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Open Access Research article

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

Victor G Levitsky12*, Ivan V Kulakovskiy34, Nikita I Ershov1, Dmitry Yu Oshchepkov1, Vsevolod J Makeev34, T C Hodgman5 and Tatyana I Merkulova12

Author Affiliations

1 Institute of Cytology and Genetics of the Siberian Division of Russian Academy of Sciences, Lavrentieva Prospect 10, Novosibirsk 630090, Russia

2 Novosibirsk State University, Pirogova 2, Novosibirsk 630090, Russia

3 Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Vavilova str. 32, Moscow 119991, Russia

4 Department of Computational Systems Biology, Vavilov Institute of General Genetics, Russian Academy of Sciences, Gubkina str. 3, Moscow 119991, Russia

5 Multidisciplinary Centre for Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington LE12 5RD, UK

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BMC Genomics 2014, 15:80  doi:10.1186/1471-2164-15-80

Published: 29 January 2014

Abstract

Background

ChIP-Seq is widely used to detect genomic segments bound by transcription factors (TF), either directly at DNA binding sites (BSs) or indirectly via other proteins. Currently, there are many software tools implementing different approaches to identify TFBSs within ChIP-Seq peaks. However, their use for the interpretation of ChIP-Seq data is usually complicated by the absence of direct experimental verification, making it difficult both to set a threshold to avoid recognition of too many false-positive BSs, and to compare the actual performance of different models.

Results

Using ChIP-Seq data for FoxA2 binding loci in mouse adult liver and human HepG2 cells we compared FoxA binding-site predictions for four computational models of two fundamental classes: pattern matching based on existing training set of experimentally confirmed TFBSs (oPWM and SiteGA) and de novo motif discovery (ChIPMunk and diChIPMunk). To properly select prediction thresholds for the models, we experimentally evaluated affinity of 64 predicted FoxA BSs using EMSA that allows safely distinguishing sequences able to bind TF. As a result we identified thousands of reliable FoxA BSs within ChIP-Seq loci from mouse liver and human HepG2 cells. It was found that the performance of conventional position weight matrix (PWM) models was inferior with the highest false positive rate. On the contrary, the best recognition efficiency was achieved by the combination of SiteGA & diChIPMunk/ChIPMunk models, properly identifying FoxA BSs in up to 90% of loci for both mouse and human ChIP-Seq datasets.

Conclusions

The experimental study of TF binding to oligonucleotides corresponding to predicted sites increases the reliability of computational methods for TFBS-recognition in ChIP-Seq data analysis. Regarding ChIP-Seq data interpretation, basic PWMs have inferior TFBS recognition quality compared to the more sophisticated SiteGA and de novo motif discovery methods. A combination of models from different principles allowed identification of proper TFBSs.

Keywords:
ChIP-Seq; EMSA; Transcription factor binding sites; FoxA; SiteGA; PWM; Transcription factor binding model; Dinucleotide frequencies