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Nucleic Acids Res. 2017 Jan 4;45(D1):D61-D67. doi: 10.1093/nar/gkw951. Epub 2016 Oct 24.

GTRD: a database of transcription factor binding sites identified by ChIP-seq experiments.

Author information

1
BIOSOFT.RU, LLC, Novosibirsk 630058, Russian Federation.
2
Institute of Computational Technologies SB RAS, Novosibirsk 630090, Russian Federation.
3
Novosibirsk State University, Novosibirsk 630090, Russian Federation.
4
A.P. Ershov Institute of Informatics Systems SB RAS, Novosibirsk 630090, Russian Federation.
5
Institute of Chemical Biology and Fundamental Medicine SB RAS, Novosibirsk 630090, Russian Federation.
6
BIOSOFT.RU, LLC, Novosibirsk 630058, Russian Federation fedor@biouml.org.

Abstract

GTRD-Gene Transcription Regulation Database (http://gtrd.biouml.org)-is a database of transcription factor binding sites (TFBSs) identified by ChIP-seq experiments for human and mouse. Raw ChIP-seq data were obtained from ENCODE and SRA and uniformly processed: (i) reads were aligned using Bowtie2; (ii) ChIP-seq peaks were called using peak callers MACS, SISSRs, GEM and PICS; (iii) peaks for the same factor and peak callers, but different experiment conditions (cell line, treatment, etc.), were merged into clusters; (iv) such clusters for different peak callers were merged into metaclusters that were considered as non-redundant sets of TFBSs. In addition to information on location in genome, the sets contain structured information about cell lines and experimental conditions extracted from descriptions of corresponding ChIP-seq experiments. A web interface to access GTRD was developed using the BioUML platform. It provides: (i) browsing and displaying information; (ii) advanced search possibilities, e.g. search of TFBSs near the specified gene or search of all genes potentially regulated by a specified transcription factor; (iii) integrated genome browser that provides visualization of the GTRD data: read alignments, peaks, clusters, metaclusters and information about gene structures from the Ensembl database and binding sites predicted using position weight matrices from the HOCOMOCO database.

PMID:
27924024
PMCID:
PMC5210645
DOI:
10.1093/nar/gkw951
[Indexed for MEDLINE]
Free PMC Article

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