{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T16:45:50Z","timestamp":1778345150107,"version":"3.51.4"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"S4","license":[{"start":{"date-parts":[[2022,4,15]],"date-time":"2022-04-15T00:00:00Z","timestamp":1649980800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,4,15]],"date-time":"2022-04-15T00:00:00Z","timestamp":1649980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST108-2628-E-038-002-MY3"],"award-info":[{"award-number":["MOST108-2628-E-038-002-MY3"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST110-2221-E-038-019-MY3"],"award-info":[{"award-number":["MOST110-2221-E-038-019-MY3"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"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>Predicting which pathogens might exhibit antimicrobial resistance (AMR) based on genomics data is one of the promising ways to swiftly and precisely identify AMR pathogens. Currently, the most widely used genomics approach is through identifying known AMR genes from genomic information in order to predict whether a pathogen might be resistant to certain antibiotic drugs. The list of known AMR genes, however, is still far from comprehensive and may result in inaccurate AMR pathogen predictions. We thus felt the need to expand the AMR gene set and proposed a pan-genome-based feature selection method to identify potential gene sets for AMR prediction purposes.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>By building pan-genome datasets and extracting gene presence\/absence patterns from four bacterial species, each with more than 2000 strains, we showed that machine learning models built from pan-genome data can be very promising for predicting AMR pathogens. The gene set selected by the eXtreme Gradient Boosting (XGBoost) feature selection approach further improved prediction outcomes, and an incremental approach selecting subsets of XGBoost-selected features brought the machine learning model performance to the next level. Investigating selected gene sets revealed that on average about 50% of genes had no known function and very few of them were known AMR genes, indicating the potential of the selected gene sets to expand resistance gene repertoires.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>We demonstrated that a pan-genome-based feature selection approach is suitable for building machine learning models for predicting AMR pathogens. The extracted gene sets may provide future clues to expand our knowledge of known AMR genes and provide novel hypotheses for inferring bacterial AMR mechanisms.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-022-04666-2","type":"journal-article","created":{"date-parts":[[2022,4,15]],"date-time":"2022-04-15T05:02:42Z","timestamp":1649998962000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Enhancing predictions of antimicrobial resistance of pathogens by expanding the potential resistance gene repertoire using a pan-genome-based feature selection approach"],"prefix":"10.1186","volume":"23","author":[{"given":"Ming-Ren","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5603-1194","authenticated-orcid":false,"given":"Yu-Wei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,4,15]]},"reference":[{"issue":"4","key":"4666_CR1","first-page":"277","volume":"40","author":"CL Ventola","year":"2015","unstructured":"Ventola CL. The antibiotic resistance crisis: part 1: causes and threats. P T. 2015;40(4):277\u201383.","journal-title":"P T"},{"key":"4666_CR2","doi-asserted-by":"publisher","first-page":"f1493","DOI":"10.1136\/bmj.f1493","volume":"346","author":"R Smith","year":"2013","unstructured":"Smith R, Coast J. The true cost of antimicrobial resistance. BMJ. 2013;346:f1493.","journal-title":"BMJ"},{"key":"4666_CR3","doi-asserted-by":"crossref","unstructured":"Roope LSJ, Smith RD, Pouwels KB, Buchanan J, Abel L, Eibich P, Butler CC, Tan PS, Walker AS, Robotham JV et al. The challenge of antimicrobial resistance: What economics can contribute. Science. 