{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T10:07:04Z","timestamp":1772964424534,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,1,18]],"date-time":"2021-01-18T00:00:00Z","timestamp":1610928000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2021,1,18]],"date-time":"2021-01-18T00:00:00Z","timestamp":1610928000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1564894"],"award-info":[{"award-number":["1564894"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Diverse microbiome communities drive biogeochemical processes and evolution of animals in their ecosystems. Many microbiome projects have demonstrated the power of using metagenomics to understand the structures and factors influencing the function of the microbiomes in their environments. In order to characterize the effects from microbiome composition for human health, diseases, and even ecosystems, one must first understand the relationship of microbes and their environment in different samples. Running machine learning model with metagenomic sequencing data is encouraged for this purpose, but it is not an easy task to make an appropriate machine learning model for all diverse metagenomic datasets.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We introduce MegaR, an R Shiny package and web application, to build an unbiased machine learning model effortlessly with interactive visual analysis. The MegaR employs taxonomic profiles from either whole metagenome sequencing or 16S rRNA sequencing data to develop machine learning models and classify the samples into two or more categories. It provides various options for model fine tuning throughout the analysis pipeline such as data processing, multiple machine learning techniques, model validation, and unknown sample prediction that can be used to achieve the highest prediction accuracy possible for any given dataset while still maintaining a user-friendly experience.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Metagenomic sample classification and phenotype prediction is important particularly when it applies to a diagnostic method for identifying and predicting microbe-related human diseases. MegaR provides various interactive visualizations for user to build an accurate machine-learning model without difficulty. Unknown sample prediction with a properly trained model using MegaR will enhance researchers to identify the sample property in a fast turnaround time.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-020-03933-4","type":"journal-article","created":{"date-parts":[[2021,1,18]],"date-time":"2021-01-18T17:04:53Z","timestamp":1610989493000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["MegaR: an interactive R package for rapid sample classification and phenotype prediction using metagenome profiles and machine learning"],"prefix":"10.1186","volume":"22","author":[{"given":"Eliza","family":"Dhungel","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yassin","family":"Mreyoud","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ho-Jin","family":"Gwak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmad","family":"Rajeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mina","family":"Rho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7281-9459","authenticated-orcid":false,"given":"Tae-Hyuk","family":"Ahn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,18]]},"reference":[{"issue":"1","key":"3933_CR1","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1186\/2042-5783-2-3","volume":"2","author":"T Thomas","year":"2012","unstructured":"Thomas T, Gilbert J, Meyer F. Metagenomics\u2014a guide from sampling to data analysis. Microb Inform Exp. 2012;2(1):3.","journal-title":"Microb Inform Exp"},{"issue":"7402","key":"3933_CR2","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1038\/nature11234","volume":"486","author":"C Huttenhower","year":"2012","unstructured":"Huttenhower C, Gevers D, Knight R, Abubucker S, Badger JH, Chinwalla AT, Creasy HH, Earl AM, FitzGerald MG, Fulton RS, et al. Structure, function and diversity of the healthy human microbiome. Nature. 