{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T05:46:39Z","timestamp":1775799999925,"version":"3.50.1"},"reference-count":74,"publisher":"Oxford University Press (OUP)","issue":"13","license":[{"start":{"date-parts":[[2018,6,27]],"date-time":"2018-06-27T00:00:00Z","timestamp":1530057600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Microbial communities play important roles in the function and maintenance of various biosystems, ranging from the human body to the environment. A major challenge in microbiome research is the classification of microbial communities of different environments or host phenotypes. The most common and cost-effective approach for such studies to date is 16S rRNA gene sequencing. Recent falls in sequencing costs have increased the demand for simple, efficient and accurate methods for rapid detection or diagnosis with proved applications in medicine, agriculture and forensic science. We describe a reference- and alignment-free approach for predicting environments and host phenotypes from 16S rRNA gene sequencing based on k-mer representations that benefits from a bootstrapping framework for investigating the sufficiency of shallow sub-samples. Deep learning methods as well as classical approaches were explored for predicting environments and host phenotypes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A k-mer distribution of shallow sub-samples outperformed Operational Taxonomic Unit (OTU) features in the tasks of body-site identification and Crohn\u2019s disease prediction. Aside from being more accurate, using k-mer features in shallow sub-samples allows (i) skipping computationally costly sequence alignments required in OTU-picking and (ii) provided a proof of concept for the sufficiency of shallow and short-length 16S rRNA sequencing for phenotype prediction. In addition, k-mer features predicted representative 16S rRNA gene sequences of 18 ecological environments, and 5 organismal environments with high macro-F1 scores of 0.88 and 0.87. For large datasets, deep learning outperformed classical methods such as Random Forest and Support Vector Machine.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The software and datasets are available at https:\/\/llp.berkeley.edu\/micropheno.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty296","type":"journal-article","created":{"date-parts":[[2018,4,15]],"date-time":"2018-04-15T13:20:04Z","timestamp":1523798404000},"page":"i32-i42","source":"Crossref","is-referenced-by-count":74,"title":["MicroPheno: predicting environments and host phenotypes from 16S rRNA gene sequencing using a k-mer based representation of shallow sub-samples"],"prefix":"10.1093","volume":"34","author":[{"given":"Ehsaneddin","family":"Asgari","sequence":"first","affiliation":[{"name":"Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, CA, USA"},{"name":"Computational Biology of Infection Research, Helmholtz Center for Infection Research, Braunschweig, Germany"}]},{"given":"Kiavash","family":"Garakani","sequence":"additional","affiliation":[{"name":"Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, CA, USA"}]},{"given":"Alice C","family":"McHardy","sequence":"additional","affiliation":[{"name":"Computational Biology of Infection Research, Helmholtz Center for Infection Research, Braunschweig, Germany"}]},{"given":"Mohammad R K","family":"Mofrad","sequence":"additional","affiliation":[{"name":"Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, CA, USA"},{"name":"Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Lab, Berkeley, CA, USA"}]}],"member":"286","published-online":{"date-parts":[[2018,6,27]]},"reference":[{"key":"2023051604242760900_bty296-B1","volume-title":"Science","author":"Ann Moran","year":"2015"},{"key":"2023051604242760900_bty296-B3","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1126\/science.aaa7378","article-title":"Structure and function of the global ocean microbiome","volume":"348","author":"Armbrust","year":"2015","journal-title":"Science"},{"key":"2023051604242760900_bty296-B4","doi-asserted-by":"crossref","DOI":"10.1126\/scitranslmed.aab2271","article-title":"Early infancy microbial and metabolic alterations affect risk of childhood asthma","volume":"7","author":"Arrieta","year":"2015","journal-title":"Sci. Transl. Med"},{"key":"2023051604242760900_bty296-B5","doi-asserted-by":"crossref","first-page":"e0141287","DOI":"10.1371\/journal.pone.0141287","article-title":"Continuous distributed representation of biological sequences for deep proteomics