Network Data


Protein-Protein Interactions

Bandyopadhyay S et al. A human MAP kinase interactome. Nature Methods 7(10):801-805 (2010)

  • Core network of 641 interactions supported by multiple lines of evidence including conservation with yeast. [Link to Data]

Ravasi T et al. An atlas of combinatorial transcriptional regulation in mouse and man. Cell 140(5):744-752 (2010)

  • Protein-protein interactions between transcription factors (gene centric and networks views) [Link to Data]

Lee K et al. Mapping plant interactomes using literature curated and predicted protein-protein interaction datasets. Plant Cell 22(4):997-1005 (2010)

  • Protein with or without specific localization using a threshold (in this study, <0.05) based on a false positive rate [Data Set]

Parrish JR et al. A proteome-wide protein interaction map for Campylobacter jejuni. Genome Biology 8(7):R130 (2007)

  • Proteome coverage from large-scale interaction screens [Data Set 1]
  • Representation of functional categories amongst the proteins in the CampyYTH v3.1 dataset [Data Set 2]
  • GO category representation amongst the proteins in CampyYTH v3.1 [Data Set 3]
  • C. jejuni genes that were toxic or inhibitory to yeast growth [Data Set 4]
  • Comparison of network features across organisms [Data Set 5]
  • Conserved subnetworks between C. jejuni and E. coli or C. jejuni and yeast [Data Set 6]
  • PredictedC. jejuni protein interactions [Data Set 7]
  • GO enrichment amongst the C. jejuni protein interactions [Data Set 10]
  • Essential proteins interact with each other more often than expected by chance [Data Set 11]
  • C. jejuni interologs predicted from large-scale protein interaction analyses performed for E. coli or H. pylori [Data Set 12]
  • Annotated list of all C. jejuni protein interactions in the CampyYTH v3.1 dataset [Data Set 13]

Genetic Interactions

Srivas R, Costelloe T, Carvunis AR, Sarkar S, Malta E, Sun SM, Pool M, Licon K, van Welsem T, van Leeuwen F, McHugh PJ, van Attikum H, Ideker T. A UV-Induced Genetic Network Links the RSC Complex to Nucleotide Excision Repair and Shows Dose-Dependent Rewiring. Cell Rep 5(6):1714-24 (2013)

  • List of query genes used in this study, related to Figure 1 [Table S1]
  • List of array genes used in this study, related to Figure 1 [Table S2]
  • List of all genetic interactions measured, related to Figure 1 [Table S3]
  • List of multigene module used in this study, related to Figure 2A [Table S4]
  • List of module-module interactions identified, related to Figure 2A [Table S5]
  • List of dose-specific interaction identified, related to Figure 5 [Table S6]
  • List of process definitions used in analysis [Table S7]
  • List of all yeast strains used [Table S8]
  • List of primers used [Table S9]

Dutkowski J, Kramer M, Surma MA, Balakrishnan R, Cherry JM, Krogan NJ, Ideker T. A gene ontology inferred from molecular networks. Nat Biotechnol. 31:38-45 (2013) [Suppl Materials]

Guénolé A, Srivas R, Vreeken K, Wang ZZ, Wang S, Krogan NJ, Ideker T, van Attikum H. Dissection of DNA Damage Responses Using Multiconditional Genetic Interaction Maps. Mol Cell (2012)

  • Lists of query and array genes used in this study along with their function, related to Figure 1 [Table S1]
  • List of all genetic interactions measured in this study, related to Figure 1 [Table S2]
  • List of gold standard genes belonging to various DNA repair and chromatin organization pathways, related to Figure 2 [Table S3]
  • List of multigene modules identified in this study, related to Figure 6 [Table S4]
  • List of module-module interactions identified in this study, related to Figure 6 [Table S5]
  • List of biological processes used in this study to examine the genetic crosstalk between DNA damage response processes, related to Figure 6 [Table S6]

Bandyopadhyay S, Mehta M, Kuo D, Sung MK, Chuang R, Jaehnig EJ, Bodenmiller D, Licon K, Copeland W, Shales M, Fiedler D, Dutkowski J, Guenole A, van Attikum H, Shokat KM, Kolodner RD, Huh WK, Aebersold R, Keogh MC, Krogan NJ, Ideker T. Rewiring of genetic networks in response to DNA damage. Science 330:1385-1389 (2010) [Get PDF][Suppl Docs][PubMed link]

Wilmes GM et al. A genetic interaction map of RNA-processing factors reveals links between Sem1/Dss1-containing complexes and mRNA export and splicing. Mol Cell 32(5):735-746 (2008)

  • Genetic interaction scores for the RNA-Processing E-MAP [Data Set]

Roguev A et al. Conservation and rewiring of functional modules revealed by an epistasis map in fission yeast. Science 322:405-410 (2008) [Suppl Materials and Methods]

Transcriptional Interactions

Lin YC et al. A global network of transcription factors, involving E2A, EBF1 and Foxo1, that orchestrates B cell fate. Nature Immunology 11(7):635-643 (2010)

  • ChIP-Seq and gene expression profiling approximating developmental stages of B cell development. [Data Set]

van Steensel B et al. Bayesian network analysis of targeting interactions in chromatin. Genome Research 20(2):190-200 (2010)