2019;364(6435):eaau4679.","DOI":"10.1126\/science.aau4679"},{"issue":"10","key":"4666_CR4","doi-asserted-by":"publisher","first-page":"2234","DOI":"10.1093\/jac\/dkt180","volume":"68","author":"N Stoesser","year":"2013","unstructured":"Stoesser N, Batty EM, Eyre DW, Morgan M, Wyllie DH, Del Ojo EC, Johnson JR, Walker AS, Peto TE, Crook DW. Predicting antimicrobial susceptibilities for Escherichia coli and Klebsiella pneumoniae isolates using whole genomic sequence data. J Antimicrob Chemother. 2013;68(10):2234\u201344.","journal-title":"J Antimicrob Chemother"},{"issue":"4","key":"4666_CR5","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.1128\/JCM.03117-13","volume":"52","author":"NC Gordon","year":"2014","unstructured":"Gordon NC, Price JR, Cole K, Everitt R, Morgan M, Finney J, Kearns AM, Pichon B, Young B, Wilson DJ, et al. Prediction of Staphylococcus aureus antimicrobial resistance by whole-genome sequencing. J Clin Microbiol. 2014;52(4):1182\u201391.","journal-title":"J Clin Microbiol"},{"key":"4666_CR6","doi-asserted-by":"crossref","unstructured":"Jeukens J, Kukavica-Ibrulj I, Emond-Rheault JG, Freschi L, Levesque RC. Comparative genomics of a drug-resistant Pseudomonas aeruginosa panel and the challenges of antimicrobial resistance prediction from genomes. FEMS Microbiol Lett. 2017;364(18):fnx161.","DOI":"10.1093\/femsle\/fnx161"},{"key":"4666_CR7","doi-asserted-by":"publisher","first-page":"592","DOI":"10.3389\/fmicb.2018.00592","volume":"9","author":"S Neuert","year":"2018","unstructured":"Neuert S, Nair S, Day MR, Doumith M, Ashton PM, Mellor KC, Jenkins C, Hopkins KL, Woodford N, de Pinna E, et al. Prediction of phenotypic antimicrobial resistance profiles from whole genome sequences of non-typhoidal Salmonella enterica. Front Microbiol. 2018;9:592.","journal-title":"Front Microbiol"},{"key":"4666_CR8","doi-asserted-by":"publisher","first-page":"27930","DOI":"10.1038\/srep27930","volume":"6","author":"JJ Davis","year":"2016","unstructured":"Davis JJ, Boisvert S, Brettin T, Kenyon RW, Mao C, Olson R, Overbeek R, Santerre J, Shukla M, Wattam AR, et al. Antimicrobial resistance prediction in PATRIC and RAST. Sci Rep. 2016;6:27930.","journal-title":"Sci Rep"},{"issue":"1","key":"4666_CR9","doi-asserted-by":"publisher","first-page":"4071","DOI":"10.1038\/s41598-019-40561-2","volume":"9","author":"A Drouin","year":"2019","unstructured":"Drouin A, Letarte G, Raymond F, Marchand M, Corbeil J, Laviolette F. Interpretable genotype-to-phenotype classifiers with performance guarantees. Sci Rep. 2019;9(1):4071.","journal-title":"Sci Rep"},{"key":"4666_CR10","doi-asserted-by":"crossref","unstructured":"Nguyen M, Long SW, McDermott PF, Olsen RJ, Olson R, Stevens RL, Tyson GH, Zhao S, Davis JJ. Using machine learning to predict antimicrobial MICs and associated genomic features for nontyphoidal Salmonella. J Clin Microbiol. 2019;57(2):e01260\u201318.","DOI":"10.1128\/JCM.01260-18"},{"issue":"1","key":"4666_CR11","doi-asserted-by":"publisher","first-page":"11033","DOI":"10.1038\/s41598-020-67949-9","volume":"10","author":"AS Chowdhury","year":"2020","unstructured":"Chowdhury AS, Call DR, Broschat SL. PARGT: a software tool for predicting antimicrobial resistance in bacteria. Sci Rep. 2020;10(1):11033.","journal-title":"Sci Rep"},{"key":"4666_CR12","doi-asserted-by":"publisher","first-page":"10063","DOI":"10.1038\/ncomms10063","volume":"6","author":"P Bradley","year":"2015","unstructured":"Bradley P, Gordon NC, Walker TM, Dunn L, Heys S, Huang B, Earle S, Pankhurst LJ, Anson L, de Cesare M, et al. Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis. Nat Commun. 