Literature DB >> 17627963

Combined experimental and computational approaches to study the regulatory elements in eukaryotic genes.

Nikolay A Kolchanov1, Tatyana I Merkulova, Elena V Ignatieva, Elena A Ananko, Dmitry Yu Oshchepkov, Viktor G Levitsky, Gennady V Vasiliev, Nataly V Klimova, Vasily M Merkulov, T Charles Hodgman.   

Abstract

The recognition of transcription factor binding sites (TFBSs) is the first step on the way to deciphering the DNA regulatory code. There is a large variety of experimental approaches providing information on TFBS location in genomic sequences. Many computational approaches to TFBS recognition based on the experimental data obtained are available, each having its own advantages and shortcomings. This article provides short review of approaches to computational recognition of TFBS in genomic sequences and methods of experimental verification of predicted sites. We also present a case study of the interplay between experimental and theoretical approaches to the successful prediction of Steroidogenic Factor 1 (SF1).

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Year:  2007        PMID: 17627963     DOI: 10.1093/bib/bbm027

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  9 in total

1.  A model of genetic search for beneficial mutations: estimating the constructive capacities of mutagenesis.

Authors:  Grigory G Ananko
Journal:  J Mol Evol       Date:  2012-01-03       Impact factor: 2.395

2.  An integrative computational approach to effectively guide experimental identification of regulatory elements in promoters.

Authors:  Igor V Deyneko; Siegfried Weiss; Sara Leschner
Journal:  BMC Bioinformatics       Date:  2012-08-16       Impact factor: 3.169

3.  Identifying functional transcription factor binding sites in yeast by considering their positional preference in the promoters.

Authors:  Fu-Jou Lai; Chia-Chun Chiu; Tzu-Hsien Yang; Yueh-Min Huang; Wei-Sheng Wu
Journal:  PLoS One       Date:  2013-12-26       Impact factor: 3.240

4.  Detection of regulatory SNPs in human genome using ChIP-seq ENCODE data.

Authors:  Leonid O Bryzgalov; Elena V Antontseva; Marina Yu Matveeva; Alexander G Shilov; Elena V Kashina; Viatcheslav A Mordvinov; Tatyana I Merkulova
Journal:  PLoS One       Date:  2013-10-29       Impact factor: 3.240

5.  Targets of the Entamoeba histolytica transcription factor URE3-BP.

Authors:  Carol A Gilchrist; Duza J Baba; Yan Zhang; Oswald Crasta; Clive Evans; Elisabet Caler; Bruno W S Sobral; Christina B Bousquet; Megan Leo; Ameilia Hochreiter; Sarah K Connell; Barbara J Mann; William A Petri
Journal:  PLoS Negl Trop Dis       Date:  2008-08-27

6.  Human Genes Encoding Transcription Factors and Chromatin-Modifying Proteins Have Low Levels of Promoter Polymorphism: A Study of 1000 Genomes Project Data.

Authors:  Elena V Ignatieva; Victor G Levitsky; Nikolay A Kolchanov
Journal:  Int J Genomics       Date:  2015-08-31       Impact factor: 2.326

Review 7.  In silico promoters: modelling of cis-regulatory context facilitates target predictio.

Authors:  Mauritz Venter; Louise Warnich
Journal:  J Cell Mol Med       Date:  2008-05-24       Impact factor: 5.310

8.  Application of experimentally verified transcription factor binding sites models for computational analysis of ChIP-Seq data.

Authors:  Victor G Levitsky; Ivan V Kulakovskiy; Nikita I Ershov; Dmitry Yu Oshchepkov; Vsevolod J Makeev; T C Hodgman; Tatyana I Merkulova
Journal:  BMC Genomics       Date:  2014-01-29       Impact factor: 3.969

9.  A single ChIP-seq dataset is sufficient for comprehensive analysis of motifs co-occurrence with MCOT package.

Authors:  Victor Levitsky; Elena Zemlyanskaya; Dmitry Oshchepkov; Olga Podkolodnaya; Elena Ignatieva; Ivo Grosse; Victoria Mironova; Tatyana Merkulova
Journal:  Nucleic Acids Res       Date:  2019-12-02       Impact factor: 16.971

  9 in total

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