{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T14:19:26Z","timestamp":1754144366854,"version":"3.41.2"},"reference-count":20,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"vor","delay-in-days":14,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R35-GM128938"],"award-info":[{"award-number":["R35-GM128938"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Replication timing (RT) refers to the order in which DNA loci are replicated during S phase. RT is cell-type specific and implicated in cellular processes including transcription, differentiation, and disease. RT is typically quantified genome-wide using two-fraction assays (e.g. Repli-Seq) which sort cells into early and late S phase fractions followed by DNA sequencing, yielding a ratio as the RT signal. While two-fraction RT data are widely available in multiple cell lines, it is limited in its ability to capture high-resolution RT features. To address this, high-resolution Repli-Seq, which quantifies RT across 16 fractions, was developed, but it is costly and technically challenging with very limited data generated to date.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Here, we developed Soffritto, a deep learning model that predicts high-resolution RT data using two-fraction RT data, histone ChIP-seq data, GC content, and gene density as input. Soffritto is composed of a Long Short-Term Memory (LSTM) module and a prediction module. The LSTM module learns long- and short-range interactions between genomic bins, while the prediction module is composed of a fully connected layer that outputs a 16-fraction probability vector for each bin using the LSTM module\u2019s embeddings as input. By performing both within cell line and cross-cell line training and testing for five human and mouse cell lines, we show that Soffritto is able to capture experimental 16-fraction RT signals with high accuracy, and the predicted signals allow detection of high-resolution RT patterns.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Soffritto is available at https:\/\/github.com\/ay-lab\/Soffritto.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf231","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T13:03:28Z","timestamp":1752584608000},"page":"i580-i589","source":"Crossref","is-referenced-by-count":0,"title":["Soffritto: a deep learning model for predicting high-resolution replication timing"],"prefix":"10.1093","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1004-4213","authenticated-orcid":false,"given":"Dante","family":"Bolzan","sequence":"first","affiliation":[{"name":"Center for Autoimmunity and Inflammation, La Jolla Institute for Immunology , La Jolla, CA 92037,","place":["United States"]},{"name":"Bioinformatics and Systems Biology PhD Program, University of California, San Diego , La Jolla, CA 92093,","place":["United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0708-6914","authenticated-orcid":false,"given":"Ferhat","family":"Ay","sequence":"additional","affiliation":[{"name":"Center for Autoimmunity and Inflammation, La Jolla Institute for Immunology , La Jolla, CA 92037,","place":["United States"]},{"name":"Bioinformatics and Systems Biology PhD Program, University of California, San Diego , La Jolla, CA 92093,","place":["United States"]},{"name":"Department of Pediatrics, University of California, San Diego , La Jolla, CA 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