{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,3]],"date-time":"2022-04-03T20:20:58Z","timestamp":1649017258014},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T00:00:00Z","timestamp":1648166400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T00:00:00Z","timestamp":1648166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Algorithms Mol Biol"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>There has been rapid development of probabilistic models and inference methods for transcript abundance estimation from RNA-seq data. These models aim to accurately estimate transcript-level abundances, to account for different biases in the measurement process, and even to assess uncertainty in resulting estimates that can be propagated to subsequent analyses. The assumed accuracy of the estimates inferred by such methods underpin gene expression based analysis routinely carried out in the lab. Although hyperparameter selection is known to affect the distributions of inferred abundances (e.g. producing smooth versus sparse estimates), strategies for performing model selection in experimental data have been addressed informally at best.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We derive <jats:italic>perplexity<\/jats:italic> for evaluating abundance estimates on fragment sets directly. We adapt perplexity from the analogous metric used to evaluate language and topic models and extend the metric to carefully account for corner cases unique to RNA-seq. In experimental data, estimates with the best perplexity also best correlate with qPCR measurements. In simulated data, perplexity is well behaved and concordant with genome-wide measurements against ground truth and differential expression analysis. Furthermore, we demonstrate theoretically and experimentally that perplexity can be computed for arbitrary transcript abundance estimation models.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Alongside the derivation and implementation of <jats:italic>perplexity<\/jats:italic> for transcript abundance estimation, our study is the first to make possible model selection for transcript abundance estimation on experimental data in the absence of ground truth.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s13015-022-00214-y","type":"journal-article","created":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T03:02:35Z","timestamp":1648177355000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Perplexity: evaluating transcript abundance estimation in the absence of ground truth"],"prefix":"10.1186","volume":"17","author":[{"given":"Jason","family":"Fan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Skylar","family":"Chan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rob","family":"Patro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,25]]},"reference":[{"issue":"9","key":"214_CR1","doi-asserted-by":"publisher","first-page":"giz100","DOI":"10.1093\/gigascience\/giz100","volume":"8","author":"E Bushmanova","year":"2019","unstructured":"Bushmanova E, Antipov D, Lapidus A, Prjibelski AD. rnaSPAdes: a de novo transcriptome assembler and its application to RNA-Seq data. GigaScience. 2019;8(9):giz100. https:\/\/doi.org\/10.1093\/gigascience\/giz100.","journal-title":"GigaScience"},{"issue":"7","key":"214_CR2","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1038\/nbt.1883","volume":"29","author":"MG Grabherr","year":"2011","unstructured":"Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011;29(7):644\u201352. https:\/\/doi.org\/10.1038\/nbt.1883.","journal-title":"Nat Biotechnol"},{"key":"214_CR3","doi-asserted-by":"publisher","first-page":"904","DOI":"10.3389\/fgene.2019.00904","volume":"10","author":"M Shakya","year":"2019","unstructured":"Shakya M, Lo C-C, Chain PSG. Advances and challenges in metatranscriptomic analysis. Front Genetics. 