Optimization of transcription factor binding map accuracy utilizing knockout-mouse models.

Nucleic Acids Res
Authors
Keywords
Abstract

Genome-wide assessment of protein-DNA interaction by chromatin immunoprecipitation followed by massive parallel sequencing (ChIP-seq) is a key technology for studying transcription factor (TF) localization and regulation of gene expression. Signal-to-noise-ratio and signal specificity in ChIP-seq studies depend on many variables, including antibody affinity and specificity. Thus far, efforts to improve antibody reagents for ChIP-seq experiments have focused mainly on generating higher quality antibodies. Here we introduce KOIN (knockout implemented normalization) as a novel strategy to increase signal specificity and reduce noise by using TF knockout mice as a critical control for ChIP-seq data experiments. Additionally, KOIN can identify 'hyper ChIPable regions' as another source of false-positive signals. As the use of the KOIN algorithm reduces false-positive results and thereby prevents misinterpretation of ChIP-seq data, it should be considered as the gold standard for future ChIP-seq analyses, particularly when developing ChIP-assays with novel antibody reagents.

Year of Publication
2014
Journal
Nucleic Acids Res
Volume
42
Issue
21
Pages
13051-60
Date Published
2014 Dec 01
ISSN
1362-4962
URL
DOI
10.1093/nar/gku1078
PubMed ID
25378309
PubMed Central ID
PMC4245947
Links
Grant list
1R01HL093262 / HL / NHLBI NIH HHS / United States