and organization of circadian gene expression in Drosophila. Cell 107, 567–578 (2001).
  Article CAS PubMed Google Scholar
• Schaffer, R. et al. Microarray analysis of diurnal and circadian-regulated genes in Arabidopsis. Plant Cell 13, 113–123 (2001).
  Article CAS PubMed PubMed Central Google Scholar
• Storch, K.-F. et al. Extensive and divergent circadian gene expression in liver and heart. Nature 417, 78–83 (2002).
  Article CAS PubMed Google Scholar
• Pauli, A. et al. Systematic identification of long noncoding RNAs expressed during zebrafish embryogenesis. Genome Res. 22, 577–591 (2012).
  Article CAS PubMed PubMed Central Google Scholar
• Otto, T. D. et al. New insights into the blood-stage transcriptome of Plasmodium falciparum using RNA-Seq. Mol. Microbiol. 76, 12–24 (2010).
  Article CAS PubMed PubMed Central Google Scholar
• Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotech. 28, 511–515 (2010).
  Article CAS Google Scholar
• Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nature Biotech. 29, 644–652 (2011).
  Article CAS Google Scholar
• Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M. & Gilad, Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 18, 1509–1517 (2008).
  Article CAS PubMed PubMed Central Google Scholar
• Kelley, D. R., Schatz, M. C. & Salzberg, S. L. Quake: quality-aware detection and correction of sequencing errors. Genome Biol. 11, R116 (2010).
  Article CAS PubMed PubMed Central Google Scholar
• Shalem, O. et al. Transient transcriptional responses to stress are generated by opposing effects of mRNA production and degradation. Mol. Syst. Biol. 4, 223 (2008).
  Article CAS PubMed PubMed Central Google Scholar
• Bolstad, B. M., Irizarry, R. A., Astrand, M. & Speed, T. P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185–193 (2003).
  Article CAS PubMed Google Scholar
• Li, C. & Hung Wong, W. Model-based analysis of oligonucleotide arrays: model validation, design issues and standard error application. Genome Biol. 2, RESEARCH0032 (2001).
  CAS PubMed PubMed Central Google Scholar
• Ernst, J. et al. IL-3 and oncogenic Abl regulate the myeloblast transcriptome by altering mRNA stability. PLoS ONE 4, e7469 (2009).
  Article CAS PubMed PubMed Central Google Scholar
• Bar-Joseph, Z., Gerber, G., Simon, I., Gifford, D. K. & Jaakkola, T. S. Comparing the continuous representation of time-series expression profiles to identify differentially expressed genes. Proc. Natl Acad. Sci. USA 100, 10146–10151 (2003).
  Article CAS PubMed PubMed Central Google Scholar
• Bar-Joseph, Z., Gerber, G. K., Gifford, D. K., Jaakkola, T. S. & Simon, I. Continuous representations of time-series gene expression data. J. Comput. Biol. 10, 341–356 (2003).
  Article CAS PubMed Google Scholar
• Kaminski, N. & Bar-Joseph, Z. A patient-gene model for temporal expression profiles in clinical studies. J. Comput. Biol. 14, 324–338 (2007).
  Article CAS PubMed Google Scholar
• Calvano, S. E. et al. A network-based analysis of systemic inflammation in humans. Nature 437, 1032–1037 (2005).
  Article CAS PubMed Google Scholar
• Lu, R. et al. Systems-level dynamic analyses of fate change in murine embryonic stem cells. Nature 462, 358–362 (2009). This study measured several types of temporal high-throughput data sets in stem cell differentiation. It determined that correlation between dynamic protein and mRNA expression levels is lower than was previously thought.
  Article CAS PubMed PubMed Central Google Scholar
• Nau, G. J. et al. Human macrophage activation programs induced by bacterial pathogens. Proc. Natl Acad. Sci. USA 99, 1503–1508 (2002).
  Article CAS PubMed PubMed Central Google Scholar
• Shapira, S. D. et al. A physical and regulatory map of host-influenza interactions reveals pathways in H1N1 infection. Cell 139, 1255–1267 (2009).
  Article PubMed PubMed Central Google Scholar
• Tusher, V. G., Tibshirani, R. & Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl Acad. Sci. USA 98, 5116–5121 (2001).
  Article CAS PubMed PubMed Central Google Scholar
• Storey, J. D., Xiao, W., Leek, J. T., Tompkins, R. G. & Davis, R. W. Significance analysis of time course microarray experiments. Proc. Natl Acad. Sci. USA 102, 12837–12842 (2005).
