Benchmarking regulatory network reconstruction with GRENDEL

MOTIVATION Over the past decade, the prospect of inferring networks of gene regulation from high-throughput experimental data has received a great deal of attention. In contrast to the massive effort that has gone into automated deconvolution of biological networks, relatively little effort has been invested in benchmarking the proposed algorithms. The rate at which new network inference methods are being proposed far outpaces our ability to objectively evaluate and compare them. This is largely due to a lack of fully understood biological networks to use as gold standards. RESULTS We have developed the most realistic system to date that generates synthetic regulatory networks for benchmarking reconstruction algorithms. The improved biological realism of our benchmark leads to conclusions about the relative accuracies of reconstruction algorithms that are significantly different from those obtained with A-BIOCHEM, an established in silico benchmark. AVAILABILITY The synthetic benchmark utility and the specific benchmark networks that were used in our analyses are available at http://mblab.wustl.edu/software/grendel/.

[1]  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.

[2]  H. Agrawal.  Extreme self-organization in networks constructed from gene expression data. , 2002, Physical review letters.

[3]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[4]  Araceli M. Huerta,et al.  From specific gene regulation to genomic networks: a global analysis of transcriptional regulation in Escherichia coli. , 1998, BioEssays : news and reviews in molecular, cellular and developmental biology.

[5]  Dirk Husmeier,et al.  Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks , 2003, Bioinform..

[6]  Chris Wiggins,et al.  ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context , 2004, BMC Bioinformatics.

[7]  S. Shen-Orr,et al.  Network motifs in the transcriptional regulation network of Escherichia coli , 2002, Nature Genetics.

[8]  Robert Krauthgamer,et al.  Detecting protein sequence conservation via metric embeddings , 2003, ISMB.

[9]  A. Hill,et al.  The possible effects of the aggregation of the molecules of haemoglobin on its dissociation curves , 1910 .

[10]  E. O’Shea,et al.  Global analysis of protein expression in yeast , 2003, Nature.

[11]  S. Ranade,et al.  Stem cell transcriptome profiling via massive-scale mRNA sequencing , 2008, Nature Methods.

[12]  Stefanie Widder,et al.  The SBML ODE Solver Library: a native API for symbolic and fast numerical analysis of reaction networks , 2006, Bioinform..

[13]  B. Williams,et al.  Mapping and quantifying mammalian transcriptomes by RNA-Seq , 2008, Nature Methods.

[14]  Pedro Mendes,et al.  Artificial gene networks for objective comparison of analysis algorithms , 2003, ECCB.

[15]  J. Pérez-Ortín,et al.  Genomic run-on evaluates transcription rates for all yeast genes and identifies gene regulatory mechanisms. , 2004, Molecular cell.

[16]  Alberto de la Fuente,et al.  Discovery of meaningful associations in genomic data using partial correlation coefficients , 2004, Bioinform..

[17]  J. Collins,et al.  Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles , 2007, PLoS biology.

[18]  N. Lee,et al.  Computational and experimental approaches for modeling gene regulatory networks. , 2007, Current pharmaceutical design.

[19]  Hamid Bolouri,et al.  Dizzy: Stochastic Simulation of Large-scale Genetic Regulatory Networks , 2005, J. Bioinform. Comput. Biol..

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

[21]  L. Aravind,et al.  Comprehensive analysis of combinatorial regulation using the transcriptional regulatory network of yeast. , 2006, Journal of molecular biology.

[22]  V. Anne Smith,et al.  Influence of Network Topology and Data Collection on Network Inference , 2003, Pacific Symposium on Biocomputing.

[23]  Kathleen Marchal,et al.  SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms , 2006, BMC Bioinformatics.

[24]  Hiroaki Kitano,et al.  The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models , 2003, Bioinform..

[25]  Mudita Singhal,et al.  COPASI - a COmplex PAthway SImulator , 2006, Bioinform..

[26]  J. H. Hofmeyr,et al.  The reversible Hill equation: how to incorporate cooperative enzymes into metabolic models , 1997, Comput. Appl. Biosci..

[27]  Francis J. Doyle,et al.  Simulation Studies for the Identification of Genetic Networks from cDNA Array and Regulatory Activity Data , 2001 .

[28]  A. Barabasi,et al.  Network biology: understanding the cell's functional organization , 2004, Nature Reviews Genetics.

[29]  Eyad Almasri,et al.  Rank-based edge reconstruction for scale-free genetic regulatory networks , 2008, BMC Bioinformatics.

[30]  D. Lockhart,et al.  Expression monitoring by hybridization to high-density oligonucleotide arrays , 1996, Nature Biotechnology.

[31]  Hiroaki Kitano,et al.  CellDesigner: a process diagram editor for gene-regulatory and biochemical networks , 2003 .

[32]  Kelvin H. Lee,et al.  Dynamical analysis of gene networks requires both mRNA and protein expression information. , 1999, Metabolic engineering.

[33]  E. O’Shea,et al.  Quantification of protein half-lives in the budding yeast proteome , 2006, Proceedings of the National Academy of Sciences.

[34]  M. Weigt,et al.  Gene-network inference by message passing , 2008, 0812.0936.

[35]  A. Califano,et al.  Dialogue on Reverse‐Engineering Assessment and Methods , 2007, Annals of the New York Academy of Sciences.

[36]  Satoru Miyano,et al.  Inferring gene networks from time series microarray data using dynamic Bayesian networks , 2003, Briefings Bioinform..

[37]  R. Laubenbacher,et al.  A computational algebra approach to the reverse engineering of gene regulatory networks. , 2003, Journal of theoretical biology.

[38]  Michael R. Green,et al.  Dissecting the Regulatory Circuitry of a Eukaryotic Genome , 1998, Cell.

[39]  John Quackenbush,et al.  Multiple-laboratory comparison of microarray platforms , 2005, Nature Methods.