2012;486(7402):207\u201314.","journal-title":"Nature"},{"issue":"7285","key":"3933_CR3","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1038\/nature08821","volume":"464","author":"J Qin","year":"2010","unstructured":"Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, Nielsen T, Pons N, Levenez F, Yamada T, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464(7285):59\u201365.","journal-title":"Nature"},{"issue":"6237","key":"3933_CR4","doi-asserted-by":"publisher","first-page":"1261359","DOI":"10.1126\/science.1261359","volume":"348","author":"S Sunagawa","year":"2015","unstructured":"Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, Djahanschiri B, Zeller G, Mende DR, Alberti A, et al. Ocean plankton. Structure and function of the global ocean microbiome. Science. 2015;348(6237):1261359.","journal-title":"Science"},{"key":"3933_CR5","doi-asserted-by":"crossref","unstructured":"Sanschagrin S, Yergeau E. Next-generation sequencing of 16S ribosomal RNA gene amplicons. J Vis Exp. 2014;(90):51709.","DOI":"10.3791\/51709"},{"issue":"9","key":"3933_CR6","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1038\/nbt.3935","volume":"35","author":"C Quince","year":"2017","unstructured":"Quince C, Walker AW, Simpson JT, Loman NJ, Segata N. Shotgun metagenomics, from sampling to analysis. Nat Biotechnol. 2017;35(9):833\u201344.","journal-title":"Nat Biotechnol"},{"key":"3933_CR7","doi-asserted-by":"publisher","first-page":"459","DOI":"10.3389\/fmicb.2016.00459","volume":"7","author":"J Jovel","year":"2016","unstructured":"Jovel J, Patterson J, Wang W, Hotte N, O\u2019Keefe S, Mitchel T, Perry T, Kao D, Mason AL, Madsen KL, et al. Characterization of the gut microbiome using 16S or shotgun metagenomics. Front Microbiol. 2016;7:459.","journal-title":"Front Microbiol"},{"issue":"6","key":"3933_CR8","doi-asserted-by":"publisher","first-page":"e00069-18","DOI":"10.1128\/mSystems.00069-18","volume":"3","author":"B Hillmann","year":"2018","unstructured":"Hillmann B, Al-Ghalith GA, Shields-Cutler RR, Zhu Q, Gohl DM, Beckman KB, Knight R, Knights D. Evaluating the information content of shallow shotgun metagenomics. mSystems. 2018;3(6):e00069-18.","journal-title":"mSystems"},{"key":"3933_CR9","doi-asserted-by":"publisher","first-page":"852","DOI":"10.1038\/s41587-019-0209-9","volume":"37","author":"ERJ Bolyen","year":"2019","unstructured":"Bolyen ERJ, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, Bai Y, Bisanz JE, Bittinger K, Brejnrod A, Brislawn CJ, Brown CT, Callahan BJ, Caraballo-Rodr\u00edguez AM, Chase J, Cope EK, Da Silva R, Diener C, Dorrestein PC, Douglas GM, Durall DM, Duvallet C, Edwardson CF, Ernst M, Estaki M, Fouquier J, Gauglitz JM, Gibbons SM, Gibson DL, Gonzalez A, Gorlick K, Guo J, Hillmann B, Holmes S, Holste H, Huttenhower C, Huttley GA, Janssen S, Jarmusch AK, Jiang L, Kaehler BD, Kang KB, Keefe CR, Keim P, Kelley ST, Knights D, Koester I, Kosciolek T, Kreps J, Langille MGI, Lee J, Ley R, Liu YX, Loftfield E, Lozupone C, Maher M, Marotz C, Martin BD, McDonald D, McIver LJ, Melnik AV, Metcalf JL, Morgan SC, Morton JT, Naimey AT, Navas-Molina JA, Nothias LF, Orchanian SB, Pearson T, Peoples SL, Petras D, Preuss ML, Pruesse E, Rasmussen LB, Rivers A, Robeson MS, Rosenthal P, Segata N, Shaffer M, Shiffer A, Sinha R, Song SJ, Spear JR, Swafford AD, Thompson LR, Torres PJ, Trinh P, Tripathi A, Turnbaugh PJ, Ul-Hasan S, van der Hooft JJJ, Vargas F, V\u00e1zquez-Baeza Y, Vogtmann E, von Hippel M, Walters W, Wan Y, Wang M, Warren J, Weber KC, Williamson CHD, Willis AD, Xu ZZ, Zaneveld JR, Zhang Y, Zhu Q, Knight R, Caporaso JG. