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BMC Bioinformatics

pp 1–12

Transcriptome analysis


BackgroundBinding of transcription factors to transcription factor binding sites TFBSs is key to the mediation of transcriptional regulation. Information on experimentally validated functional TFBSs is limited and consequently there is a need for accurate prediction of TFBSs for gene annotation and in applications such as evaluating the effects of single nucleotide variations in causing disease. TFBSs are generally recognized by scanning a position weight matrix PWM against DNA using one of a number of available computer programs. Thus we set out to evaluate the best tools that can be used locally and are therefore suitable for large-scale analyses for creating PWMs from high-throughput ChIP-Seq data and for scanning them against DNA.

ResultsWe evaluated a set of de novo motif discovery tools that could be downloaded and installed locally using ENCODE-ChIP-Seq data and showed that rGADEM was the best-performing tool. TFBS prediction tools used to scan PWMs against DNA fall into two classes — those that predict individual TFBSs and those that identify clusters. Our evaluation showed that FIMO and MCAST performed best respectively.

ConclusionsSelection of the best-performing tools for generating PWMs from ChIP-Seq data and for scanning PWMs against DNA has the potential to improve prediction of precise transcription factor binding sites within regions identified by ChIP-Seq experiments for gene finding, understanding regulation and in evaluating the effects of single nucleotide variations in causing disease.

KeywordsPWMs Motif discovery Performance evaluation Motif scanning tools AbbreviationsChIPChromatin immunoprecipitation

EMSAElectro-mobility shift assay

PWMPosition weight matrix

SNVSingle nucleotide variant

SELEXSystematic evolution of ligands by exponential enrichment

TFBSTranscription factor binding site

Electronic supplementary materialThe online version of this article doi:10.1186-s12859-016-1298-9 contains supplementary material, which is available to authorized users.

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Autor: Narayan Jayaram - Daniel Usvyat - Andrew C. R. Martin

Fuente: https://link.springer.com/

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