{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T05:45:43Z","timestamp":1769579143668,"version":"3.49.0"},"reference-count":21,"publisher":"World Scientific Pub Co Pte Lt","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Bioinform. Comput. Biol."],"published-print":{"date-parts":[[2013,2]]},"abstract":"<jats:p> Chromatin immunoprecipitation followed by deep sequencing (ChIP-Seq) became a method of choice to locate DNA segments bound by different regulatory proteins. ChIP-Seq produces extremely valuable information to study transcriptional regulation. The wet-lab workflow is often supported by downstream computational analysis including construction of models of nucleotide sequences of transcription factor binding sites in DNA, which can be used to detect binding sites in ChIP-Seq data at a single base pair resolution. The most popular TFBS model is represented by positional weight matrix (PWM) with statistically independent positional weights of nucleotides in different columns; such PWMs are constructed from a gapless multiple local alignment of sequences containing experimentally identified TFBSs. Modern high-throughput techniques, including ChIP-Seq, provide enough data for careful training of advanced models containing more parameters than PWM. Yet, many suggested multiparametric models often provide only incremental improvement of TFBS recognition quality comparing to traditional PWMs trained on ChIP-Seq data. We present a novel computational tool, diChIPMunk, that constructs TFBS models as optimal dinucleotide PWMs, thus accounting for correlations between nucleotides neighboring in input sequences. diChIPMunk utilizes many advantages of ChIPMunk, its ancestor algorithm, accounting for ChIP-Seq base coverage profiles (\"peak shape\") and using the effective subsampling-based core procedure which allows processing of large datasets. We demonstrate that diPWMs constructed by diChIPMunk outperform traditional PWMs constructed by ChIPMunk from the same ChIP-Seq data. Software website: http:\/\/autosome.ru\/dichipmunk\/ <\/jats:p>","DOI":"10.1142\/s0219720013400040","type":"journal-article","created":{"date-parts":[[2013,1,17]],"date-time":"2013-01-17T01:01:10Z","timestamp":1358384470000},"page":"1340004","source":"Crossref","is-referenced-by-count":56,"title":["FROM BINDING MOTIFS IN CHIP-SEQ DATA TO IMPROVED MODELS OF TRANSCRIPTION FACTOR BINDING SITES"],"prefix":"10.1142","volume":"11","author":[{"given":"IVAN","family":"KULAKOVSKIY","sequence":"first","affiliation":[{"name":"Laboratory of Bioinformatics and Systems Biology, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Vavilov Street 32, Moscow 119991, GSP-1, Russia"},{"name":"Department of Computational Systems Biology, Vavilov Institute of General Genetics, Russian Academy of Sciences, Gubkina Street 3, Moscow 119991, Russia"}]},{"given":"VICTOR","family":"LEVITSKY","sequence":"additional","affiliation":[{"name":"Laboratory of Molecular Genetics Systems, Institute of Cytology and Genetics of the Siberian Division of Russian, Academy of Sciences, Lavrentiev Prospect 6, Novosibirsk 630090, Russia"},{"name":"Faculty of Natural Sciences, Novosibirsk State University, Pirogova Street 2, Novosibirsk 630090, Russia"}]},{"given":"DMITRY","family":"OSHCHEPKOV","sequence":"additional","affiliation":[{"name":"Laboratory of Molecular Genetics Systems, Institute of Cytology and Genetics of the Siberian Division of Russian Academy of Sciences, Lavrentiev Prospect 6, Novosibirsk 630090, Russia"}]},{"given":"LEONID","family":"BRYZGALOV","sequence":"additional","affiliation":[{"name":"Laboratory of Regulation of Gene Expression, Institute of Cytology and Genetics of the Siberian Division of Russian Academy of Sciences, Lavrentiev Prospect 6, Novosibirsk 630090, Russia"}]},{"given":"ILYA","family":"VORONTSOV","sequence":"additional","affiliation":[{"name":"Department of Computational Systems Biology, Vavilov Institute of General Genetics, Russian Academy of Sciences, Gubkina Street 3, Moscow 119991, Russia"},{"name":"Yandex Data Analysis School, Data Analysis Department, Moscow Institute of Physics and Technology, Leo Tolstoy Street 16, Moscow 119021, Russia"}]},{"given":"VSEVOLOD","family":"MAKEEV","sequence":"additional","affiliation":[{"name":"Department of Computational Systems Biology, Vavilov Institute of General Genetics, Russian Academy of Sciences, Gubkina Street 3, Moscow 119991, Russia"},{"name":"State Research Institute of Genetics and Selection of Industrial Microorganisms, 1st Dorozhny proezd, 1 Moscow 117545, Russia"},{"name":"Moscow Institute of Physics and Technology, Institutskii per. 9, Dolgoprudny 141700, Moscow Region, Russia"}]}],"member":"219","published-online":{"date-parts":[[2013,2,21]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/16.1.16"},{"key":"rf2","doi-asserted-by":"publisher","DOI":"10.1038\/nrg1315"},{"key":"rf3","author":"Zambelli F.","year":"2012","journal-title":"Brief. 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