  Article CAS PubMed PubMed Central Google Scholar
• Aryee, M. J., Gutiérrez-Pabello, J. A., Kramnik, I., Maiti, T. & Quackenbush, J. An improved empirical bayes approach to estimating differential gene expression in microarray time-course data: BETR (Bayesian Estimation of Temporal Regulation). BMC Bioinformatics 10, 409 (2009).
  Article CAS PubMed PubMed Central Google Scholar
• Eisen, M. B., Spellman, P. T., Brown, P. O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA 95, 14863–14868 (1998).
  Article CAS PubMed PubMed Central Google Scholar
• Ramoni, M. F., Sebastiani, P. & Kohane, I. S. Cluster analysis of gene expression dynamics. Proc. Natl Acad. Sci. USA 99, 9121–9126 (2002).
  Article CAS PubMed PubMed Central Google Scholar
• Schliep, A., Schönhuth, A. & Steinhoff, C. Using hidden Markov models to analyze gene expression time course data. Bioinformatics 19, i255–i263 (2003).
  Article PubMed Google Scholar
• Ernst, J., Nau, G. J. & Bar-Joseph, Z. Clustering short time series gene expression data. Bioinformatics 21, i159–i168 (2005).
  Article CAS PubMed Google Scholar
• Baranzini, S. E. et al. Transcription-based prediction of response to IFNβ using supervised computational methods. PLoS Biol. 3, e2 (2005).
  Article CAS PubMed Google Scholar
• Borgwardt, K. M., Vishwanathan, S. V. N. & Kriegel, H.-P. Class prediction from time series gene expression profiles using dynamical systems kernels. Pac. Symp. Biocomput. 2006, 547–558 (2006).
  Google Scholar
• Lin, T., Kaminski, N. & Bar-Joseph, Z. Alignment and classification of time series gene expression in clinical studies. Bioinformatics 24, i147–i155 (2008).
  Article CAS PubMed PubMed Central Google Scholar
• Costa, I. G., Schönhuth, A., Hafemeister, C. & Schliep, A. Constrained mixture estimation for analysis and robust classification of clinical time series. Bioinformatics 25, i6–i14 (2009).
  Article CAS PubMed PubMed Central Google Scholar
• Qian, J., Dolled-Filhart, M., Lin, J., Yu, H. & Gerstein, M. Beyond synexpression relationships: local clustering of time-shifted and inverted gene expression profiles identifies new, biologically relevant interactions. J. Mol. Biol. 314, 1053–1066 (2001).
  Article CAS PubMed Google Scholar
• Mukhopadhyay, N. D. & Chatterjee, S. Causality and pathway search in microarray time series experiment. Bioinformatics 23, 442–449 (2007).
  Article CAS PubMed Google Scholar
• Shi, Y., Mitchell, T. & Bar-Joseph, Z. Inferring pairwise regulatory relationships from multiple time series datasets. Bioinformatics 23, 755–763 (2007).
  Article CAS PubMed Google Scholar
• Zhu, J. et al. Characterizing dynamic changes in the human blood transcriptional network. PLoS Comput. Biol. 6, e1000671 (2010).
  Article CAS PubMed PubMed Central Google Scholar
• The FANTOM Consortium & Riken Omics Science Center. The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line. Nature Genet. 41, 553–562 (2009).
• Ramsey, S. A. et al. Uncovering a macrophage transcriptional program by integrating evidence from motif scanning and expression dynamics. PLoS Comput. Biol. 4, e1000021 (2008).
  Article CAS PubMed PubMed Central Google Scholar
• Davidson, E. H. Emerging properties of animal gene regulatory networks. Nature 468, 911–920 (2010).
  Article CAS PubMed PubMed Central Google Scholar
• Oliveri, P., Tu, Q. & Davidson, E. H. Global regulatory logic for specification of an embryonic cell lineage. Proc. Natl Acad. Sci. USA 105, 5955–5962 (2008).
  Article CAS PubMed PubMed Central Google Scholar
• Simon, I. et al. Serial regulation of transcriptional regulators in the yeast cell cycle. Cell 106, 697–708 (2001).
  Article CAS PubMed Google Scholar
• Ihmels, J., Levy, R. & Barkai, N. Principles of transcriptional control in the metabolic network of Saccharomyces cerevisiae. Nature Biotech. 22, 86–92 (2004).
  Article CAS Google Scholar
• Alon, U. Network motifs: theory and experimental approaches. Nature Rev. Genet. 8, 450–461 (2007).
