{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T00:37:04Z","timestamp":1705106224923},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"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":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Metagene plots provide a visualization of biological signal trends over subsections of the genome and are used to perform high-level analysis of experimental data by aggregating genome-level data to create an average profile. The generation of metagene plots is useful for summarizing the results of many sequencing-based applications. Despite their prevalence and utility, the standard metagene plot is blind to conflicting signals within data. If multiple distinct trends occur, they can interact destructively, creating a plot that does not accurately represent any of the underlying trends.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We present MetageneCluster, a Python tool to generate a collection of representative metagene plots based on k-means clustering of genomic regions of interest. Clustering the data by similarity allows us to identify patterns within the features of interest. We are then able to summarize each pattern present in the data, rather than averaging across the entire feature space. We show that our method performs well when used to identify conflicting signals in real-world genome-level data.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Overall, MetageneCluster is a user-friendly tool for the creation of metagene plots that capture distinct patterns in underlying sequence data.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-024-05647-3","type":"journal-article","created":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T10:02:35Z","timestamp":1705053755000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MetageneCluster: a Python package for filtering conflicting signal trends in metagene plots"],"prefix":"10.1186","volume":"25","author":[{"given":"Clayton","family":"Carter","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aaron","family":"Saporito","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephen M.","family":"Douglass","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,12]]},"reference":[{"issue":"4","key":"5647_CR1","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1093\/bioinformatics\/btx633","volume":"34","author":"X Huang","year":"2018","unstructured":"Huang X, Zhang S, Li K, Thimmapuram J, Xie S. ViewBS: a powerful toolkit for visualization of high-throughput bisulfite sequencing data. Bioinformatics. 2018;34(4):708\u20139.","journal-title":"Bioinformatics"},{"key":"5647_CR2","doi-asserted-by":"publisher","first-page":"e1004751","DOI":"10.1371\/journal.pcbi.1004751","volume":"12","author":"CJ Beauparlant","year":"2016","unstructured":"Beauparlant CJ, Lamaze FC, Desch\u00eanes A, Samb R, Lema\u00e7on A, Belleau P, Bilodeau S, Droit A. metagene Profiles analyses reveal regulatory element\u2019s factor-specific recruitment patterns. PLoS Comput Biol. 2016;12:e1004751.","journal-title":"PLoS Comput Biol"},{"key":"5647_CR3","doi-asserted-by":"publisher","first-page":"19545","DOI":"10.1038\/s41598-021-98889-7","volume":"11","author":"E Blanco","year":"2021","unstructured":"Blanco E, Gonz\u00e1lez-Ram\u00edrez M, Di Croce L. Productive visualization of high-throughput sequencing data using the SeqCode open portable platform. Sci Rep. 2021;11:19545. https:\/\/doi.org\/10.1038\/s41598-021-98889-7.","journal-title":"Sci Rep"},{"key":"5647_CR4","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1186\/s12859-020-03670-8","volume":"21","author":"F Li","year":"2020","unstructured":"Li F, Xing X, Xiao Z, et al. RiboMiner: a toolset for mining multi-dimensional features of the translatome with ribosome profiling data. BMC Bioinform. 2020;21:340. https:\/\/doi.org\/10.1186\/s12859-020-03670-8.","journal-title":"BMC Bioinform"},{"issue":"10","key":"5647_CR5","doi-asserted-by":"publisher","first-page":"1563","DOI":"10.1093\/bioinformatics\/btx002","volume":"33","author":"AO Olarerin-George","year":"2017","unstructured":"Olarerin-George AO, Jaffrey SR. MetaPlotR: a Perl\/R pipeline for plotting metagenes of nucleotide modifications and other transcriptomic sites. Bioinformatics. 2017;33(10):1563\u20134.","journal-title":"Bioinformatics"},{"issue":"1","key":"5647_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40529-022-00366-5","volume":"64","author":"RJ Lu","year":"2023","unstructured":"Lu RJ, Lin PY, Yen MR, Wu BH, Chen PY. MethylC-analyzer: a comprehensive downstream pipeline for the analysis of genome-wide DNA methylation. Bot Stud. 2023;64(1):1. https:\/\/doi.org\/10.1186\/s40529-022-00366-5. (Erratum in: Bot Stud. 2023 May 30;64(1):13).","journal-title":"Bot Stud"},{"issue":"1","key":"5647_CR7","doi-asserted-by":"publisher","first-page":"083","DOI":"10.1093\/bioadv\/vbac083","volume":"2","author":"HL Her","year":"2022","unstructured":"Her HL, Boyle E, Yeo GW. Metadensity: a background-aware python pipeline for summarizing CLIP signals on various transcriptomic sites. Bioinform Adv. 2022;2(1):083. https:\/\/doi.org\/10.1093\/bioadv\/vbac083.","journal-title":"Bioinform Adv"},{"key":"5647_CR8","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btu775","author":"A Akalin","year":"2014","unstructured":"Akalin A, Franke V, Vlahovicek K, Mason C, Schubeler D. genomation: a toolkit to summarize, annotate and visualize genomic intervals. Bioinformatics. 