Analyzing time series gene expression data
TLDR
This review is intended to serve as both, a point of reference for experimental biologists looking for practical solutions for analyzing their data, and a starting point for computer scientists interested in working on the computational problems related to time series expression analysis.Abstract:
Motivation: Time series expression experiments are an increasingly popular method for studying a wide range of biological systems. However, when analyzing these experiments researchers face many new computational challenges. Algorithms that are specifically designed for time series experiments are required so that we can take advantage of their unique features (such as the ability to infer causality from the temporal response pattern) and address the unique problems they raise (e.g. handling the different non-uniform sampling rates).
Results: We present a comprehensive review of the current research in time series expression data analysis. We divide the computational challenges into four analysis levels: experimental design, data analysis, pattern recognition and networks. For each of these levels, we discuss computational and biological problems at that level and point out some of the methods that have been proposed to deal with these issues. Many open problems in all these levels are discussed. This review is intended to serve as both, a point of reference for experimental biologists looking for practical solutions for analyzing their data, and a starting point for computer scientists interested in working on the computational problems related to time series expression analysis.read more
Citations
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Scottish Mytilus trossulus mussels retain ancestral mitochondrial DNA: complete sequences of male and female mtDNA genomes.
TL;DR: Analysis of subspecies-specific diagnostic nuclear DNA markers confirms the presence of a high frequency of mussels with M. trossulus ancestry at Loch Etive and the genetic structure suggests hybridisation at an intermediate stage compared with North American populations, where there is little hybridisation, and Baltic populationsWhere there is extensive introgression.
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Automated Discovery of Functional Generality of Human Gene Expression Programs
TL;DR: GeneProgram is a new unsupervised computational framework based on Hierarchical Dirichlet Processes that addresses each of the above challenges of discovery of expression programs and discovered expression programs that appear to implicate surprising signaling pathways or receptor types in the response to infection.
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Gene Expression Switching of Receptor Subunits in Human Brain Development
TL;DR: This work systematically detects pairs of receptor-subunit variants that switch during the lifetime of the human brain by analyzing postmortem expression data collected in a population of donors at various ages and brain regions measured using microarray and RNA-seq.
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Inferring time-varying network topologies from gene expression data
TL;DR: The approach uses a clustering method based on these underlying dynamics, followed by system identification using a state-space model for each learnt cluster—to infer a network adjacency matrix.
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Genome-Wide Network Analysis Reveals the Global Properties of IFN-β Immediate Transcriptional Effects in Humans
Guy Haskin Fernald,Simon R.V. Knott,Andrew R. Pachner,Stacy J. Caillier,Kavitha Narayan,Jorge R. Oksenberg,Parvin Mousavi,Sergio E. Baranzini +7 more
TL;DR: This is the first study that incorporates network analysis to investigate gene regulation in response to a therapeutic drug in humans, and reveals a tight core of immune- and apoptosis-related genes associated with higher values of MI.
References
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Cluster analysis and display of genome-wide expression patterns
TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.
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Todd R. Golub,Todd R. Golub,Donna K. Slonim,Pablo Tamayo,Christine Huard,Michelle Gaasenbeek,Jill P. Mesirov,Hilary A. Coller,Mignon L. Loh,James R. Downing,Michael A. Caligiuri,Clara D. Bloomfield,Eric S. Lander +12 more
TL;DR: A generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case and suggests a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
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Comprehensive Identification of Cell Cycle–regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization
Paul T. Spellman,Gavin Sherlock,Gavin Sherlock,Michael Q. Zhang,Vishwanath R. Iyer,Kirk R. Anders,Michael B. Eisen,Patrick O. Brown,Patrick O. Brown,David Botstein,Bruce Futcher +10 more
TL;DR: A comprehensive catalog of yeast genes whose transcript levels vary periodically within the cell cycle is created, and it is found that the mRNA levels of more than half of these 800 genes respond to one or both of these cyclins.
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Genomic expression programs in the response of yeast cells to environmental changes.
Audrey P. Gasch,Paul T. Spellman,Camilla M. Kao,Orna Carmel-Harel,Michael B. Eisen,Gisela Storz,David Botstein,Patrick O. Brown +7 more
TL;DR: Analysis of genomic expression patterns in the yeast Saccharomyces cerevisiae implicated the transcription factors Yap1p, as well as Msn2p and Msn4p, in mediating specific features of the transcriptional response, while the identification of novel sequence elements provided clues to novel regulators.
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Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays.
Uri Alon,Naama Barkai,Daniel A. Notterman,Kurt C. Gish,S. Ybarra,David H. Mack,A. J. Levine,A. J. Levine +7 more
TL;DR: In this paper, a two-way clustering algorithm was applied to both the genes and the tissues, revealing broad coherent patterns that suggest a high degree of organization underlying gene expression in these tissues.