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This article is part of the supplement: Proceedings of the Tenth Annual Research in Computational Molecular Biology (RECOMB) Satellite Workshop on Comparative Genomics

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Exploiting ancestral mammalian genomes for the prediction of human transcription factor binding sites

Mathieu Blanchette

Author Affiliations

McGill Centre for Bioinformatics and School of Computer Science, McGill University, H3C 2B4, Québec, Canada

This work was carried out in part while on sabbatical at the Laboratoire d'Informatique, Robotique, et Microélectronique de Montpellier (Université Montpellier 2), 161 rue Ada, Montpellier, France

BMC Bioinformatics 2012, 13(Suppl 19):S2  doi:10.1186/1471-2105-13-S19-S2

Published: 19 December 2012



The computational prediction of Transcription Factor Binding Sites (TFBS) remains a challenge due to their short length and low information content. Comparative genomics approaches that simultaneously consider several related species and favor sites that have been conserved throughout evolution improve the accuracy (specificity) of the predictions but are limited due to a phenomenon called binding site turnover, where sequence evolution causes one TFBS to replace another in the same region. In parallel to this development, an increasing number of mammalian genomes are now sequenced and it is becoming possible to infer, to a surprisingly high degree of accuracy, ancestral mammalian sequences.


We propose a TFBS prediction approach that makes use of the availability of inferred ancestral mammalian genomes to improve its accuracy. This method aims to identify binding loci, which are regions of a few hundred base pairs that have preserved their potential to bind a given transcription factor over evolutionary time. After proposing a neutral evolutionary model of predicted TFBS counts in a DNA region of a given length, we use it to identify regions that have preserved the number of predicted TFBS they contain to an unexpected degree given their divergence. The approach is applied to human chromosome 1 and shows significant gains in accuracy as compared to both existing single-species and multi-species TFBS prediction approaches, in particular for transcription factors that are subject to high turnover rates.


The source code and predictions made by the program are available at webcite.