  Article CAS PubMed Google Scholar
• Zaslaver, A. et al. Just-in-time transcription program in metabolic pathways. Nature Genet. 36, 486–491 (2004).
  Article CAS PubMed Google Scholar
• Kalir, S., Mangan, S. & Alon, U. A coherent feed-forward loop with a SUM input function prolongs flagella expression in Escherichia coli. Mol. Syst. Biol. 1, 2005.0006 (2005).
  Article CAS PubMed PubMed Central Google Scholar
• Yosef, N. & Regev, A. Impulse control: temporal dynamics in gene transcription. Cell 144, 886–896 (2011). A comprehensive review of impulse response in time-series gene expression data. It highlights both basic response patterns and the combination of such patterns in specific response programs.
  Article CAS PubMed PubMed Central Google Scholar
• Chechik, G. & Koller, D. Timing of gene expression responses to environmental changes. J. Comput. Biol. 16, 279–290 (2009).
  Article CAS PubMed Google Scholar
• Braun, E. & Brenner, N. Transient responses and adaptation to steady state in a eukaryotic gene regulation system. Phys. Biol. 1, 67–76 (2004).
  Article CAS PubMed Google Scholar
• Murray, J. I. et al. Diverse and specific gene expression responses to stresses in cultured human cells. Mol. Biol. Cell 15, 2361–2374 (2004).
  Article CAS PubMed PubMed Central Google Scholar
• López-Maury, L., Marguerat, S. & Bähler, J. Tuning gene expression to changing environments: from rapid responses to evolutionary adaptation. Nature Rev. Genet. 9, 583–593 (2008).
  Article CAS PubMed Google Scholar
• Litvak, V. et al. Function of C/EBPδ in a regulatory circuit that discriminates between transient and persistent TLR4-induced signals. Nature Immunol. 10, 437–443 (2009).
  Article CAS Google Scholar
• Basma, H. et al. Differentiation and transplantation of human embryonic stem cell-derived hepatocytes. Gastroenterology 136, 990–999 (2009).
  Article CAS PubMed Google Scholar
• Iliopoulos, D., Hirsch, H. A. & Struhl, K. An epigenetic switch involving NF-κB, Lin28, Let-7 microRNA, and IL6 links inflammation to cell transformation. Cell 139, 693–706 (2009).
  Article CAS PubMed PubMed Central Google Scholar
• Seok, J., Xiao, W., Moldawer, L. L., Davis, R. W. & Covert, M. W. A dynamic network of transcription in LPS-treated human subjects. BMC Syst. Biol. 3, 78 (2009).
  Article CAS PubMed PubMed Central Google Scholar
• Shedden, K. & Cooper, S. Analysis of cell-cycle- specific gene expression in human cells as determined by microarrays and double-thymidine block synchronization. Proc. Natl Acad. Sci. USA 99, 4379–4384 (2002).
  Article CAS PubMed PubMed Central Google Scholar
• Simon, I., Siegfried, Z., Ernst, J. & Bar-Joseph, Z. Combined static and dynamic analysis for determining the quality of time-series expression profiles. Nature Biotech. 23, 1503–1508 (2005).
  Article CAS Google Scholar
• Lu, Y. et al. Combined analysis reveals a core set of cycling genes. Genome Biol. 8, R146 (2007).
  Article CAS PubMed PubMed Central Google Scholar
• Jensen, L. J., Jensen, T. S., de Lichtenberg, U., Brunak, S. & Bork, P. Co-evolution of transcriptional and post-translational cell-cycle regulation. Nature 443, 594–597 (2006). A study of the conservation of cell cycle regulation across species. It determined that temporal complex activation, rather than specific gene expression, is conserved between species.
  Article CAS PubMed Google Scholar
• Eshaghi, M. et al. Deconvolution of chromatin immunoprecipitation-microarray (ChIP-chip) analysis of MBF occupancies reveals the temporal recruitment of Rep2 at the MBF target genes. Eukaryot. Cell 10, 130–141 (2011).
  Article CAS PubMed PubMed Central Google Scholar
• Sandmann, T. et al. A temporal map of transcription factor activity: Mef2 directly regulates target genes at all stages of muscle development. Dev. Cell 10, 797–807 (2006).
  Article CAS PubMed Google Scholar
• Jakobsen, J. S. et al. Temporal ChIP-on-chip reveals Biniou as a universal regulator of the visceral muscle transcriptional network. Genes Dev. 21, 2448–2460 (2007).