2019;10:904. https:\/\/doi.org\/10.3389\/fgene.2019.00904.","journal-title":"Front Genetics"},{"issue":"2","key":"214_CR4","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1093\/bioinformatics\/btu638","volume":"31","author":"S Anders","year":"2015","unstructured":"Anders S, Pyl PT, Huber W. Htseq-a python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31(2):166\u20139.","journal-title":"Bioinformatics"},{"issue":"7","key":"214_CR5","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1093\/bioinformatics\/btt656","volume":"30","author":"Y Liao","year":"2014","unstructured":"Liao Y, Smyth GK, Shi W. featurecounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30(7):923\u201330.","journal-title":"Bioinformatics"},{"key":"214_CR6","doi-asserted-by":"publisher","DOI":"10.12688\/f1000research.7563.1","author":"C Soneson","year":"2015","unstructured":"Soneson C, Love MI, Robinson MD. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research. 2015. https:\/\/doi.org\/10.12688\/f1000research.7563.1.","journal-title":"F1000Research"},{"issue":"8","key":"214_CR7","doi-asserted-by":"publisher","first-page":"1026","DOI":"10.1093\/bioinformatics\/btp113","volume":"25","author":"H Jiang","year":"2009","unstructured":"Jiang H, Wong WH. Statistical inferences for isoform expression in RNA-seq. Bioinformatics. 2009;25(8):1026\u201332.","journal-title":"Bioinformatics"},{"issue":"2","key":"214_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/gb-2011-12-2-r13","volume":"12","author":"E Turro","year":"2011","unstructured":"Turro E, Su S-Y, Gon\u00e7alves \u00c2, Coin LJ, Richardson S, Lewin A. Haplotype and isoform specific expression estimation using multi-mapping RNA-seq reads. Genome Biol. 2011;12(2):1\u201315.","journal-title":"Genome Biol"},{"issue":"1","key":"214_CR9","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1186\/1471-2105-12-323","volume":"12","author":"B Li","year":"2011","unstructured":"Li B, Dewey CN. Rsem: accurate transcript quantification from rna-seq data with or without a reference genome. BMC Bioinform. 2011;12(1):323. https:\/\/doi.org\/10.1186\/1471-2105-12-323.","journal-title":"BMC Bioinform"},{"issue":"13","key":"214_CR10","doi-asserted-by":"publisher","first-page":"1721","DOI":"10.1093\/bioinformatics\/bts260","volume":"28","author":"P Glaus","year":"2012","unstructured":"Glaus P, Honkela A, Rattray M. Identifying differentially expressed transcripts from RNA-seq data with biological variation. Bioinformatics. 2012;28(13):1721\u20138.","journal-title":"Bioinformatics"},{"issue":"24","key":"214_CR11","doi-asserted-by":"publisher","first-page":"3881","DOI":"10.1093\/bioinformatics\/btv483","volume":"31","author":"J Hensman","year":"2015","unstructured":"Hensman J, Papastamoulis P, Glaus P, Honkela A, Rattray M. Fast and accurate approximate inference of transcript expression from RNA-seq data. Bioinformatics. 2015;31(24):3881\u20139. https:\/\/doi.org\/10.1093\/bioinformatics\/btv483.","journal-title":"Bioinformatics."},{"issue":"18","key":"214_CR12","doi-asserted-by":"publisher","first-page":"2292","DOI":"10.1093\/bioinformatics\/btt381","volume":"29","author":"N Nariai","year":"2013","unstructured":"Nariai N, Hirose O, Kojima K, Nagasaki M. TIGAR: transcript isoform abundance estimation method with gapped alignment of RNA-Seq data by variational Bayesian inference. Bioinformatics. 2013;29(18):2292\u20139. https:\/\/doi.org\/10.1093\/bioinformatics\/btt381.","journal-title":"Bioinformatics."},{"key":"214_CR13","doi-asserted-by":"crossref","unstructured":"Nariai N, Kojima K, Mimori T, Kawai Y, Nagasaki M A bayesian approach for estimating allele-specific expression from RNA-seq data with diploid genomes. In: BMC Genomics, vol. 17, 2016. pp. 7\u201317 . New York: BioMed Central","DOI":"10.1186\/s12864-015-2295-5"},{"issue":"10","key":"214_CR14","first-page":"1","volume":"15","author":"N Nariai","year":"2014","unstructured":"Nariai N, Kojima K, Mimori T, Sato Y, Kawai Y, Yamaguchi-Kabata Y, Nagasaki M. Tigar2: sensitive and accurate estimation of transcript isoform expression with longer RNA-seq reads. BMC Genomics. 