and genomics","volume":"10","author":"Asgari","year":"2015","journal-title":"PLoS One"},{"key":"2023051604242760900_bty296-B6","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation learning: a review and new perspectives","volume":"35","author":"Bengio","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"2023051604242760900_bty296-B7","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":". Mach. Learn"},{"key":"2023051604242760900_bty296-B8","doi-asserted-by":"crossref","first-page":"e1005518","DOI":"10.1371\/journal.pcbi.1005518","article-title":"ESPRIT-forest: parallel clustering of massive amplicon sequence data in subquadratic time","volume":"13","author":"Cai","year":"2017","journal-title":"PLoS Comput. Biol"},{"key":"2023051604242760900_bty296-B9","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1038\/nmeth.3869","article-title":"DADA2: high-resolution sample inference from Illumina amplicon data","volume":"13","author":"Callahan","year":"2016","journal-title":"Nature Methods"},{"key":"2023051604242760900_bty296-B10","first-page":"693","volume-title":"J. Clin. Gastroenterol.","author":"Cammarota","year":"2014"},{"key":"2023051604242760900_bty296-B11","first-page":"27","volume-title":"Host Phenotype Prediction from Differentially Abundant Microbes Using RoDEO","author":"Carrieri","year":"2017"},{"key":"2023051604242760900_bty296-B13","first-page":"489","volume-title":"Biol. Fertil. Soils","author":"Chaparro","year":"2012"},{"key":"2023051604242760900_bty296-B15","doi-asserted-by":"crossref","first-page":"e87126.","DOI":"10.1371\/journal.pone.0087126","article-title":"MetaMetaDB: a database and analytic system for investigating microbial habitability","volume":"9","author":"Chia Yang","year":"2014","journal-title":"PLoS One"},{"key":"2023051604242760900_bty296-B16","first-page":"260","volume-title":"Nat. Rev. Genet.","author":"Cho","year":"2012"},{"key":"2023051604242760900_bty296-B18","doi-asserted-by":"crossref","first-page":"9118","DOI":"10.1021\/acs.est.7b01518","article-title":"Predicting the ecological quality status of marine environments from eDNA metabarcoding data using supervised machine learning","volume":"51","author":"Cordier","year":"2017","journal-title":"Environ. Sci. Technol"},{"key":"2023051604242760900_bty296-B19","doi-asserted-by":"crossref","first-page":"1694","DOI":"10.1126\/science.1177486","article-title":"Bacterial community variation in human body habitats across space and time","volume":"326","author":"Costello","year":"2009","journal-title":"Science (New York, N.Y.)"},{"key":"2023051604242760900_bty296-B20","doi-asserted-by":"crossref","first-page":"641.","DOI":"10.1186\/1471-2164-14-641","article-title":"Alignment-free supervised classification of metagenomes by recursive SVM","volume":"14","author":"Cui","year":"2013","journal-title":"BMC Genomics"},{"key":"2023051604242760900_bty296-B22","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1109\/TNB.2015.2461219","article-title":"Multi-layer and recursive neural networks for metagenomic classification","volume":"14","author":"Ditzler","year":"2015","journal-title":"IEEE Trans. Nanobiosci"},{"key":"2023051604242760900_bty296-B23","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1093\/bfgp\/elt008","article-title":"Explaining microbial phenotypes on a genomic scale: GWAS for microbes","volume":"12","author":"Dutilh","year":"2013","journal-title":"Brief. Funct. Genomics"},{"key":"2023051604242760900_bty296-B24","doi-asserted-by":"crossref","first-page":"1784","DOI":"10.1038\/s41467-017-01973-8","article-title":"Meta-analysis of gut microbiome studies identifies disease-specific and shared responses","volume":"8","author":"Duvallet","year":"2017","journal-title":"Nat. Commun"},{"key":"2023051604242760900_bty296-B25","doi-asserted-by":"crossref","first-page":"1720","DOI":"10.1128\/JCM.00162-17","article-title":"Robust microbiota-based diagnostics for inflammatory bowel disease","volume":"55","author":"Eck","year":"2017","journal-title":"J. Clin. Microbiol"},{"key":"2023051604242760900_bty296-B26","doi-asserted-by":"crossref","first-page":"2194","DOI":"10.1093\/bioinformatics\/btr381","article-title":"UCHIME improves sensitivity and speed of chimera detection","volume":"27","author":"Edgar","year":"2011","journal-title":"Bioinformatics"},{"key":"2023051604242760900_bty296-B27","first-page":"579","volume-title":"Nat. Rev. Microbiol.","author":"Fierer","year":"2017"},{"key":"2023051604242760900_bty296-B28","doi-asserted-by":"crossref","first-page":"6477","DOI":"10.1073\/pnas.1000162107","article-title":"Forensic