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 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.
Sharov
Alexei A
AA
Developmental Genomics and Aging Section, Laboratory of Genetics, National Institute on Aging, NIH, Baltimore, MD 21224, USA.
Ko
Minoru S H
MS
D000465:Algorithms; D020816:Amino Acid Motifs; D000818:Animals; D001483:Base Sequence; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods; D053595:Embryonic Stem Cells / Q000378:metabolism*; D039904:Pluripotent Stem Cells / Q000378:metabolism*; D056510:Position-Specific Scoring Matrices; D012984:Software; D014157:Transcription Factors / Q000378:metabolism*
D014157:Transcription Factors