2014;15(10):1\u20139.","journal-title":"BMC Genomics"},{"issue":"4","key":"214_CR15","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1038\/nmeth.4197","volume":"14","author":"R Patro","year":"2017","unstructured":"Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nature Methods. 2017;14(4):417\u20139. https:\/\/doi.org\/10.1038\/nmeth.4197.","journal-title":"Nature Methods"},{"key":"214_CR16","doi-asserted-by":"publisher","DOI":"10.1101\/088765","author":"DC Jones","year":"2016","unstructured":"Jones DC, Kuppusamy KT, Palpant NJ, Peng X, Murry CE, Ruohola-Baker H, Ruzzo WL. Isolator: accurate and stable analysis of isoform-level expression in rna-seq experiments. BioRxiv. 2016. https:\/\/doi.org\/10.1101\/088765.","journal-title":"BioRxiv"},{"issue":"2","key":"214_CR17","doi-asserted-by":"publisher","first-page":"046","DOI":"10.1093\/nargab\/lqab046","volume":"3","author":"DC Jones","year":"2021","unstructured":"Jones DC, Ruzzo WL. Polee: RNA-Seq analysis using approximate likelihood. NAR Genomics Bioinformatics. 2021;3(2):046.","journal-title":"NAR Genomics Bioinformatics"},{"issue":"1","key":"214_CR18","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1093\/bioinformatics\/btaa450","volume":"36","author":"A Srivastava","year":"2020","unstructured":"Srivastava A, Malik L, Sarkar H, Patro R. A Bayesian framework for inter-cellular information sharing improves dscRNA-seq quantification. Bioinformatics. 2020;36(1):292\u20139.","journal-title":"Bioinformatics"},{"issue":"8","key":"214_CR19","doi-asserted-by":"publisher","first-page":"1124","DOI":"10.1101\/gr.199174.115","volume":"26","author":"P Liu","year":"2016","unstructured":"Liu P, Sanalkumar R, Bresnick EH, Kele\u015f S, Dewey CN. Integrative analysis with chip-seq advances the limits of transcript quantification from rna-seq. Genome Res. 2016;26(8):1124\u201333.","journal-title":"Genome Res"},{"issue":"9","key":"214_CR20","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1038\/nbt.2957","volume":"32","author":"Z Su","year":"2014","unstructured":"Su Z, \u0141abaj PP, Li S, Thierry-Mieg J, Thierry-Mieg D, Shi W, et al. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat Biotechnol. 2014;32(9):903\u201314. https:\/\/doi.org\/10.1038\/nbt.2957.","journal-title":"Nat Biotechnol"},{"issue":"1","key":"214_CR21","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1186\/gb-2013-14-1-r8","volume":"14","author":"A Rahman","year":"2013","unstructured":"Rahman A, Pachter L. CGAL: computing genome assembly likelihoods. Genome Biol. 2013;14(1):8. https:\/\/doi.org\/10.1186\/gb-2013-14-1-r8.","journal-title":"Genome Biol"},{"issue":"12","key":"214_CR22","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1186\/s13059-014-0553-5","volume":"15","author":"B Li","year":"2014","unstructured":"Li B, Fillmore N, Bai Y, Collins M, Thomson JA, Stewart R, Dewey CN. Evaluation of de novo transcriptome assemblies from RNA-Seq data. Genome Biol. 2014;15(12):553. https:\/\/doi.org\/10.1186\/s13059-014-0553-5.","journal-title":"Genome Biol"},{"issue":"8","key":"214_CR23","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.1101\/gr.196469.115","volume":"26","author":"R Smith-Unna","year":"2016","unstructured":"Smith-Unna R, Boursnell C, Patro R, Hibberd JM, Kelly S. TransRate: reference-free quality assessment of de novo transcriptome assemblies. Genome Res. 2016;26(8):1134\u201344. https:\/\/doi.org\/10.1101\/gr.196469.115.","journal-title":"Genome Res"},{"issue":"4","key":"214_CR24","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1093\/bioinformatics\/bts723","volume":"29","author":"SC Clark","year":"2013","unstructured":"Clark SC, Egan R, Frazier PI, Wang Z. ALE: a generic assembly likelihood evaluation framework for assessing the accuracy of genome and metagenome assemblies. Bioinformatics. 