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852\u20137.","journal-title":"Nat Biotechnol"},{"issue":"Suppl 2","key":"3933_CR10","doi-asserted-by":"publisher","first-page":"S4","DOI":"10.1186\/1471-2164-12-S2-S4","volume":"12","author":"B Liu","year":"2011","unstructured":"Liu B, Gibbons T, Ghodsi M, Treangen T, Pop M. Accurate and fast estimation of taxonomic profiles from metagenomic shotgun sequences. BMC Genomics. 2011;12(Suppl 2):S4.","journal-title":"BMC Genomics"},{"issue":"3","key":"3933_CR11","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1101\/gr.5969107","volume":"17","author":"DH Huson","year":"2007","unstructured":"Huson DH, Auch AF, Qi J, Schuster SC. MEGAN analysis of metagenomic data. Genome Res. 2007;17(3):377\u201386.","journal-title":"Genome Res"},{"issue":"2","key":"3933_CR12","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1093\/bioinformatics\/btu641","volume":"31","author":"TH Ahn","year":"2015","unstructured":"Ahn TH, Chai J, Pan C. Sigma: strain-level inference of genomes from metagenomic analysis for biosurveillance. Bioinformatics. 2015;31(2):170\u20137.","journal-title":"Bioinformatics"},{"issue":"5","key":"3933_CR13","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1038\/nmeth0511-367","volume":"8","author":"A Brady","year":"2011","unstructured":"Brady A, Salzberg S. PhymmBL expanded: confidence scores, custom databases, parallelization and more. Nat Methods. 2011;8(5):367.","journal-title":"Nat Methods"},{"issue":"3","key":"3933_CR14","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1038\/nmeth0311-191","volume":"8","author":"KR Patil","year":"2011","unstructured":"Patil KR, Haider P, Pope PB, Turnbaugh PJ, Morrison M, Scheffer T, McHardy AC. Taxonomic metagenome sequence assignment with structured output models. Nat Methods. 2011;8(3):191\u20132.","journal-title":"Nat Methods"},{"issue":"10","key":"3933_CR15","doi-asserted-by":"publisher","first-page":"902","DOI":"10.1038\/nmeth.3589","volume":"12","author":"DT Truong","year":"2015","unstructured":"Truong DT, Franzosa EA, Tickle TL, Scholz M, Weingart G, Pasolli E, Tett A, Huttenhower C, Segata N. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat Methods. 2015;12(10):902\u20133.","journal-title":"Nat Methods"},{"issue":"7","key":"3933_CR16","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.1093\/bioinformatics\/bts079","volume":"28","author":"M Wu","year":"2012","unstructured":"Wu M, Scott AJ. Phylogenomic analysis of bacterial and archaeal sequences with AMPHORA2. Bioinformatics. 2012;28(7):1033\u20134.","journal-title":"Bioinformatics"},{"key":"3933_CR17","doi-asserted-by":"crossref","unstructured":"Douglas GM, Maffei VJ, Zaneveld J, Yurgel SN, Brown JR, Taylor CM, Huttenhower C, Langille MGI. PICRUSt2: an improved and extensible approach for metagenome inference. bioRxiv 2019;672295.","DOI":"10.1101\/672295"},{"issue":"2","key":"3933_CR18","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1093\/bib\/bby012","volume":"19","author":"SY Niu","year":"2018","unstructured":"Niu SY, Yang J, McDermaid A, Zhao J, Kang Y, Ma Q. Bioinformatics tools for quantitative and functional metagenome and metatranscriptome data analysis in microbes. Brief Bioinform. 2018;19(2):360.","journal-title":"Brief Bioinform"},{"issue":"5","key":"3933_CR19","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1101\/gr.213959.116","volume":"27","author":"S Nurk","year":"2017","unstructured":"Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27(5):824\u201334.","journal-title":"Genome Res"},{"issue":"4","key":"3933_CR20","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1101\/gr.216242.116","volume":"27","author":"DT Truong","year":"2017","unstructured":"Truong DT, Tett A, Pasolli E, Huttenhower C, Segata N. Microbial strain-level population structure and genetic diversity from metagenomes. Genome Res. 2017;27(4):626\u201338.","journal-title":"Genome Res"},{"issue":"1","key":"3933_CR21","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1186\/s13059-019-1891-0","volume":"20","author":"DE Wood","year":"2019","unstructured":"Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol. 