2015;6:10063.","journal-title":"Nat Commun"},{"issue":"11","key":"4666_CR13","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1186\/s13073-014-0090-6","volume":"6","author":"M Inouye","year":"2014","unstructured":"Inouye M, Dashnow H, Raven LA, Schultz MB, Pope BJ, Tomita T, Zobel J, Holt KE. SRST2: rapid genomic surveillance for public health and hospital microbiology labs. Genome Med. 2014;6(11):90.","journal-title":"Genome Med"},{"issue":"10","key":"4666_CR14","doi-asserted-by":"publisher","first-page":"2764","DOI":"10.1093\/jac\/dkx217","volume":"72","author":"E Zankari","year":"2017","unstructured":"Zankari E, Allesoe R, Joensen KG, Cavaco LM, Lund O, Aarestrup FM. PointFinder: a novel web tool for WGS-based detection of antimicrobial resistance associated with chromosomal point mutations in bacterial pathogens. J Antimicrob Chemother. 2017;72(10):2764\u20138.","journal-title":"J Antimicrob Chemother"},{"issue":"1","key":"4666_CR15","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1038\/ismej.2014.106","volume":"9","author":"MK Gibson","year":"2015","unstructured":"Gibson MK, Forsberg KJ, Dantas G. Improved annotation of antibiotic resistance determinants reveals microbial resistomes cluster by ecology. ISME J. 2015;9(1):207\u201316.","journal-title":"ISME J"},{"issue":"D1","key":"4666_CR16","first-page":"D517","volume":"48","author":"BP Alcock","year":"2020","unstructured":"Alcock BP, Raphenya AR, Lau TTY, Tsang KK, Bouchard M, Edalatmand A, Huynh W, Nguyen AV, Cheng AA, Liu S, et al. CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Res. 2020;48(D1):D517\u201325.","journal-title":"Nucleic Acids Res"},{"key":"4666_CR17","doi-asserted-by":"crossref","unstructured":"Bortolaia V, Kaas RS, Ruppe E, Roberts MC, Schwarz S, Cattoir V, Philippon A, Allesoe RL, Rebelo AR, Florensa AF, et al. ResFinder 4.0 for predictions of phenotypes from genotypes. J Antimicrob Chemother. 2020;75(12):3491\u20133500.","DOI":"10.1093\/jac\/dkaa345"},{"issue":"10","key":"4666_CR18","first-page":"e000131","volume":"3","author":"M Hunt","year":"2017","unstructured":"Hunt M, Mather AE, Sanchez-Buso L, Page AJ, Parkhill J, Keane JA, Harris SR. ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads. Microb Genom. 2017;3(10):e000131.","journal-title":"Microb Genom"},{"issue":"6","key":"4666_CR19","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1016\/j.gde.2005.09.006","volume":"15","author":"D Medini","year":"2005","unstructured":"Medini D, Donati C, Tettelin H, Masignani V, Rappuoli R. The microbial pan-genome. Curr Opin Genet Dev. 2005;15(6):589\u201394.","journal-title":"Curr Opin Genet Dev"},{"issue":"39","key":"4666_CR20","doi-asserted-by":"publisher","first-page":"13950","DOI":"10.1073\/pnas.0506758102","volume":"102","author":"H Tettelin","year":"2005","unstructured":"Tettelin H, Masignani V, Cieslewicz MJ, Donati C, Medini D, Ward NL, Angiuoli SV, Crabtree J, Jones AL, Durkin AS, et al. Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: implications for the microbial \u201cpan-genome.\u201d Proc Natl Acad Sci U S A. 2005;102(39):13950\u20135.","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"1","key":"4666_CR21","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1186\/s13059-019-1751-y","volume":"20","author":"Z Duan","year":"2019","unstructured":"Duan Z, Qiao Y, Lu J, Lu H, Zhang W, Yan F, Sun C, Hu Z, Zhang Z, Li G, et al. HUPAN: a pan-genome analysis pipeline for human genomes. Genome Biol. 2019;20(1):149.","journal-title":"Genome Biol"},{"issue":"4","key":"4666_CR22","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1038\/s41576-020-0210-7","volume":"21","author":"RM Sherman","year":"2020","unstructured":"Sherman RM, Salzberg SL. Pan-genomics in the human genome era. Nat Rev Genet. 