PROTEIN-PROTEIN INTERACTIONS

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

• Core network of 641 interations 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]

Fossum E et al. Evolutionarily conserved herpesviral protein interaction networks. PLoS Pathogens 5(9):e1000570 (2009)

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

Hannum G, Srivas R et al. Genome-wide assocation data reveal a global map of genetic interactions among protein compolexes. PLoS Genetics 5(12):e1000782 (2009)

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)

• [Data Set 1]
• 151 S. pombe protein-protein interaction pairs considering only interactions derived from stringent biochemical methods [Data Set 2]
• [Data Set 3]
• [Data Set 4]

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 trageting interactions in chromatin. Gemone 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 inthe DNA damage response. Proc Natl Acad Sci105(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]
• Additional data sets can be found at: http://www.fli-leibniz.de/tsb/tfb

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

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 dysfuction. 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 inthe DNA damage response. Proc Natl Acad Sci105(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

Kim J et al. Functional genomic screen for modulators of ciliogenesis and cilium length. Nature 464(7291):1048-51 (2010)

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]

Bushman FD et al. Host cell factors in HIV replication: meta-analysis of genome-wide studies. PLoS Pathog 5(5):e1000437 (2009)

Konig R et al. Global analysis of host-pathogen interactions that regulate early-stage HIV-1 replication. Cell 135(1):49-60 (2008)

Begley TJ et al. Hot spots for modulating toxicity identified by genomic phenotyping and localization on mapping. Molecular Cell 16(1):117-125 (2004)

Begley T et al. Damage recovery pathways in Saccharomyces cerevisiae revealed by genomic phenotyping and interactome mapping. Mol Cancer Res 1(2):103-112 (2002)

Griffin T et al. Complementary profiling of gene expression at the transcriptome and proteome levels in S. cerevisiae. Molecular and Cellular Proteomics 1:323-333 (2002)

PROTEOMICS

Griffin T et al. Complementary profiling of gene expression at the transcriptome and proteome levels in S. cerevisiae. Molecular and Cellular Proteomics 1:323-333 (2002)

PROTEIN INTERACTION DATABASES

Database of Interacting Proteins (DIP)
The Biomolecular Interaction Network Database (BIND)
Molecular INTeractions Database (MINT)
National Center for Biotechnology Information (NCBI)
General Respository for Interaction Datasets (GRID)
IntAct
MIPS Mammalian Protein-Protein Interaction Database
Saccharomyces Genome Database (SGD)
Curagen PathCalling
NCICB Cancer Molecular Analysis Project (cMAP)
Amino Acid-Nucleotide Interaction Database (AANT)
Human Protein Interaction Database (HPID)
Human Protein Reference Database (HPRD)
CellCircuit Search
Molecular interaction models provide us with a framework for integrating the large-scale data that we are now able to collect at multiple levels of biological information - genes, RNAs, proteins, and small molecules. Cell Circuit Search is a web-based interface for searching for genes that appear in our library of network models.