identification using skin bacterial communities","volume":"107","author":"Fierer","year":"2010","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"2023051604242760900_bty296-B29","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/j.chom.2014.02.005","article-title":"The treatment-naive microbiome in new-onset Crohn\u2019s disease","volume":"15","author":"Gevers","year":"2014","journal-title":"Cell Host Microbe"},{"key":"2023051604242760900_bty296-B31","doi-asserted-by":"crossref","first-page":"e1002020.","DOI":"10.1371\/journal.pbio.1002020","article-title":"Life in a world without microbes","volume":"12","author":"Gilbert","year":"2014","journal-title":"PLoS Biol"},{"key":"2023051604242760900_bty296-B32","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.chom.2017.06.006","article-title":"Cutaneous leishmaniasis induces a transmissible dysbiotic skin microbiota that promotes skin inflammation","volume":"22","author":"Gimblet","year":"2017","journal-title":"Cell Host Microbe"},{"key":"2023051604242760900_bty296-B33","volume-title":"Deep Learning","author":"Goodfellow","year":"2016"},{"key":"2023051604242760900_bty296-B34","first-page":"335","volume-title":"Nat. Methods","author":"Gregory Caporaso","year":"2010"},{"key":"2023051604242760900_bty296-B35","first-page":"1141","volume-title":"Genome Res.","author":"Hamady","year":"2009"},{"key":"2023051604242760900_bty296-B36","article-title":"Erratum to: stability of operational taxonomic units: an important but neglected property for analyzing microbial diversity","volume":"3","author":"He","year":"2015","journal-title":"Microbiome"},{"key":"2023051604242760900_bty296-B38","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1038\/nature11234","article-title":"Structure, function and diversity of the healthy human microbiome","volume":"486","author":"Huttenhower","year":"2012","journal-title":"Nature"},{"key":"2023051604242760900_bty296-B40","doi-asserted-by":"crossref","first-page":"2317","DOI":"10.1101\/gr.096651.109","article-title":"The NIH human microbiome project","volume":"19","author":"Jane","year":"2009","journal-title":"Genome Res"},{"key":"2023051604242760900_bty296-B42","first-page":"1","author":"Jolliffe","year":"1986"},{"key":"2023051604242760900_bty296-B43","doi-asserted-by":"crossref","first-page":"e01012","DOI":"10.1128\/mBio.01012-14","article-title":"Metatranscriptomics of the human oral microbiome during health and disease","volume":"5","author":"Jorth","year":"2014","journal-title":"mBio"},{"key":"2023051604242760900_bty296-B44","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1016\/j.cgh.2007.07.012","article-title":"The prevalence and geographic distribution of Crohn\u2019s disease and ulcerative colitis in the United States","volume":"5","author":"Kappelman","year":"2007","journal-title":"Clin. Gastroenterol. Hepatol"},{"key":"2023051604242760900_bty296-B45","doi-asserted-by":"crossref","first-page":"e0121453","DOI":"10.1371\/journal.pone.0121453","article-title":"CoMeta: classification of metagenomes using k-mers","volume":"10","author":"Kawulok","year":"2015","journal-title":"PLoS One"},{"key":"2023051604242760900_bty296-B46","first-page":"1","article-title":"Adam: a method for stochastic optimization","author":"Kingma","year":"2015","journal-title":"Int. Learn. Represent. 2015"},{"key":"2023051604242760900_bty296-B47","first-page":"343","volume-title":"FEMS Microbiol. Rev.","author":"Knights","year":"2011"},{"key":"2023051604242760900_bty296-B48","doi-asserted-by":"crossref","first-page":"5175","DOI":"10.1093\/nar\/gkt241","article-title":"Surprisingly extensive mixed phylogenetic and ecological signals among bacterial operational taxonomic units","volume":"41","author":"Koeppel","year":"2013","journal-title":"Nucleic Acids Res"},{"key":"2023051604242760900_bty296-B51","author":"Lawley","year":"2017"},{"key":"2023051604242760900_bty296-B53","first-page":"1","article-title":"Dysbiosis in chronic periodontitis: key microbial players and interactions with the human host","volume":"7","author":"Luo Deng","year":"2017","journal-title":"Sci. Rep"},{"key":"2023051604242760900_bty296-B54","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.1056\/NEJMra1600266","article-title":"The human intestinal microbiome in health and disease","volume":"375","author":"Lynch","year":"2016","journal-title":"N. Engl. J. Med"},{"key":"2023051604242760900_bty296-B55","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1093\/bioinformatics\/btr011","article-title":"A