2020;21(4):243\u201354.","journal-title":"Nat Rev Genet"},{"key":"4666_CR23","doi-asserted-by":"publisher","first-page":"1169","DOI":"10.3389\/fgene.2019.01169","volume":"10","author":"R Li","year":"2019","unstructured":"Li R, Fu W, Su R, Tian X, Du D, Zhao Y, Zheng Z, Chen Q, Gao S, Cai Y, et al. Towards the complete goat pan-genome by recovering missing genomic segments from the reference genome. Front Genet. 2019;10:1169.","journal-title":"Front Genet"},{"key":"4666_CR24","doi-asserted-by":"crossref","unstructured":"Tian X, Li R, Fu W, Li Y, Wang X, Li M, Du D, Tang Q, Cai Y, Long Y, et al. Building a sequence map of the pig pan-genome from multiple de novo assemblies and Hi-C data. Sci China Life Sci. 2020;63(5):750\u201363.","DOI":"10.1007\/s11427-019-9551-7"},{"issue":"8","key":"4666_CR25","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1038\/s41477-020-0733-0","volume":"6","author":"PE Bayer","year":"2020","unstructured":"Bayer PE, Golicz AA, Scheben A, Batley J, Edwards D. Plant pan-genomes are the new reference. Nat Plants. 2020;6(8):914\u201320.","journal-title":"Nat Plants"},{"issue":"1","key":"4666_CR26","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1186\/s13059-016-1108-8","volume":"17","author":"O Brynildsrud","year":"2016","unstructured":"Brynildsrud O, Bohlin J, Scheffer L, Eldholm V. Rapid scoring of genes in microbial pan-genome-wide association studies with Scoary. Genome Biol. 2016;17(1):238.","journal-title":"Genome Biol"},{"issue":"12","key":"4666_CR27","doi-asserted-by":"publisher","first-page":"e1006258","DOI":"10.1371\/journal.pcbi.1006258","volume":"14","author":"D Moradigaravand","year":"2018","unstructured":"Moradigaravand D, Palm M, Farewell A, Mustonen V, Warringer J, Parts L. Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data. PLoS Comput Biol. 2018;14(12):e1006258.","journal-title":"PLoS Comput Biol"},{"issue":"13","key":"4666_CR28","doi-asserted-by":"publisher","first-page":"i89","DOI":"10.1093\/bioinformatics\/bty276","volume":"34","author":"HL Her","year":"2018","unstructured":"Her HL, Wu YW. A pan-genome-based machine learning approach for predicting antimicrobial resistance activities of the Escherichia coli strains. Bioinformatics. 2018;34(13):i89\u201395.","journal-title":"Bioinformatics"},{"issue":"D1","key":"4666_CR29","first-page":"D606","volume":"48","author":"JJ Davis","year":"2020","unstructured":"Davis JJ, Wattam AR, Aziz RK, Brettin T, Butler R, Butler RM, Chlenski P, Conrad N, Dickerman A, Dietrich EM, et al. The PATRIC Bioinformatics Resource Center: expanding data and analysis capabilities. Nucleic Acids Res. 2020;48(D1):D606\u201312.","journal-title":"Nucleic Acids Res"},{"key":"4666_CR30","doi-asserted-by":"crossref","unstructured":"Lobb B, Tremblay BJ, Moreno-Hagelsieb G, Doxey AC. An assessment of genome annotation coverage across the bacterial tree of life. Microb Genom. 2020;6(3):e000341.","DOI":"10.1099\/mgen.0.000341"},{"issue":"3","key":"4666_CR31","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/S0924-8579(03)00202-4","volume":"22","author":"P Butaye","year":"2003","unstructured":"Butaye P, Cloeckaert A, Schwarz S. Mobile genes coding for efflux-mediated antimicrobial resistance in Gram-positive and Gram-negative bacteria. Int J Antimicrob Agents. 2003;22(3):205\u201310.","journal-title":"Int J Antimicrob Agents"},{"key":"4666_CR32","doi-asserted-by":"publisher","first-page":"118","DOI":"10.3389\/fcimb.2016.00118","volume":"6","author":"J Huang","year":"2016","unstructured":"Huang J, Ma J, Shang K, Hu X, Liang Y, Li D, Wu Z, Dai L, Chen L, Wang L. Evolution and diversity of the antimicrobial resistance associated mobilome in Streptococcus suis: a probable mobile genetic elements reservoir for other Streptococci. Front Cell Infect Microbiol. 