  Article CAS PubMed PubMed Central Google Scholar
• Liu, Y.-H. et al. A systematic analysis of Tinman function reveals Eya and JAK-STAT signaling as essential regulators of muscle development. Dev. Cell 16, 280–291 (2009).
  Article CAS PubMed Google Scholar
• Mohn, F. et al. Lineage-specific polycomb targets and de novo DNA methylation define restriction and potential of neuronal progenitors. Mol. Cell 30, 755–766 (2008).
  Article CAS PubMed Google Scholar
• Jayaswal, V., Lutherborrow, M., Ma, D. D. F. & Hwa Yang, Y. Identification of microRNAs with regulatory potential using a matched microRNA-mRNA time-course data. Nucleic Acids Res. 37, e60 (2009).
  Article CAS PubMed PubMed Central Google Scholar
• Luscombe, N. M. et al. Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 431, 308–312 (2004). This was among the first studies to integrate time-series and static interaction data to determine temporal activity of transcription factors.
  Article CAS PubMed Google Scholar
• Liao, J. C. et al. Network component analysis: reconstruction of regulatory signals in biological systems. Proc. Natl Acad. Sci. USA 100, 15522–15527 (2003).
  Article CAS PubMed PubMed Central Google Scholar
• Bansal, M., Della Gatta, G. & di Bernardo, D. Inference of gene regulatory networks and compound mode of action from time course gene expression profiles. Bioinformatics 22, 815–822 (2006).
  Article CAS PubMed Google Scholar
• Ernst, J., Vainas, O., Harbison, C. T., Simon, I. & Bar-Joseph, Z. Reconstructing dynamic regulatory maps. Mol. Syst. Biol. 3, 74 (2007). This paper shows that integrating static protein–DNA binding interactions with temporal gene expression data allows the reconstruction of dynamic networks.
  Article CAS PubMed PubMed Central Google Scholar
• Mendoza-Parra, M. A., Walia, M., Sankar, M. & Gronemeyer, H. Dissecting the retinoid-induced differentiation of F9 embryonal stem cells by integrative genomics. Mol. Syst. Biol. 7, 538 (2011).
  Article CAS PubMed PubMed Central Google Scholar
• Kalir, S. et al. Ordering genes in a flagella pathway by analysis of expression kinetics from living bacteria. Science 292, 2080–2083 (2001).
  Article CAS PubMed Google Scholar
• Sigal, A. et al. Generation of a fluorescently labeled endogenous protein library in living human cells. Nature Protoc. 2, 1515–1527 (2007).
  Article CAS Google Scholar
• Yeang, C.-H., Ideker, T. & Jaakkola, T. Physical network models. J. Comput. Biol. 11, 243–262 (2004).
  Article CAS PubMed Google Scholar
• Yeger-Lotem, E. et al. Bridging high-throughput genetic and transcriptional data reveals cellular responses to α-synuclein toxicity. Nature Genet. 41, 316–323 (2009).
  Article CAS PubMed Google Scholar
• Huang, S. C. & Fraenkel, E. Integration of proteomic, transcriptional, and interactome data reveals hidden signaling components. Sci. Signal. 2, ra40 (2009).
  PubMed PubMed Central Google Scholar
• Vinayagam, A. et al. A directed protein interaction network for investigating intracellular signal transduction. Sci. Signal. 4, rs8 (2011).
  Article CAS PubMed Google Scholar
• White, F. M. Quantitative phosphoproteomic analysis of signaling network dynamics. Curr. Opin. Biotechnol. 19, 404–409 (2008).
  Article CAS PubMed PubMed Central Google Scholar
• Kholodenko, B. N., Hancock, J. F. & Kolch, W. Signalling ballet in space and time. Nature Rev. Mol. Cell Biol. 11, 414–426 (2010).
  Article CAS Google Scholar
• Olsen, J. V. et al. Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 127, 635–648 (2006).
  Article CAS PubMed Google Scholar
• Gaucher, D. et al. Yellow fever vaccine induces integrated multilineage and polyfunctional immune responses. J. Exp. Med. 205, 3119–3131 (2008).
  Article CAS PubMed PubMed Central Google Scholar
• Cho, R. J. et al. Transcriptional regulation and function during the human cell cycle. Nature Genet. 27, 48–54 (2001).
  Article CAS PubMed Google Scholar
• Lu, X., Zhang, W., Qin, Z. S., Kwast, K. E. & Liu, J. S. Statistical resynchronization and Bayesian detection of periodically expressed genes. Nucleic Acids Res. 32, 447–455 (2004).
