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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| Published in: | DNA research Vol. 16; no. 5; pp. 261 - 273 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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01.10.2009
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| 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. |
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| 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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| Keywords | ChIP-seq transcription factor binding site software embryonic stem cells algorithm Nucleotide sequence Embryonic cell Stem cell Software Binding site Transcription factor Chip Algorithm |
| Language | English |
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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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