  • Binding profiles of 43 chromatin components, with probe annotation. [Data Set]

Tan K et al. A systems approach to delineate functions of paralogous transcription factors: role of the Yap family in the DNA damage response. Proc Natl Acad Sci 105(8):2934-8 (2008)

  • Chip-chip of five YAPs in each drug-treated (MMS or CDDP) and untreated (SC media) conditions [Data Set]

Beyer A et al. Integrated assessment and prediction of transcription factor binding. Proc Natl Acad Sci 103(25):9464-9 (2006)

  • All TF–Target Interactions with LLS > 4 [Data]
  • TF Modules for LLS Threshold 4 [Data]
  • TF Modules for LLS Threshold 5 [Data]
  • Significant Overlaps (p < 10−4) between Target Gene Sets and Coexpression Clusters [Data]
  • Positive Control Set of TF–Target Interactions [Data]

Workman CT et al. A systems approach to mapping DNA damage response pathways. Science 312(5776):1054-1059 (2006)

  • Transcription factor binding data (ChIP-chip) for MMS treated and untreated cells, and pathway models [Data Tables]

 

Gene Expression

Chuang HY, Rassenti L, Salcedo M, Licon K, Kohlmann A, Haferlach T, Foà R, Ideker T, Kipps TJ. Subnetwork-based analysis of chronic lymphocytic leukemia identifies pathways that associate with disease progression. Blood (2012) [Suppl Figures, Methods, Data]

  • Expression data from untreated CLL patients [GEO Data Set]

Kuo D et al. Evolutionary divergence in the fungal response to fluconazole revealed by soft clustering. Genome Biology 11(7):R77 (2010)

  • Expression profiles of three yeast species after exposure to fluconazole. [Link to Data]

Gersten M et al. An integrated systems analysis implicates EGR1 downregulation in SIVE-induced neural dysfunction. Journal of Neuroscience 29(40):12467-76 (2009)

  • RNA from duplicate hippocampal samples taken from nine control monkeys and nine monkeys with evidence of SIV encephalitis were hybridized to Affymetrix arrays.[Data Set]

Kelley R, Ideker T. Genome-wide fitness and expression profiling implicate Mga2 in adaptation to hydrogen peroxide. PLoS Genetics 5(5):31000488 (2009)

  • Enrichment Summary: Differentially expressed or sensitive members of each significantly over-represented condition or transcription factor target set mentioned in the study. [Data Set]
  • Expression Table: Log ratios and p-values for all micro-array expression profiling experiments conducted in this study. [Data Set]

Tan K et al. A systems approach to delineate functions of paralogous transcription factors: role of the Yap family in the DNA damage response. Proc Natl Acad Sci 105(8):2934-8 (2008)

  • Chip-chip of five YAPs in each drug-treated (MMS or CDDP) and untreated (SC media) conditions [Data Set]

Workman CT et al. A systems approach to mapping DNA damage response pathways. Science 312(5776):1054-1059 (2006)

  • Expression and deletion-buffering data in TF knockouts and wild-type, and deletion-buffering and deletion-enhancement analyses [Data Tables]

Smith JJ et al. Transcriptome profiling to identify genes involved in peroxisome assembly and function. J Cell Biol 158(2):259-271 (2002)

  • Summary of clustering and array data for 3,031 genes that showed significant differential expression for at least one of the eight experiments listed. [Data Set]

Ideker T et al. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 292: 929-934 (2001)

  • mRNA- and protein-expression responses to galactose pathway perturbations [Data Table]

Systematic Phenotyping

Chen M, Licon K, Otsuka R, Pillus L, Ideker T. Decoupling epigenetic and genetic effects through systematic analysis of gene position. Cell Reports (2013)

  • Data included are RNA-Seq data for 4 heterzygous diploid yeast strains and diploid wild-type. There are three replicates for each heterzygous strain, and six replicates for wild-type. [GEO data set]
  • Primers used for quantitative PCR, related to experimental procedures [Table S1]

Konig R et al. Human host factors required for influenza virus replication. Nature 463:813-817 (2010)

  • Scores of 295 confirmed genes required for influenza virus replication [Data Table]

Kelley R, Ideker T. Genome-wide fitness and expression profiling implicate Mga2 in adaptation to hydrogen peroxide. PLoS Genetics 5(5):31000488 (2009)

  • Fitness Table: P-values for acute and adaptive screens conducted in this study. [Data Set]
  • TFs Table: Table containing all of the transcription factor target sets used in this study. [Data Set]

Methylation

Hannum G, Ginney J, Zhao L, Zhang L, Hughes G, Sadda S, Klotzle B, Bibikova M, Fan JB, Gao Y, Deconde R, Chen M, Rajapakse I, Friend S, Ideker T, Zhang K. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell (2012)

  • Bisulphite converted DNA from the 656 samples were hybridised to the Illumina Infinium 450k Human Methylation Beadchip [GEO Data Set]
  • Aging model markers, related to Figure 2 [Table S3]
  • Aging model markers for TCGA data, related to Figure 4 [Table S5]
  • Genes associated with aging in both the methylome and the transcriptome, related to Figure 7 [Table S6]
  • Transcriptome aging model, related to Figure 7 [Table S7]