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

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 fact...

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Detalles Bibliográficos
Autores principales: Vorontsov, Ilya E., Fedorova, Alla D., Yevshin, Ivan S., Sharipov, Ruslan N., Kolpakov, Fedor A., Makeev, Vsevolod J., Kulakovskiy, Ivan V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6199713/
https://www.ncbi.nlm.nih.gov/pubmed/30352610
http://dx.doi.org/10.1186/s13104-018-3856-x
Descripción
Sumario: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.