SAMNetWeb: identifying condition-specific networks linking signaling and transcription
Name
gosline_samnetweb_manuscript-final-Fraenkel.pdf
Description
author note: . (The journal provide a template for formatting that we used for all submissions, which is why the layout looks like the published version).
Size
211.98 KB
Format
Adobe PDF
Checksum (MD5)
904f0a4029f7975bb70ea936c8687961
Author(s)
Oh, Coyin
Fraenkel, Ernest
Gosline, Sara Calafell
Date Issued
November 2014
Journal
Bioinformatics
Publisher
Oxford University Press
Citation
Gosline, S. J. C.; Oh, C. and Fraenkel, E.. “SAMNetWeb: Identifying Condition-Specific Networks Linking Signaling and Transcription.” Bioinformatics 31, no. 7 (November 2014): 1124–1126.
Version
Author's final manuscript
Abstract
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 http://fraenkel.mit.edu/samnetweb.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1093/bioinformatics/btu748