  Article CAS PubMed PubMed Central Google Scholar
• Bar-Joseph, Z., Farkash, S., Gifford, D. K., Simon, I. & Rosenfeld, R. Deconvolving cell cycle expression data with complementary information. Bioinformatics 20, i23–i30 (2004).
  Article CAS PubMed Google Scholar
• Siegal-Gaskins, D., Ash, J. N. & Crosson, S. Model-based deconvolution of cell cycle time-series data reveals gene expression details at high resolution. PLoS Comput. Biol. 5, e1000460 (2009).
  Article CAS PubMed PubMed Central Google Scholar
• Klutstein, M. et al. Combination of genomic approaches with functional genetic experiments reveals two modes of repression of yeast middle-phase meiosis genes. BMC Genomics 11, 478 (2010).
  Article CAS PubMed PubMed Central Google Scholar
• Smith, A. A., Vollrath, A., Bradfield, C. A. & Craven, M. Similarity queries for temporal toxicogenomic expression profiles. PLoS Comput. Biol. 4, e1000116 (2008).
  Article CAS PubMed PubMed Central Google Scholar
• Hafemeister, C., Costa, I. G., Schönhuth, A. & Schliep, A. Classifying short gene expression time-courses with Bayesian estimation of piecewise constant functions. Bioinformatics 27, 946–952 (2011).
  Article CAS PubMed Google Scholar
• Desai, K. H. et al. Dissecting inflammatory complications in critically injured patients by within-patient gene expression changes: a longitudinal clinical genomics study. PLoS Med. 8, e1001093 (2011).
  Article CAS PubMed PubMed Central Google Scholar
• Taylor, M. W. et al. Changes in gene expression during pegylated interferon and ribavirin therapy of chronic hepatitis C virus distinguish responders from nonresponders to antiviral therapy. J. Virol. 81, 3391–3401 (2007).
  Article CAS PubMed PubMed Central Google Scholar
• Liew, C.-C., Ma, J., Tang, H.-C., Zheng, R. & Dempsey, A. A. The peripheral blood transcriptome dynamically reflects system wide biology: a potential diagnostic tool. J. Lab. Clin. Med. 147, 126–132 (2006).
  Article CAS PubMed Google Scholar
• Smyth, G. in Bioinformatics and Computational Biology Solutions Using R And Bioconductor 397–420 (Springer, 2005).
  Book Google Scholar
• Leek, J. T., Monsen, E., Dabney, A. R. & Storey, J. D. EDGE: extraction and analysis of differential gene expression. Bioinformatics 22, 507–508 (2006).
  Article CAS PubMed Google Scholar
• Woo, S., Leek, J. T. & Storey, J. D. A computationally efficient modular optimal discovery procedure. Bioinformatics 27, 509–515 (2011).
  Article CAS PubMed Google Scholar
• Ernst, J. & Bar-Joseph, Z. STEM: a tool for the analysis of short time series gene expression data. BMC Bioinformatics 7, 191 (2006).
  Article CAS PubMed PubMed Central Google Scholar
• Schliep, A., Steinhoff, C. & Schönhuth, A. Robust inference of groups in gene expression time-courses using mixtures of HMMs. Bioinformatics 20, i283–i289 (2004).
  Article CAS PubMed Google Scholar
• Magni, P., Ferrazzi, F., Sacchi, L. & Bellazzi, R. TimeClust: a clustering tool for gene expression time series. Bioinformatics 24, 430–432 (2008).
  Article CAS PubMed Google Scholar
• Sivriver, J., Habib, N. & Friedman, N. An integrative clustering and modeling algorithm for dynamical gene expression data. Bioinformatics 27, i392–i400 (2011).
  Article CAS PubMed PubMed Central Google Scholar
• Sinha, A. & Markatou, M. A. Platform for Processing Expression of Short Time Series (PESTS). BMC Bioinformatics 12, 13 (2011).
  Article PubMed PubMed Central Google Scholar
• Bonneau, R. et al. The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol. 7, R36 (2006).
• Galbraith, S. J., Tran, L. M. & Liao, J. C. Transcriptome network component analysis with limited microarray data. Bioinformatics 22, 1886–1894 (2006).
  Article CAS PubMed Google Scholar
• Schaffter, T., Marbach, D. & Floreano, D. GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods. Bioinformatics 27, 2263–2270 (2011).
  Article CAS PubMed Google Scholar