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

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Krebs, W; Schmidt, SV; Goren, A; De Nardo, D; Labzin, L; Bovier, A; Ulas, T; Theis, H; Kraut, M; Latz, E; ...Date
2014-12-01Source Title
Nucleic Acids ResearchPublisher
OXFORD UNIV PRESSUniversity of Melbourne Author/s
de Nardo, DominicAffiliation
Medical Biology (W.E.H.I.)Metadata
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Krebs, 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 Status
Open AccessAbstract
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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