{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T04:21:50Z","timestamp":1744863710701},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T00:00:00Z","timestamp":1711411200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T00:00:00Z","timestamp":1711411200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Coordination for the Improvement of Higher Education Personnel (CAPES) - Program of Academic Excellence","award":["Finance Code 001","Finance Code 001"],"award-info":[{"award-number":["Finance Code 001","Finance Code 001"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Discovery biological motifs plays a fundamental role in understanding regulatory mechanisms. Computationally, they can be efficiently represented as <jats:italic>kmers<\/jats:italic>, making the counting of these elements a critical aspect for ensuring not only the accuracy but also the efficiency of the analytical process. This is particularly useful in scenarios involving large data volumes, such as those generated by the <jats:italic>ChIP-seq<\/jats:italic> protocol. Against this backdrop, we introduce <jats:sc>biomapp::chip<\/jats:sc>, a tool specifically designed to optimize the discovery of biological motifs in large data volumes.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We conducted a comprehensive set of comparative tests with state-of-the-art algorithms. Our analyses revealed that <jats:sc>biomapp::chip<\/jats:sc> outperforms existing approaches in various metrics, excelling both in terms of performance and accuracy. The tests demonstrated a higher detection rate of significant motifs and also greater agility in the execution of the algorithm. Furthermore, the <jats:sc>smt<\/jats:sc> component played a vital role in the system\u2019s efficiency, proving to be both agile and accurate in <jats:italic>kmer<\/jats:italic> counting, which in turn improved the overall efficacy of our tool.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p><jats:sc>biomapp::chip<\/jats:sc> represent real advancements in the discovery of biological motifs, particularly in large data volume scenarios, offering a relevant alternative for the analysis of <jats:italic>ChIP-seq<\/jats:italic> data and have the potential to boost future research in the field. This software can be found at the following address: (https:\/\/github.com\/jadermcg\/biomapp-chip).<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-024-05752-3","type":"journal-article","created":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T03:02:03Z","timestamp":1711422123000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["biomapp::chip: large-scale motif analysis"],"prefix":"10.1186","volume":"25","author":[{"given":"Jader M. Caldonazzo","family":"Garbelini","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danilo S.","family":"Sanches","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aurora T. 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