Gracob: a novel graph-based constant-column biclustering method for mining growth phenotype data

Majed A. Alzahrani, Hiroyuki Kuwahara, Wei Wang, Xin Gao

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Growth phenotype profiling of genome-wide genedeletion strains over stress conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. Systematically identifying groups of genes from such high-throughput data that share similar patterns of conditional essentiality and dispensability under various environmental conditions can elucidate how genetic interactions of the growth phenotype are regulated in response to the environment.We first demonstrate that detecting such\co-fit
Original languageEnglish (US)
Pages (from-to)2523-2531
Number of pages9
JournalBioinformatics
Volume33
Issue number16
DOIs
StatePublished - Apr 4 2017
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): URF/1/1976-04, NIH U01HG008488, NIH R01GM115833, NIH U54GM114833, NSF DBI-1565137, NSF IIS-1313606
Acknowledgements: The research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/1976-04. NIH U01HG008488, NIH R01GM115833, NIH U54GM114833, NSF DBI-1565137, and NSF IIS-1313606.

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