Exhaustive Search for Over-represented DNA Sequence Motifs with CisFinder

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...

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Vydáno v:	DNA research Ročník 16; číslo 5; s. 261 - 273
Hlavní autoři:	Sharov, Alexei A., Ko, Minoru S.H.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford Oxford University Press 01.10.2009
Témata:	Algorithms Amino Acid Motifs Animals Base Sequence Binding Sites Biological and medical sciences Computational Biology - methods Embryonic Stem Cells - metabolism Fundamental and applied biological sciences. Psychology Genes. Genome Genetics of eukaryotes. Biological and molecular evolution Molecular and cellular biology Molecular genetics Pluripotent Stem Cells - metabolism Position-Specific Scoring Matrices Software Transcription Factors - metabolism ChIP-seq transcription factor binding site software embryonic stem cells algorithm Nucleotide sequence Embryonic cell Stem cell Software Binding site Transcription factor Chip Algorithm
ISSN:	1340-2838, 1756-1663, 1756-1663
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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.
AbstractList	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.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. 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.
Author	Sharov, Alexei A. Ko, Minoru S.H.
AuthorAffiliation	Developmental Genomics and Aging Section, Laboratory of Genetics , National Institute on Aging, NIH , Baltimore, MD 21224 , USA
AuthorAffiliation_xml	– name: Developmental Genomics and Aging Section, Laboratory of Genetics , National Institute on Aging, NIH , Baltimore, MD 21224 , USA
Author_xml	– sequence: 1 givenname: Alexei A. surname: Sharov fullname: Sharov, Alexei A. organization: National Institute on Aging, NIH – sequence: 2 givenname: Minoru S.H. surname: Ko fullname: Ko, Minoru S.H. email: kom@mail.nih.gov organization: National Institute on Aging, NIH
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Snippet	We present CisFinder software, which generates a comprehensive list of motifs enriched in a set of DNA sequences and describes them with position frequency...
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SubjectTerms	Algorithms Amino Acid Motifs Animals Base Sequence Binding Sites Biological and medical sciences Computational Biology - methods Embryonic Stem Cells - metabolism Fundamental and applied biological sciences. Psychology Genes. Genome Genetics of eukaryotes. Biological and molecular evolution Molecular and cellular biology Molecular genetics Pluripotent Stem Cells - metabolism Position-Specific Scoring Matrices Software Transcription Factors - metabolism
Title	Exhaustive Search for Over-represented DNA Sequence Motifs with CisFinder
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