SAMNetWeb: identifying condition-specific networks linking signaling and transcription.

TitleSAMNetWeb: identifying condition-specific networks linking signaling and transcription.
Publication TypeJournal Article
Year of Publication2015
AuthorsGosline, SJC, Oh, C, Fraenkel, E
Date Published2015 Apr 1
KeywordsAlgorithms, Biomarkers, Tumor, Breast Neoplasms, Data Interpretation, Statistical, Female, Gene Expression Profiling, Gene Regulatory Networks, Genomics, Humans, Internet, Lung Neoplasms, Proteomics, RNA, Messenger, Signal Transduction, Software, Systems Biology, Tumor Cells, Cultured

MOTIVATION: High-throughput datasets such as genetic screens, mRNA expression assays and global phospho-proteomic experiments are often difficult to interpret due to inherent noise in each experimental system. Computational tools have improved interpretation of these datasets by enabling the identification of biological processes and pathways that are most likely to explain the measured results. These tools are primarily designed to analyse data from a single experiment (e.g. drug treatment versus control), creating a need for computational algorithms that can handle heterogeneous datasets across multiple experimental conditions at once.SUMMARY: We introduce SAMNetWeb, a web-based tool that enables functional enrichment analysis and visualization of high-throughput datasets. SAMNetWeb can analyse two distinct data types (e.g. mRNA expression and global proteomics) simultaneously across multiple experimental systems to identify pathways activated in these experiments and then visualize the pathways in a single interaction network. Through the use of a multi-commodity flow based algorithm that requires each experiment 'share' underlying protein interactions, SAMNetWeb can identify distinct and common pathways across experiments.AVAILABILITY AND IMPLEMENTATION: SAMNetWeb is freely available at

Alternate JournalBioinformatics
PubMed ID25414365
PubMed Central IDPMC4382899
Grant ListR01 GM089903 / GM / NIGMS NIH HHS / United States
R01GM089903 / GM / NIGMS NIH HHS / United States
U54 CA112967 / CA / NCI NIH HHS / United States
U54CA112967 / CA / NCI NIH HHS / United States