Computational identification of cellular networks and pathways

In this article we highlight recent developments in computational functional genomics to identify networks of functionally related genes and proteins based on diverse sources of genomic data. Our specific focus is on statistical methods to identify genetic networks. We discuss integrated analysis of microarray datasets, methods to combine heterogeneous data sources, the analysis of high-dimensional phenotyping screens and describe efforts to establish a reliable and unbiased gold standard for method comparison and evaluation.

[1]  D. Pe’er.  Bayesian Network Analysis of Signaling Networks: A Primer , 2005, Science's STKE.

[2]  K. Strimmer,et al.  Statistical Applications in Genetics and Molecular Biology A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics , 2011 .

[3]  Taro L. Saito,et al.  High-dimensional and large-scale phenotyping of yeast mutants. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Werner Dubitzky,et al.  Representing bioinformatics causality , 2004, Briefings Bioinform..

[5]  L. Wodicka,et al.  Genome-wide expression monitoring in Saccharomyces cerevisiae , 1997, Nature Biotechnology.

[6]  Robert E. Schapire,et al.  Hierarchical multi-label prediction of gene function , 2006, Bioinform..

[7]  Rainer Spang,et al.  Non-transcriptional pathway features reconstructed from secondary effects of RNA interference , 2005, Bioinform..

[8]  Tsuyoshi Kato,et al.  Selective integration of multiple biological data for supervised network inference , 2005, Bioinform..

[9]  T. Ideker,et al.  Systematic interpretation of genetic interactions using protein networks , 2005, Nature Biotechnology.

[10]  Paul Shannon,et al.  Derivation of genetic interaction networks from quantitative phenotype data , 2005, Genome Biology.

[11]  Yudong D. He,et al.  Functional Discovery via a Compendium of Expression Profiles , 2000, Cell.

[12]  Patrik D'haeseleer,et al.  Genetic network inference: from co-expression clustering to reverse engineering , 2000, Bioinform..

[13]  Korbinian Strimmer,et al.  An empirical Bayes approach to inferring large-scale gene association networks , 2005, Bioinform..

[14]  Marco Grzegorczyk,et al.  Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks , 2006, Bioinform..

[15]  Ronald W. Davis,et al.  Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray , 1995, Science.

[16]  Ezgi O. Booth,et al.  Epistasis analysis with global transcriptional phenotypes , 2005, Nature Genetics.

[17]  Matthew A. Hibbs,et al.  Finding function: evaluation methods for functional genomic data , 2006, BMC Genomics.

[18]  Joshua M. Stuart,et al.  A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules , 2003, Science.

[19]  Matthew A. Hibbs,et al.  Discovery of biological networks from diverse functional genomic data , 2005, Genome Biology.

[20]  Olga G. Troyanskaya,et al.  A scalable method for integration and functional analysis of multiple microarray datasets , 2006, Bioinform..

[21]  A. Fire,et al.  Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans , 1998, Nature.

[22]  Michal Linial,et al.  Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..

[23]  Ronald W. Davis,et al.  Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. , 1999, Science.

[24]  D. Pe’er,et al.  Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data , 2003, Nature Genetics.

[25]  Gavin Sherlock,et al.  Global analysis of gene function in yeast by quantitative phenotypic profiling , 2006, Molecular systems biology.

[26]  L. Avery,et al.  Ordering gene function: the interpretation of epistasis in regulatory hierarchies. , 1992, Trends in genetics : TIG.

[27]  Bernhard Schölkopf,et al.  Learning with kernels , 2001 .

[28]  Gary D Bader,et al.  Global Mapping of the Yeast Genetic Interaction Network , 2004, Science.

[29]  N. Perrimon,et al.  Genome-Wide RNAi Analysis of Growth and Viability in Drosophila Cells , 2004, Science.

[30]  Dmitrij Frishman,et al.  MIPS: analysis and annotation of proteins from whole genomes in 2005 , 2005, Nucleic Acids Res..

[31]  Gary D Bader,et al.  Systematic Genetic Analysis with Ordered Arrays of Yeast Deletion Mutants , 2001, Science.

[32]  Nir Friedman,et al.  Inferring Cellular Networks Using Probabilistic Graphical Models , 2004, Science.

[33]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[34]  Yoshihiro Yamanishi,et al.  Protein network inference from multiple genomic data: a supervised approach , 2004, ISMB/ECCB.

[35]  Gregory W. Carter,et al.  Inferring network interactions within a cell , 2005, Briefings Bioinform..

[36]  N. Perrimon,et al.  Sequential activation of signaling pathways during innate immune responses in Drosophila. , 2002, Developmental cell.

[37]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[38]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[39]  Adam A. Margolin,et al.  Reverse engineering of regulatory networks in human B cells , 2005, Nature Genetics.

[40]  D. Koller,et al.  From signatures to models: understanding cancer using microarrays , 2005, Nature Genetics.

[41]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[42]  D. Kell,et al.  A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations , 2001, Nature Biotechnology.

[43]  Atul J. Butte,et al.  Systematic survey reveals general applicability of "guilt-by-association" within gene coexpression networks , 2005, BMC Bioinformatics.