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

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.

SUBMITTER: Krebs W

PROVIDER: S-EPMC4245947 | biostudies-literature |

REPOSITORIES: biostudies-literature

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

Project description:Genome-wide assessment of protein-DNA interactions binding by ChIP-seq is a key technology to study transcription factor (TF) localization and regulation of gene-expression. In ChIP-seq, signal-to-noise-ratio as well as signal specificity depend on many variables including antibody quality, and efforts to improve ChIP-seq data thus far focused mostly on generating better reagents. Here we introduce KOIN (KO 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.

2014-07-17 | GSE55317 | GEO

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

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Inferring functional transcription factor-gene binding pairs by integrating transcription factor binding data with transcription factor knockout data.

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Genome-wide map of human and mouse transcription factor binding sites aggregated from ChIP-Seq data.

Project description:OBJECTIVES:Mammalian genomics studies, especially those focusing on transcriptional regulation, require information on genomic locations of regulatory regions, particularly, transcription factor (TF) binding sites. There are plenty of published ChIP-Seq data on in vivo binding of transcription factors in different cell types and conditions. However, handling of thousands of separate data sets is often impractical and it is desirable to have a single global map of genomic regions potentially bound by a particular TF in any of studied cell types and conditions. DATA DESCRIPTION:Here we report human and mouse cistromes, the maps of genomic regions that are routinely identified as TF binding sites, organized by TF. We provide cistromes for 349 mouse and 599 human TFs. Given a TF, its cistrome regions are supported by evidence from several ChIP-Seq experiments or several computational tools, and, as an optional filter, contain occurrences of sequence motifs recognized by the TF. Using the cistrome, we provide an annotation of TF binding sites in the vicinity of human and mouse transcription start sites. This information is useful for selecting potential gene targets of transcription factors and detecting co-regulated genes in differential gene expression data.

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Comprehensive human transcription factor binding site map for combinatory binding motifs discovery.

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Knockout Mouse Models for Peroxiredoxins.

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