fast, lock-free approach for efficient parallel counting of occurrences of k-mers","volume":"27","author":"Mar\u00e7ais","year":"2011","journal-title":"Bioinformatics"},{"key":"2023051604242760900_bty296-B56","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1378\/chest.12-2854","article-title":"The airway microbiome and disease","volume":"144","author":"Marsland","year":"2013","journal-title":"Chest"},{"key":"2023051604242760900_bty296-B57","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1038\/ismej.2011.139","article-title":"An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea","volume":"6","author":"McDonald","year":"2012","journal-title":"ISME J"},{"key":"2023051604242760900_bty296-B58","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1038\/nmeth976","article-title":"Accurate phylogenetic classification of variable-length DNA fragments","volume":"4","author":"McHardy","year":"2007","journal-title":"Nat. Methods"},{"key":"2023051604242760900_bty296-B59","first-page":"1","article-title":"Kaiju: fast and sensitive taxonomic classification for metagenomics","volume":"7","author":"Menzel","year":"2015","journal-title":"bioRxiv"},{"key":"2023051604242760900_bty296-B60","first-page":"2761","volume-title":"J. Clin. Microbiol.","author":"Michael Janda","year":"2007"},{"key":"2023051604242760900_bty296-B61","doi-asserted-by":"crossref","first-page":"1799","DOI":"10.1002\/ibd.22860","article-title":"Alterations in the gut microbiome of children with severe ulcerative colitis","volume":"18","author":"Michail","year":"2012","journal-title":"Inflamm. Bowel Dis"},{"key":"2023051604242760900_bty296-B62","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbw068","article-title":"Deep learning in bioinformatics","author":"Min","year":"2016","journal-title":"Brief. Bioinformatics"},{"key":"2023051604242760900_bty296-B65","doi-asserted-by":"crossref","DOI":"10.1038\/npjbiofilms.2016.4","article-title":"A perspective on 16S rRNA operational taxonomic unit clustering using sequence similarity","volume":"2","author":"Nguyen","year":"2016","journal-title":"NPJ Biofilms Microbiomes"},{"key":"2023051604242760900_bty296-B66","author":"Olson","year":"2017"},{"key":"2023051604242760900_bty296-B69","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1136\/gutjnl-2016-313235","article-title":"A microbial signature for Crohn\u2019s disease","volume":"66","author":"Pascal","year":"2017","journal-title":"Gut"},{"key":"2023051604242760900_bty296-B70","doi-asserted-by":"crossref","first-page":"e1004977","DOI":"10.1371\/journal.pcbi.1004977","article-title":"Machine learning meta-analysis of large metagenomic datasets: tools and biological insights","volume":"12","author":"Pasolli","year":"2016","journal-title":"PLoS Comput. Biol"},{"key":"2023051604242760900_bty296-B71","first-page":"191","volume-title":"Nat. Methods","author":"Patil","year":"2011"},{"key":"2023051604242760900_bty296-B72","first-page":"2825","volume-title":"J. Mach. Learn. Res.","author":"Pedregosa","year":"2011"},{"key":"2023051604242760900_bty296-B74","doi-asserted-by":"crossref","first-page":"8851","DOI":"10.1021\/es302042t","article-title":"Bacterial community structure in the drinking water microbiome is governed by filtration processes","volume":"46","author":"Pinto","year":"2012","journal-title":"Environ. Sci. Technol"},{"key":"2023051604242760900_bty296-B76","doi-asserted-by":"crossref","DOI":"10.1128\/AEM.02627-17","article-title":"The madness of microbiome: attempting to find consensus \u2018best practice\u2019 for 16S microbiome studies","author":"Pollock","year":"2018","journal-title":"Appl. Environ. Microbiol"},{"key":"2023051604242760900_bty296-B77","doi-asserted-by":"crossref","first-page":"D590","DOI":"10.1093\/nar\/gks1219","article-title":"The SILVA ribosomal RNA gene database project: improved data processing and web-based tools","volume":"41","author":"Quast","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2023051604242760900_bty296-B78","doi-asserted-by":"crossref","first-page":"657.","DOI":"10.1681\/ASN.2013080905","article-title":"The gut microbiome, kidney disease, and targeted interventions","volume":"25","author":"Ramezani","year":"2014","journal-title":"J. Am. Soc. Nephrol"},{"key":"2023051604242760900_bty296-B79","doi-asserted-by":"crossref","first-page":"1241214","DOI":"10.1126\/science.1241214","article-title":"Gut microbiota from twins discordant for obesity modulate metabolism in