2019;20(1):257.","journal-title":"Genome Biol"},{"issue":"343","key":"3933_CR22","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1126\/scitranslmed.aad0917","volume":"8","author":"M Yassour","year":"2016","unstructured":"Yassour M, Vatanen T, Siljander H, Hamalainen AM, Harkonen T, Ryhanen SJ, Franzosa EA, Vlamakis H, Huttenhower C, Gevers D, et al. Natural history of the infant gut microbiome and impact of antibiotic treatment on bacterial strain diversity and stability. Sci Transl Med. 2016;8(343):343\u201381.","journal-title":"Sci Transl Med"},{"issue":"2","key":"3933_CR23","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1016\/j.chom.2015.01.001","volume":"17","author":"AD Kostic","year":"2015","unstructured":"Kostic AD, Gevers D, Siljander H, Vatanen T, Hyotylainen T, Hamalainen AM, Peet A, Tillmann V, Poho P, Mattila I, et al. The dynamics of the human infant gut microbiome in development and in progression toward type 1 diabetes. Cell Host Microbe. 2015;17(2):260\u201373.","journal-title":"Cell Host Microbe"},{"issue":"6","key":"3933_CR24","doi-asserted-by":"publisher","first-page":"1551","DOI":"10.1016\/j.cell.2016.05.056","volume":"165","author":"T Vatanen","year":"2016","unstructured":"Vatanen T, Kostic AD, d\u2019Hennezel E, Siljander H, Franzosa EA, Yassour M, Kolde R, Vlamakis H, Arthur TD, Hamalainen AM, et al. Variation in microbiome LPS immunogenicity contributes to autoimmunity in humans. Cell. 2016;165(6):1551.","journal-title":"Cell"},{"issue":"1","key":"3933_CR25","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1186\/s40168-016-0168-z","volume":"4","author":"Meta SUBIC","year":"2016","unstructured":"Meta SUBIC. The Metagenomics and metadesign of the subways and urban biomes (MetaSUB) international consortium inaugural meeting report. Microbiome. 2016;4(1):24.","journal-title":"Microbiome"},{"issue":"1","key":"3933_CR26","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1186\/s40168-018-0603-4","volume":"6","author":"JD Forbes","year":"2018","unstructured":"Forbes JD, Chen CY, Knox NC, Marrie RA, El-Gabalawy H, de Kievit T, Alfa M, Bernstein CN, Van Domselaar G. A comparative study of the gut microbiota in immune-mediated inflammatory diseases-does a common dysbiosis exist? Microbiome. 2018;6(1):221.","journal-title":"Microbiome"},{"issue":"1","key":"3933_CR27","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1186\/s13062-019-0242-0","volume":"14","author":"ZN Harris","year":"2019","unstructured":"Harris ZN, Dhungel E, Mosior M, Ahn TH. Massive metagenomic data analysis using abundance-based machine learning. Biol Direct. 2019;14(1):12.","journal-title":"Biol Direct"},{"issue":"7","key":"3933_CR28","doi-asserted-by":"publisher","first-page":"e1004977","DOI":"10.1371\/journal.pcbi.1004977","volume":"12","author":"E Pasolli","year":"2016","unstructured":"Pasolli E, Truong DT, Malik F, Waldron L, Segata N. Machine learning meta-analysis of large metagenomic datasets: tools and biological insights. PLoS Comput Biol. 2016;12(7):e1004977.","journal-title":"PLoS Comput Biol"},{"issue":"9","key":"3933_CR29","doi-asserted-by":"crossref","first-page":"1286","DOI":"10.1093\/bioinformatics\/btw828","volume":"33","author":"D Luo","year":"2017","unstructured":"Luo D, Ziebell S, An L. An informative approach on differential abundance analysis for time-course metagenomic sequencing data. Bioinformatics. 2017;33(9):1286\u201392.","journal-title":"Bioinformatics"},{"issue":"1","key":"3933_CR30","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1186\/s40168-018-0402-y","volume":"6","author":"AA Metwally","year":"2018","unstructured":"Metwally AA, Yang J, Ascoli C, Dai Y, Finn PW, Perkins DL. MetaLonDA: a flexible R package for identifying time intervals of differentially abundant features in metagenomic longitudinal studies. Microbiome. 