Inhibition of mutagenic translesion synthesis: A possible strategy for improving chemotherapy?
Name
journal.pgen.1006842.pdf
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2.25 MB
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3be90eba083ee998ba65bc3bd7762838
Author(s)
Yamanaka, Kinrin
Chatterjee, Nimrat
Hemann, Michael
Walker, Graham C.
Date Issued
August 2017
Journal
PLOS Genetics
Publisher
Public Library of Science (PLoS)
Citation
Yamanaka, Kinrin, et al. “Inhibition of Mutagenic Translesion Synthesis: A Possible Strategy for Improving Chemotherapy?” PLOS Genetics, edited by Sue Jinks-Robertson, vol. 13, no. 8, Aug. 2017, p. e1006842.
Version
Final published version
Abstract
With the recent technological developments a vast amount of high-throughput data has been profiled to understand the mechanism of complex diseases. The current bioinformatics challenge is to interpret the data and underlying biology, where efficient algorithms for analyzing heterogeneous high-throughput data using biological networks are becoming increasingly valuable. In this paper, we propose a software package based on the Prize-collecting Steiner Forest graph optimization approach. The PCSF package performs fast and user-friendly network analysis of high-throughput data by mapping the data onto a biological networks such as protein-protein interaction, gene-gene interaction or any other correlation or coexpression based networks. Using the interaction networks as a template, it determines high-confidence subnetworks relevant to the data, which potentially leads to predictions of functional units. It also interactively visualizes the resulting subnetwork with functional enrichment analysis.
MIT Department
Massachusetts Institute of Technology. Department of Biology
Koch Institute for Integrative Cancer Research at MIT
Terms of Use
Creative Commons Attribution 4.0 International License
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DOI of Published Version
https://doi.org/10.1371/JOURNAL.PGEN.1006842
