Additional file 1: Supplementary Text and Figures. of Integrating transcriptional and protein interaction networks to prioritize condition-specific master regulators

Supplementary text describes (a) the robustness of the results to the growth rate cutoff for yeast strains, (b) an analysis combining differential expression with protein interaction degree, and (c) the characterization of the protein interactors of driver TFs. Figures present results for (i) network integration for rapamycin data with alternative growth cutoff, (ii) PANDA analysis of rapamycin expression data, (iii) rapamycin network integration using only yeast two-hybrid data, (iv) results of combining differential expression with PPI degree, (v) ROC curves for network integration in multiple yeast conditions, and (vi) the rank characteristics and local PPI neighborhoods of driver TFs for menadione, DTT and diamide. (PDF 1304 kb

Additional file 3: of Integrating transcriptional and protein interaction networks to prioritize condition-specific master regulators

Top TF drivers of rapamycin response ranked by degree in transcriptional or PPI network. (XLSX 57 kb

Additional file 4: of Integrating transcriptional and protein interaction networks to prioritize condition-specific master regulators

Top transcription factors, as ranked either by 1) differential expression by transforming viral oncogenes, 2) degree in the GMIT network or 3) degree in the PANDA network. (XLSX 41 kb

Additional file 5: of Integrating transcriptional and protein interaction networks to prioritize condition-specific master regulators

Lists of enriched GO terms for the direct protein interactors of subsets of driver TFs that are ranked higher using the combined network score than either individual network. Lists correspond to the following conditions: rapamycin, diamide and menadione in yeast, and viral oncogene perturbation in humans. (XLSX 77 kb

An Optimized Predictive Strategy for Interactome Mapping

We present an optimized experimental strategy that can accelerate progress toward identifying the majority of pairwise protein interactions. Our method involves applying a predictive algorithm, based on the existing data, to identify protein pairs likely to interact and prioritizing these for screening. The approach is iterative as additional data allows one to refine predictions directing the next stage of experimentation

Additional file 2: of Integrating transcriptional and protein interaction networks to prioritize condition-specific master regulators

GO term enrichment for expression-based clusters in rapamycin-treated yeast. (XLSX 161 kb

The majority of COPD Network GWAS SNPs are annotated for functional impact.

<p>Of the 30 SNPs that are eQTLs in the LGRC network and also associated with COPD (FDR < 0.05), 15 are likely to affect transcription factor (TF) binding and linked to the expression of a target gene (a score of 1b, d, or f), 2 have evidence of TF binding or a DNase peak (a score of 5), and 11 are located in a motif hit (a score of 6) according to RegulomeDB [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005033#pcbi.1005033.ref037" target="_blank">37</a>].</p

eQTLs show strong community structure.

<p><b>(a)</b> Plot of the communities within the bipartite eQTL network. The nodes (genes and SNPs) in each community form a ring, with the link density within each ring visibly darker than links between communities. <b>(b)</b> Links within communities (colored points) are shown along the diagonal, with links that go between communities in black. Community IDs are plotted along the <i>x</i>-axis.</p

Overview of the CONDOR algorithm.

<p>All possible SNP-gene pairs from an appropriate data set are considered in an eQTL analysis. Both <i>cis-</i> and <i>trans-</i>acting eQTLs (FDR < 0.1) are used to construct a bipartite network linking SNPs and genes. The resulting network structure is then analyzed, first globally to understand its overall structure and to identify network “hubs.” Then the community structure of the bipartite network is determined, each community is subject to functional enrichment analysis, and a core score is calculated to identify those SNPs most likely to disrupt individual communities.</p

Quantile-quantile plot for 13,333,199 <i>cis-</i> and 17,228,062,483 <i>trans-</i>eQTL p-values.

<p>Quantile-quantile plot for 13,333,199 <i>cis-</i> and 17,228,062,483 <i>trans-</i>eQTL p-values.</p