2018;6(1):32.","journal-title":"Microbiome"},{"issue":"12","key":"3933_CR31","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1186\/s12859-019-2833-2","volume":"20","author":"C Lo","year":"2019","unstructured":"Lo C, Marculescu R. MetaNN: accurate classification of host phenotypes from metagenomic data using neural networks. BMC Bioinform. 2019;20(12):314.","journal-title":"BMC Bioinform"},{"issue":"1","key":"3933_CR32","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1186\/2047-217X-1-7","volume":"1","author":"D McDonald","year":"2012","unstructured":"McDonald D, Clemente JC, Kuczynski J, Rideout JR, Stombaugh J, Wendel D, Wilke A, Huse S, Hufnagle J, Meyer F, et al. The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome. Gigascience. 2012;1(1):7.","journal-title":"Gigascience"},{"issue":"5","key":"3933_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v028.i05","volume":"28","author":"M Kuhn","year":"2008","unstructured":"Kuhn M. Building predictive models in R using the caret package. J Stat Softw. 2008;28(5):1\u201326.","journal-title":"J Stat Softw"},{"issue":"3","key":"3933_CR34","first-page":"18","volume":"2","author":"A Liaw","year":"2002","unstructured":"Liaw A, Wiener M. Classification and regression by randomForest. R News. 2002;2(3):18\u201322.","journal-title":"R News"},{"issue":"3","key":"3933_CR35","doi-asserted-by":"publisher","first-page":"370","DOI":"10.2307\/2344614","volume":"135","author":"JA Nelder","year":"1972","unstructured":"Nelder JA, Wedderburn RWM. Generalized linear model. J R Stat Soc Ser A. 1972;135(3):370\u201384.","journal-title":"J R Stat Soc Ser A"},{"issue":"3","key":"3933_CR36","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V. Support-Vector networks. Mach Learn. 1995;20(3):273\u201397.","journal-title":"Mach Learn"},{"issue":"12","key":"3933_CR37","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1038\/nmeth.2658","volume":"10","author":"JN Paulson","year":"2013","unstructured":"Paulson JN, Stine OC, Bravo HC, Pop M. Differential abundance analysis for microbial marker-gene surveys. Nat Methods. 2013;10(12):1200\u20132.","journal-title":"Nat Methods"},{"issue":"7418","key":"3933_CR38","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1038\/nature11450","volume":"490","author":"J Qin","year":"2012","unstructured":"Qin J, Li Y, Cai Z, Li S, Zhu J, Zhang F, Liang S, Zhang W, Guan Y, Shen D, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature. 2012;490(7418):55\u201360.","journal-title":"Nature"},{"issue":"7452","key":"3933_CR39","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1038\/nature12198","volume":"498","author":"FH Karlsson","year":"2013","unstructured":"Karlsson FH, Tremaroli V, Nookaew I, Bergstr\u00f6m G, Behre CJ, Fagerberg B, Nielsen J, B\u00e4ckhed F. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature. 2013;498(7452):99\u2013103.","journal-title":"Nature"},{"issue":"7516","key":"3933_CR40","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1038\/nature13568","volume":"513","author":"N Qin","year":"2014","unstructured":"Qin N, Yang F, Li A, Prifti E, Chen Y, Shao L, Guo J, Le Chatelier E, Yao J, Wu L, et al. Alterations of the human gut microbiome in liver cirrhosis. Nature. 2014;513(7516):59\u201364.","journal-title":"Nature"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-03933-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12859-020-03933-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-03933-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T23:11:34Z","timestamp":1697584294000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-020-03933-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,18]]},"references-count":40,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["3933"],"URL":"https:\/\/doi.org\/10.1186\/s12859-020-03933-4","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,18]]},"assertion":[{"value":"28 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 December 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"25"}}