{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T06:18:41Z","timestamp":1772173121554,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1009921","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T00:00:00Z","timestamp":1663804800000}}],"reference-count":31,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T00:00:00Z","timestamp":1662940800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Determining transcriptional factor binding sites (TFBSs) is critical for understanding the molecular mechanisms regulating gene expression in different biological conditions. Biological assays designed to directly mapping TFBSs require large sample size and intensive resources. As an alternative, ATAC-seq assay is simple to conduct and provides genomic cleavage profiles that contain rich information for imputing TFBSs indirectly. Previous footprint-based tools are inheritably limited by the accuracy of their bias correction algorithms and the efficiency of their feature extraction models. Here we introduce TAMC (\n                    <jats:underline>\n                      <jats:bold>T<\/jats:bold>\n                    <\/jats:underline>\n                    ranscriptional factor binding prediction from\n                    <jats:underline>\n                      <jats:bold>A<\/jats:bold>\n                    <\/jats:underline>\n                    TAC-seq profile at\n                    <jats:underline>\n                      <jats:bold>M<\/jats:bold>\n                    <\/jats:underline>\n                    otif-predicted binding sites using\n                    <jats:underline>\n                      <jats:bold>C<\/jats:bold>\n                    <\/jats:underline>\n                    onvolutional neural networks), a deep-learning approach for predicting motif-centric TF binding activity from paired-end ATAC-seq data. TAMC does not require bias correction during signal processing. By leveraging a one-dimensional convolutional neural network (1D-CNN) model, TAMC make predictions based on both footprint and non-footprint features at binding sites for each TF and outperforms existing footprinting tools in TFBS prediction particularly for ATAC-seq data with limited sequencing depth.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1009921","type":"journal-article","created":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T13:46:38Z","timestamp":1662990398000},"page":"e1009921","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":7,"title":["TAMC: A deep-learning approach to predict motif-centric transcriptional factor binding activity based on ATAC-seq profile"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5358-9144","authenticated-orcid":true,"given":"Tianqi","family":"Yang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4980-845X","authenticated-orcid":true,"given":"Ricardo","family":"Henao","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2022,9,12]]},"reference":[{"issue":"9","key":"pcbi.1009921.ref001","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1038\/nrg3207","article-title":"Transcription factors: from enhancer binding to developmental control","volume":"13","author":"F Spitz","year":"2012","journal-title":"Nat Rev Genet"},{"issue":"5830","key":"pcbi.1009921.ref002","doi-asserted-by":"crossref","first-page":"1497","DOI":"10.1126\/science.1141319","article-title":"Genome-wide mapping of in vivo protein-DNA interactions","volume":"316","author":"DS Johnson","year":"2007","journal-title":"Science"},{"key":"pcbi.1009921.ref003","first-page":"6","article-title":"An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites","author":"PJ Skene","year":"2017","journal-title":"Elife"},{"issue":"23","key":"pcbi.1009921.ref004","doi-asserted-by":"crossref","first-page":"3181","DOI":"10.1093\/bioinformatics\/btp554","article-title":"MOODS: fast search for position weight matrix matches in DNA sequences","volume":"25","author":"J Korhonen","year":"2009","journal-title":"Bioinformatics"},{"issue":"7","key":"pcbi.1009921.ref005","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1093\/bioinformatics\/btr064","article-title":"FIMO: scanning for occurrences of a given motif","volume":"27","author":"CE Grant","year":"2011","journal-title":"Bioinformatics"},{"issue":"5","key":"pcbi.1009921.ref006","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1093\/bib\/bbp025","article-title":"Computational methods for the detection of cis-regulatory modules","volume":"10","author":"P Van Loo","year":"2009","journal-title":"Brief Bioinform"},{"issue":"4","key":"pcbi.1009921.ref007","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1038\/s41576-018-0089-8","article-title":"Chromatin accessibility and the regulatory epigenome","volume":"20","author":"SL Klemm","year":"2019","journal-title":"Nat Rev Genet"},{"issue":"9","key":"pcbi.1009921.ref008","doi-asserted-by":"crossref","first-page":"3157","DOI":"10.1093\/nar\/5.9.3157","article-title":"DNAse footprinting: a simple method for the detection of protein-DNA binding specificity","volume":"5","author":"DJ Galas","year":"1978","journal-title":"Nucleic Acids Res"},{"issue":"4","key":"pcbi.1009921.ref009","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1038\/nmeth.1313","article-title":"Global mapping of protein-DNA interactions in vivo by digital genomic footprinting","volume":"6","author":"JR Hesselberth","year":"2009","journal-title":"Nature Methods"},{"issue":"7","key":"pcbi.1009921.ref010","doi-asserted-by":"crossref","first-page":"956","DOI":"10.1093\/bioinformatics\/btw740","article-title":"DeFCoM: analysis and modeling of transcription factor binding sites using a motif-centric genomic footprinter","volume":"33","author":"B Quach","year":"2017","journal-title":"Bioinformatics"},{"issue":"17","key":"pcbi.1009921.ref011","doi-asserted-by":"crossref","first-page":"2852","DOI":"10.1093\/bioinformatics\/btv294","article-title":"BinDNase: a discriminatory approach for transcription factor binding prediction using DNase I hypersensitivity data","volume":"31","author":"J Kahara","year":"2015","journal-title":"Bioinformatics"},{"issue":"9","key":"pcbi.1009921.ref012","doi-asserted-by":"crossref","first-page":"e0138030","DOI":"10.1371\/journal.pone.0138030","article-title":"msCentipede: Modeling Heterogeneity across Genomic Sites and Replicates Improves Accuracy in the Inference of Transcription Factor Binding","volume":"10","author":"A Raj","year":"2015","journal-title":"Plos One"},{"issue":"12","key":"pcbi.1009921.ref013","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0187046","article-title":"DNase-capture reveals differential transcription factor binding modalities","volume":"12","author":"D Kang","year":"2017","journal-title":"Plos One"},{"issue":"3","key":"pcbi.1009921.ref014","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1101\/gr.112623.110","article-title":"Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data","volume":"21","author":"R Pique-Regi","year":"2011","journal-title":"Genome Res"},{"key":"pcbi.1009921.ref015","doi-asserted-by":"crossref","DOI":"10.1186\/s12864-015-2081-4","article-title":"Wellington-bootstrap: differential DNase-seq footprinting identifies cell-type determining transcription factors","volume":"16","author":"J Piper","year":"2015","journal-title":"Bmc Genomics"},{"key":"pcbi.1009921.ref016","article-title":"Identification of transcription factor binding sites using ATAC-seq","volume":"20","author":"ZJ Li","year":"2019","journal-title":"Genome Biol"},{"issue":"1","key":"pcbi.1009921.ref017","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-020-18035-1","article-title":"ATAC-seq footprinting unravels kinetics of transcription factor binding during zygotic genome activation","volume":"11","author":"M Bentsen","year":"2020","journal-title":"Nat Commun"},{"issue":"7","key":"pcbi.1009921.ref018","doi-asserted-by":"crossref","first-page":"1040","DOI":"10.1101\/gr.258228.119","article-title":"TRACE: transcription factor footprinting using chromatin accessibility data and DNA sequence","volume":"30","author":"NX Ouyang","year":"2020","journal-title":"Genome Res"},{"issue":"7414","key":"pcbi.1009921.ref019","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1038\/nature11212","article-title":"An expansive human regulatory lexicon encoded in transcription factor footprints","volume":"489","author":"S Neph","year":"2012","journal-title":"Nature"},{"issue":"3","key":"pcbi.1009921.ref020","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1038\/nmeth.3768","article-title":"Genomic footprinting","volume":"13","author":"J Vierstra","year":"2016","journal-title":"Nat Methods"},{"key":"pcbi.1009921.ref021","article-title":"Reproducible inference of transcription factor footprints in ATAC-seq and DNase-seq datasets using protocol-specific bias modeling","volume":"20","author":"AK Calviello","year":"2019","journal-title":"Genome Biol"},{"issue":"11","key":"pcbi.1009921.ref022","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.1038\/cr.2017.131","article-title":"Molecular mechanism of directional CTCF recognition of a diverse range of genomic sites","volume":"27","author":"M Yin","year":"2017","journal-title":"Cell Res"},{"issue":"2","key":"pcbi.1009921.ref023","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.molcel.2014.08.016","article-title":"DNase Footprint Signatures Are Dictated by Factor Dynamics and DNA Sequence","volume":"56","author":"MH Sung","year":"2014","journal-title":"Mol Cell"},{"issue":"7786","key":"pcbi.1009921.ref024","doi-asserted-by":"crossref","first-page":"306-+","DOI":"10.1038\/s41586-019-1812-0","article-title":"Key role for CTCF in establishing chromatin structure in human embryos","volume":"576","author":"XP Chen","year":"2019","journal-title":"Nature"},{"issue":"7704","key":"pcbi.1009921.ref025","doi-asserted-by":"crossref","first-page":"256-+","DOI":"10.1038\/s41586-018-0080-8","article-title":"Chromatin analysis in human early development reveals epigenetic transition during ZGA","volume":"557","author":"JY Wu","year":"2018","journal-title":"Nature"},{"issue":"1","key":"pcbi.1009921.ref026","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-019-13753-7","article-title":"Constitutively bound CTCF sites maintain 3D chromatin architecture and long-range epigenetically regulated domains","volume":"11","author":"A Khoury","year":"2020","journal-title":"Nat Commun"},{"issue":"4","key":"pcbi.1009921.ref027","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1093\/bioinformatics\/btq696","article-title":"Using non-uniform read distribution models to improve isoform expression inference in RNA-Seq","volume":"27","author":"ZP Wu","year":"2011","journal-title":"Bioinformatics"},{"issue":"4","key":"pcbi.1009921.ref028","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/nmeth.1923","article-title":"Fast gapped-read alignment with Bowtie 2","volume":"9","author":"B Langmead","year":"2012","journal-title":"Nature Methods"},{"issue":"16","key":"pcbi.1009921.ref029","doi-asserted-by":"crossref","first-page":"2078","DOI":"10.1093\/bioinformatics\/btp352","article-title":"The Sequence Alignment\/Map format and SAMtools","volume":"25","author":"H Li","year":"2009","journal-title":"Bioinformatics"},{"key":"pcbi.1009921.ref030","article-title":"Model-based Analysis of ChIP-Seq (MACS)","volume":"9","author":"9","year":"2008","journal-title":"Genome Biol"},{"key":"pcbi.1009921.ref031","article-title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","volume":"32","author":"A Paszke","year":"2019","journal-title":"Adv Neur In"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1009921","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T00:00:00Z","timestamp":1663804800000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1009921","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,18]],"date-time":"2023-02-18T15:47:53Z","timestamp":1676735273000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1009921"}},"subtitle":[],"editor":[{"given":"Saurabh","family":"Sinha","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,9,12]]},"references-count":31,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,9,12]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1009921","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2022.02.15.480482","asserted-by":"object"}]},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,12]]}}}