2016;6:118.","journal-title":"Front Cell Infect Microbiol"},{"key":"4666_CR33","doi-asserted-by":"crossref","unstructured":"Partridge SR, Kwong SM, Firth N, Jensen SO. Mobile genetic elements associated with antimicrobial resistance. Clin Microbiol Rev. 2018;31(4).","DOI":"10.1128\/CMR.00088-17"},{"issue":"2","key":"4666_CR34","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1017\/S146625230800159X","volume":"9","author":"P Boerlin","year":"2008","unstructured":"Boerlin P, Reid-Smith RJ. Antimicrobial resistance: its emergence and transmission. Anim Health Res Rev. 2008;9(2):115\u201326.","journal-title":"Anim Health Res Rev"},{"issue":"2","key":"4666_CR35","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1080\/10495390600957092","volume":"17","author":"H Harbottle","year":"2006","unstructured":"Harbottle H, Thakur S, Zhao S, White DG. Genetics of antimicrobial resistance. Anim Biotechnol. 2006;17(2):111\u201324.","journal-title":"Anim Biotechnol"},{"issue":"2","key":"4666_CR36","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1136\/archdischild-2015-309069","volume":"102","author":"E Germovsek","year":"2017","unstructured":"Germovsek E, Barker CI, Sharland M. What do I need to know about aminoglycoside antibiotics? Arch Dis Child Educ Pract Ed. 2017;102(2):89\u201393.","journal-title":"Arch Dis Child Educ Pract Ed"},{"issue":"4","key":"4666_CR37","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1128\/CMR.5.4.387","volume":"5","author":"BS Speer","year":"1992","unstructured":"Speer BS, Shoemaker NB, Salyers AA. Bacterial resistance to tetracycline: mechanisms, transfer, and clinical significance. Clin Microbiol Rev. 1992;5(4):387\u201399.","journal-title":"Clin Microbiol Rev"},{"issue":"7","key":"4666_CR38","doi-asserted-by":"publisher","first-page":"1043","DOI":"10.1101\/gr.186072.114","volume":"25","author":"DH Parks","year":"2015","unstructured":"Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25(7):1043\u201355.","journal-title":"Genome Res"},{"issue":"17","key":"4666_CR39","doi-asserted-by":"publisher","first-page":"3389","DOI":"10.1093\/nar\/25.17.3389","volume":"25","author":"SF Altschul","year":"1997","unstructured":"Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25(17):3389\u2013402.","journal-title":"Nucleic Acids Res"},{"issue":"23","key":"4666_CR40","doi-asserted-by":"publisher","first-page":"3150","DOI":"10.1093\/bioinformatics\/bts565","volume":"28","author":"L Fu","year":"2012","unstructured":"Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012;28(23):3150\u20132.","journal-title":"Bioinformatics"},{"issue":"5","key":"4666_CR41","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1016\/j.mib.2008.09.006","volume":"11","author":"H Tettelin","year":"2008","unstructured":"Tettelin H, Riley D, Cattuto C, Medini D. Comparative genomics: the bacterial pan-genome. Curr Opin Microbiol. 2008;11(5):472\u20137.","journal-title":"Curr Opin Microbiol"},{"key":"4666_CR42","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C. XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (KDD 16). San Francisco, California, USA: ACM; 2016, p. 785\u201394.","DOI":"10.1145\/2939672.2939785"},{"key":"4666_CR43","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825\u201330.","journal-title":"J Mach Learn Res"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-04666-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-022-04666-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-04666-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T10:05:07Z","timestamp":1691402707000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-022-04666-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,15]]},"references-count":43,"journal-issue":{"issue":"S4","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["4666"],"URL":"https:\/\/doi.org\/10.1186\/s12859-022-04666-2","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,15]]},"assertion":[{"value":"23 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 April 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"131"}}