2013;29(4):435\u201343. https:\/\/doi.org\/10.1093\/bioinformatics\/bts723.","journal-title":"Bioinformatics"},{"key":"214_CR25","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw PJ. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 1987;20:53\u201365. https:\/\/doi.org\/10.1016\/0377-0427(87)90125-7.","journal-title":"J Comput Appl Math"},{"key":"214_CR26","first-page":"993","volume":"3","author":"DM Blei","year":"2003","unstructured":"Blei DM, Ng AY, Jordan MI. Latent dirichlet allocation. J Mach Learn Res. 2003;3:993\u20131022.","journal-title":"J Mach Learn Res."},{"issue":"4","key":"214_CR27","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1109\/proc.1976.10159","volume":"64","author":"F Jelinek","year":"1976","unstructured":"Jelinek F. Continuous speech recognition by statistical methods. Proc IEEE. 1976;64(4):532\u201356. https:\/\/doi.org\/10.1109\/proc.1976.10159.","journal-title":"Proc IEEE"},{"issue":"14","key":"214_CR28","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1093\/bioinformatics\/btx262","volume":"33","author":"M Zakeri","year":"2017","unstructured":"Zakeri M, Srivastava A, Almodaresi F, Patro R. Improved data-driven likelihood factorizations for transcript abundance estimation. Bioinformatics. 2017;33(14):142\u201351. https:\/\/doi.org\/10.1093\/bioinformatics\/btx262.","journal-title":"Bioinformatics."},{"key":"214_CR29","volume-title":"Pattern Recognition and Machine Learning","author":"CM Bishop","year":"2016","unstructured":"Bishop CM. Pattern Recognition and Machine Learning. Berlin: Springer; 2016."},{"key":"214_CR30","first-page":"2","volume":"9","author":"WA Gale","year":"1995","unstructured":"Gale WA. Good-turing smoothing without tears. J Quantit Linguistics. 1995;9:2.","journal-title":"J Quantit Linguistics"},{"issue":"1","key":"214_CR31","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1186\/s13059-019-1662-y","volume":"20","author":"ATL Lun","year":"2019","unstructured":"Lun ATL, Riesenfeld S, Andrews T, Dao TP, Gomes T, Marioni JC. participants in the 1st Human Cell Atlas Jamboree: Emptydrops: distinguishing cells from empty droplets in droplet-based single-cell rna sequencing data. Genome Biol. 2019;20(1):63. https:\/\/doi.org\/10.1186\/s13059-019-1662-y.","journal-title":"Genome Biol"},{"issue":"9","key":"214_CR32","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1038\/nbt1239","volume":"24","author":"L Shi","year":"2006","unstructured":"Shi L, Reid LH, Jones WD, Shippy R, Warrington JA, et al. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol. 2006;24(9):1151\u201361. https:\/\/doi.org\/10.1038\/nbt1239.","journal-title":"Nat Biotechnol"},{"issue":"10","key":"214_CR33","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1038\/nmeth1005-731","volume":"2","author":"SC Baker","year":"2005","unstructured":"Baker SC, Bauer SR, Beyer RP, Brenton JD, Bromley B, Burrill J, et al. The External RNA Controls Consortium: a progress report. Nat Methods. 2005;2(10):731\u20134. https:\/\/doi.org\/10.1038\/nmeth1005-731.","journal-title":"Nat Methods"},{"key":"214_CR34","doi-asserted-by":"publisher","first-page":"206937","DOI":"10.1155\/2015\/206937","volume":"2015","author":"WJ Kim","year":"2015","unstructured":"Kim WJ, Lim JH, Lee JS, Lee S-D, Kim JH, Oh Y-M. Comprehensive analysis of transcriptome sequencing data in the lung tissues of copd subjects. Int J Genomics. 2015;2015:206937. https:\/\/doi.org\/10.1155\/2015\/206937.","journal-title":"Int J Genomics"},{"issue":"17","key":"214_CR35","doi-asserted-by":"publisher","first-page":"2778","DOI":"10.1093\/bioinformatics\/btv272","volume":"31","author":"AC Frazee","year":"2015","unstructured":"Frazee AC, Jaffe AE, Langmead B, Leek JT. Polyester: simulating RNA-seq datasets with differential transcript expression. Bioinformatics. 2015;31(17):2778\u201384. https:\/\/doi.org\/10.1093\/bioinformatics\/btv272.","journal-title":"Bioinformatics."