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Exhaustive search for over-represented DNA sequence motifs with CisFinder.

ABSTRACT: We present CisFinder software, which generates a comprehensive list of motifs enriched in a set of DNA sequences and describes them with position frequency matrices (PFMs). A new algorithm was designed to estimate PFMs directly from counts of n-mer words with and without gaps; then PFMs are extended over gaps and flanking regions and clustered to generate non-redundant sets of motifs. The algorithm successfully identified binding motifs for 12 transcription factors (TFs) in embryonic stem cells based on published chromatin immunoprecipitation sequencing data. Furthermore, CisFinder successfully identified alternative binding motifs of TFs (e.g. POU5F1, ESRRB, and CTCF) and motifs for known and unknown co-factors of genes associated with the pluripotent state of ES cells. CisFinder also showed robust performance in the identification of motifs that were only slightly enriched in a set of DNA sequences.

SUBMITTER: Sharov AA

PROVIDER: S-EPMC2762409 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature

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Exhaustive search for over-represented DNA sequence motifs with CisFinder.

Sharov Alexei A AA Ko Minoru S H MS

DNA research : an international journal for rapid publication of reports on genes and genomes 20090909 5

We present CisFinder software, which generates a comprehensive list of motifs enriched in a set of DNA sequences and describes them with position frequency matrices (PFMs). A new algorithm was designed to estimate PFMs directly from counts of n-mer words with and without gaps; then PFMs are extended over gaps and flanking regions and clustered to generate non-redundant sets of motifs. The algorithm successfully identified binding motifs for 12 transcription factors (TFs) in embryonic stem cells ...[more]

PMID: 19740934

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Project description:BackgroundShort linear motifs (SLiMs) in proteins are functional microdomains of fundamental importance in many biological systems. SLiMs typically consist of a 3 to 10 amino acid stretch of the primary protein sequence, of which as few as two sites may be important for activity, making identification of novel SLiMs extremely difficult. In particular, it can be very difficult to distinguish a randomly recurring "motif" from a truly over-represented one. Incorporating ambiguous amino acid positions and/or variable-length wildcard spacers between defined residues further complicates the matter.Methodology/principal findingsIn this paper we present two algorithms. SLiMBuild identifies convergently evolved, short motifs in a dataset of proteins. Motifs are built by combining dimers into longer patterns, retaining only those motifs occurring in a sufficient number of unrelated proteins. Motifs with fixed amino acid positions are identified and then combined to incorporate amino acid ambiguity and variable-length wildcard spacers. The algorithm is computationally efficient compared to alternatives, particularly when datasets include homologous proteins, and provides great flexibility in the nature of motifs returned. The SLiMChance algorithm estimates the probability of returned motifs arising by chance, correcting for the size and composition of the dataset, and assigns a significance value to each motif. These algorithms are implemented in a software package, SLiMFinder. SLiMFinder default settings identify known SLiMs with 100% specificity, and have a low false discovery rate on random test data.Conclusions/significanceThe efficiency of SLiMBuild and low false discovery rate of SLiMChance make SLiMFinder highly suited to high throughput motif discovery and individual high quality analyses alike. Examples of such analyses on real biological data, and how SLiMFinder results can help direct future discoveries, are provided. SLiMFinder is freely available for download under a GNU license from http://bioinformatics.ucd.ie/shields/software/slimfinder/.

Dataset Information

Exhaustive search for over-represented DNA sequence motifs with CisFinder.

Publications

Exhaustive search for over-represented DNA sequence motifs with CisFinder.

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