mice","volume":"341","author":"Ridaura","year":"2013","journal-title":"Science"},{"key":"2023051604242760900_bty296-B80","doi-asserted-by":"crossref","first-page":"e545","DOI":"10.7717\/peerj.545","article-title":"Subsampled open-reference clustering creates consistent, comprehensive OTU definitions and scales to billions of sequences","volume":"2","author":"Rideout","year":"2014","journal-title":"Peer J"},{"key":"2023051604242760900_bty296-B81","doi-asserted-by":"crossref","first-page":"e73056","DOI":"10.1371\/journal.pone.0073056","article-title":"Metagenomic predictions: from microbiome to complex health and environmental phenotypes in humans and cattle","volume":"8","author":"Ross","year":"2013","journal-title":"PLoS One"},{"key":"2023051604242760900_bty296-B83","doi-asserted-by":"crossref","first-page":"1782","DOI":"10.1053\/j.gastro.2011.06.072","article-title":"Gastrointestinal microbiome signatures of pediatric patients with irritable bowel syndrome","volume":"141","author":"Saulnier","year":"2011","journal-title":"Gastroenterology"},{"key":"2023051604242760900_bty296-B84","doi-asserted-by":"crossref","first-page":"7537","DOI":"10.1128\/AEM.01541-09","article-title":"Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities","volume":"75","author":"Schloss","year":"2009","journal-title":"Appl. Environ. Microbiol"},{"key":"2023051604242760900_bty296-B85","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.fsigen.2017.10.004","article-title":"Targeted sequencing of clade-specific markers from skin microbiomes for forensic human identification","volume":"32","author":"Schmedes","year":"2018","journal-title":"Forensic Sci. Int.: Genetics"},{"key":"2023051604242760900_bty296-B87","first-page":"1929","article-title":"Dropout: prevent NN from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J Mach. Learn. Res"},{"key":"2023051604242760900_bty296-B88","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/2049-2618-1-11","article-title":"A comprehensive evaluation of multicategory classification methods for microbiomic data","volume":"1","author":"Statnikov","year":"2013","journal-title":"Microbiome"},{"key":"2023051604242760900_bty296-B89","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1023\/A:1018628609742","article-title":"Least squares support vector machine classifiers","volume":"9","author":"Suykens","year":"1999","journal-title":"Neural Process. Lett"},{"key":"2023051604242760900_bty296-B90","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.chom.2008.02.015","article-title":"Diet-induced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome","volume":"3","author":"Turnbaugh","year":"2008","journal-title":"Cell Host Microbe"},{"key":"2023051604242760900_bty296-B92","first-page":"2579","article-title":"Visualizing high-dimensional data using t-SNE","volume":"9","author":"Van Der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res"},{"key":"2023051604242760900_bty296-B93","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1093\/bioinformatics\/btv683","article-title":"Large-scale machine learning for metagenomics sequence classification","volume":"32","author":"Vervier","year":"2016","journal-title":"Bioinformatics"},{"key":"2023051604242760900_bty296-B95","doi-asserted-by":"crossref","first-page":"R46.","DOI":"10.1186\/gb-2014-15-3-r46","article-title":"Kraken: ultrafast metagenomic sequence classification using exact alignments","volume":"15","author":"Wood","year":"2014","journal-title":"Genome Biol"},{"key":"2023051604242760900_bty296-B96","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1007\/s41048-016-0033-4","article-title":"Metadp: a comprehensive web server for disease prediction of 16s rRNA metagenomic datasets","volume":"2","author":"Xu","year":"2016","journal-title":"Biophys. Rep"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/34\/13\/i32\/50316082\/bioinformatics_34_13_i32.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/34\/13\/i32\/50316082\/bioinformatics_34_13_i32.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T00:27:27Z","timestamp":1684196847000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/34\/13\/i32\/5045790"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,27]]},"references-count":74,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2018,7,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/bty296","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/255018","asserted-by":"object"}]},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2018,7,1]]},"published":{"date-parts":[[2018,6,27]]}}}