},{"issue":"D1","key":"214_CR36","doi-asserted-by":"publisher","first-page":"682","DOI":"10.1093\/nar\/gkz966","volume":"48","author":"AD Yates","year":"2019","unstructured":"Yates AD, Achuthan P, Akanni W, Allen J, Allen J, Alvarez-Jarreta J, et al. Ensembl 2020. Nucleic Acids Res. 2019;48(D1):682\u20138. https:\/\/doi.org\/10.1093\/nar\/gkz966.","journal-title":"Nucleic Acids Res."},{"key":"214_CR37","unstructured":"Rainer J. EnsDb.Hsapiens.v86: Ensembl Based Annotation Package. 2017. R package version 2.99.0"},{"issue":"8","key":"214_CR38","doi-asserted-by":"publisher","first-page":"1184","DOI":"10.1038\/nprot.2009.97","volume":"4","author":"S Durinck","year":"2009","unstructured":"Durinck S, Spellman PT, Birney E, Huber W. Mapping identifiers for the integration of genomic datasets with the R\/Bioconductor package biomaRt. Nature Protocols. 2009;4(8):1184\u201391. https:\/\/doi.org\/10.1038\/nprot.2009.97.","journal-title":"Nature Protocols"},{"issue":"18","key":"214_CR39","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1093\/nar\/gkz622","volume":"47","author":"A Zhu","year":"2019","unstructured":"Zhu A, Srivastava A, Ibrahim JG, Patro R, Love MI. Nonparametric expression analysis using inferential replicate counts. Nucleic Acids Res. 2019;47(18):105\u2013105. https:\/\/doi.org\/10.1093\/nar\/gkz622.","journal-title":"Nucleic Acids Res"},{"issue":"4","key":"214_CR40","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/nmeth.1923","volume":"9","author":"B Langmead","year":"2012","unstructured":"Langmead B, Salzberg SL. Fast gapped-read alignment with bowtie 2. Nat Methods. 2012;9(4):357\u20139. https:\/\/doi.org\/10.1038\/nmeth.1923.","journal-title":"Nat Methods"},{"key":"214_CR41","doi-asserted-by":"publisher","unstructured":"M\u00f6lder F, Jablonski KP, Letcher B, Hall MB, Tomkins-Tinch CH, Sochat V, et al.: Sustainable data analysis with Snakemake. F1000Research 10, 33, 2021. https:\/\/doi.org\/10.12688\/f1000research.29032.1","DOI":"10.12688\/f1000research.29032.1"},{"issue":"1","key":"214_CR42","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1038\/nmeth.2251","volume":"10","author":"A Roberts","year":"2013","unstructured":"Roberts A, Pachter L. Streaming fragment assignment for real-time analysis of sequencing experiments. Nature Methods. 2013;10(1):71\u20133. https:\/\/doi.org\/10.1038\/nmeth.2251.","journal-title":"Nature Methods"},{"key":"214_CR43","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-018-1554-6","author":"DJ Nasko","year":"2018","unstructured":"Nasko DJ, Koren S, Phillippy AM, Treangen TJ. RefSeq database growth influences the accuracy of k-mer-based lowest common ancestor species identification. Genome Biol. 2018. https:\/\/doi.org\/10.1186\/s13059-018-1554-6.","journal-title":"Genome Biol."}],"container-title":["Algorithms for Molecular Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13015-022-00214-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13015-022-00214-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13015-022-00214-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T03:04:58Z","timestamp":1648177498000},"score":1,"resource":{"primary":{"URL":"https:\/\/almob.biomedcentral.com\/articles\/10.1186\/s13015-022-00214-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,25]]},"references-count":43,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["214"],"URL":"https:\/\/doi.org\/10.1186\/s13015-022-00214-y","relation":{},"ISSN":["1748-7188"],"issn-type":[{"value":"1748-7188","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,25]]},"assertion":[{"value":"16 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 March 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 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":"RP is a co-founder of Ocean Genomics, Inc.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"6"}}