Department of Medicine, Division of Infectious Diseases and Immunology
Algorithms; Animals; Binding Sites; Chromatin Immunoprecipitation; High-Throughput Nucleotide Sequencing; Mice, Inbred C57BL; Mice, Knockout; Models, Animal; Nucleotide Motifs; Sequence Analysis, DNA; Transcription Factors
Cell Biology | Computational Biology | Genetics | Genomics
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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Citation: Nucleic Acids Res. 2014 Dec 1;42(21):13051-60. doi: 10.1093/nar/gku1078. Epub 2014 Nov 5. Link to article on publisher's site.
DOI of Published Version
Nucleic acids research
Krebs, Wolfgang; Schmidt, Susanne V.; Goren, Alon; De Nardo, Dominic; Labzin, Larisa; Bovier, Anton; Ulas, Thomas; Theis, Heidi; Kraut, Michael; Latz, Eicke; Beyer, Marc; and Schultze, Joachim L., "Optimization of transcription factor binding map accuracy utilizing knockout-mouse models" (2014). Open Access Articles. 2580.
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