Using combined evidence from replicates to evaluate ChIP-seq peaks
Author(s) -
Vahid Jalili,
Matteo Matteucci,
Marco Masseroli,
Marco J. Morelli
Publication year - 2015
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btv293
Subject(s) - computer science , false positive paradox , chromatin immunoprecipitation , computational biology , encode , source code , chromatin , data mining , false discovery rate , false positives and false negatives , chip , code (set theory) , genome , biology , set (abstract data type) , genetics , dna , artificial intelligence , gene , programming language , telecommunications , gene expression , promoter
Chromatin Immunoprecipitation followed by sequencing (ChIP-seq) detects genome-wide DNA-protein interactions and chromatin modifications, returning enriched regions (ERs), usually associated with a significance score. Moderately significant interactions can correspond to true, weak interactions, or to false positives; replicates of a ChIP-seq experiment can provide co-localised evidence to decide between the two cases. We designed a general methodological framework to rigorously combine the evidence of ERs in ChIP-seq replicates, with the option to set a significance threshold on the repeated evidence and a minimum number of samples bearing this evidence.
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