Optimization of transcription factor binding map accuracy utilizing knockout-mouse models
Web of Science
AuthorKrebs, W; Schmidt, SV; Goren, A; De Nardo, D; Labzin, L; Bovier, A; Ulas, T; Theis, H; Kraut, M; Latz, E; ...
Source TitleNucleic Acids Research
PublisherOXFORD UNIV PRESS
University of Melbourne Author/sde Nardo, Dominic
AffiliationMedical Biology (W.E.H.I.)
Document TypeJournal Article
CitationsKrebs, W., Schmidt, S. V., Goren, A., De Nardo, D., Labzin, L., Bovier, A., Ulas, T., Theis, H., Kraut, M., Latz, E., Beyer, M. & Schultze, J. L. (2014). Optimization of transcription factor binding map accuracy utilizing knockout-mouse models. NUCLEIC ACIDS RESEARCH, 42 (21), pp.13051-13060. https://doi.org/10.1093/nar/gku1078.
Access StatusOpen Access
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.
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