2014. https:\/\/doi.org\/10.1093\/bioinformatics\/btu775.","journal-title":"Bioinformatics"},{"issue":"1","key":"5647_CR9","doi-asserted-by":"publisher","first-page":"19545","DOI":"10.1038\/s41598-021-98889-7","volume":"11","author":"E Blanco","year":"2021","unstructured":"Blanco E, Gonz\u00e1lez-Ram\u00edrez M, Di Croce L. Productive visualization of high-throughput sequencing data using the SeqCode open portable platform. Sci Rep. 2021;11(1):19545. https:\/\/doi.org\/10.1038\/s41598-021-98889-7.","journal-title":"Sci Rep"},{"key":"5647_CR10","doi-asserted-by":"publisher","first-page":"39501","DOI":"10.1109\/ACCESS.2018.2855437","volume":"6","author":"E Min","year":"2018","unstructured":"Min E, Guo X, Liu Q, et al. A survey of clustering with deep learning: from the perspective of network architecture. IEEE Access. 2018;6:39501\u201314.","journal-title":"IEEE Access"},{"key":"5647_CR11","doi-asserted-by":"crossref","unstructured":"Gan G, Ma C, Wu J. Data clustering: theory, algorithms, and applications, vol. 20. SIAM, 2007.","DOI":"10.1137\/1.9780898718348"},{"key":"5647_CR12","doi-asserted-by":"crossref","unstructured":"Oyelade J, Isewon I, Funke, et al. Clustering algorithms: their application to gene expression data. Bioinform Biol Insights 2016;10:237\u2013253.","DOI":"10.4137\/BBI.S38316"},{"key":"5647_CR13","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1186\/s12859-019-3223-5","volume":"20","author":"R Mawhorter","year":"2019","unstructured":"Mawhorter R, Libeskind-Hadas R. Hierarchical clustering of maximum parsimony reconciliations. BMC Bioinform. 2019;20:612.","journal-title":"BMC Bioinform"},{"key":"5647_CR14","doi-asserted-by":"publisher","first-page":"RESEARCH0087","DOI":"10.1186\/gb-2002-3-12-research0087","volume":"3","author":"W Ohler","year":"2002","unstructured":"Ohler W, Liao C, Niemann H, Rubin GM. Computational analysis of core promoters in the drosophila genome. Genome Biol. 2002;3:RESEARCH0087.","journal-title":"Genome Biol"},{"key":"5647_CR15","doi-asserted-by":"publisher","first-page":"S75","DOI":"10.1093\/bioinformatics\/18.suppl_2.S75","volume":"18","author":"S Degroeve","year":"2002","unstructured":"Degroeve S, Baets BD, de Peer YV, Rouz P. Feature subset selection for splice site prediction. Bioinformatics. 2002;18:S75\u201383.","journal-title":"Bioinformatics"},{"key":"5647_CR16","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1038\/75556","volume":"25","author":"M Ashburner","year":"2000","unstructured":"Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G. Gene ontology: tool for the unification of biology. Nat Genet. 2000;25:25\u20139.","journal-title":"Nat Genet"},{"key":"5647_CR17","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","volume":"585","author":"CR Harris","year":"2020","unstructured":"Harris CR, Millman KJ, van der Walt SJ, et al. Array programming with NumPy. Nature. 2020;585:357\u201362.","journal-title":"Nature"},{"issue":"3","key":"5647_CR18","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1109\/MCSE.2007.55","volume":"9","author":"JD Hunter","year":"2007","unstructured":"Hunter JD. Matplotlib: a 2D graphics environment. Comput Sci Eng. 2007;9(3):90\u20135.","journal-title":"Comput Sci Eng"},{"issue":"13","key":"5647_CR19","doi-asserted-by":"publisher","first-page":"3760","DOI":"10.1016\/j.celrep.2019.05.100","volume":"27","author":"CS Leung","year":"2019","unstructured":"Leung CS, Douglass SM, Morselli M, Obusan MB, Pavlyukov MS, Pellegrini M, Johnson TL. H3K36 methylation and the chromodomain protein Eaf3 are required for proper cotranscriptional spliceosome assembly. Cell Rep. 2019;27(13):3760\u20139.","journal-title":"Cell Rep"},{"issue":"1","key":"5647_CR20","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1038\/s41467-022-28468-5","volume":"13","author":"TK To","year":"2022","unstructured":"To TK, Yamasaki C, Oda S, Tominaga S, Kobayashi A, Tarutani Y, Kakutani T. Local and global crosstalk among heterochromatin marks drives DNA methylome patterning in Arabidopsis. Nat Commun. 2022;13(1):861.","journal-title":"Nat Commun"},{"issue":"1","key":"5647_CR21","doi-asserted-by":"publisher","first-page":"iyad031","DOI":"10.1093\/genetics\/iyad031","volume":"224","author":"SA Aleksander","year":"2023","unstructured":"Aleksander SA, Balhoff J, Carbon S, Cherry JA, Drabkin HJ, Ebert D, et al. The Gene Ontology knowledgebase in 2023. Genetics. 2023;224(1):iyad031.","journal-title":"Genetics"},{"issue":"1","key":"5647_CR22","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1002\/pro.4218","volume":"31","author":"PD Thomas","year":"2021","unstructured":"Thomas PD, Ebert D, Muruganujan A, Mushayahama T, Albou LP, Mi H. PANTHER: making genome-scale phylogenetics accessible to all. Protein Sci. 2021;31(1):8\u201322.","journal-title":"Protein Sci"},{"issue":"1","key":"5647_CR23","doi-asserted-by":"publisher","first-page":"603","DOI":"10.3390\/ijms24010603","volume":"24","author":"C Vahlensieck","year":"2022","unstructured":"Vahlensieck C, Thiel CS, Christoffel S, Herbst S, Polzer J, Lauber BA, et al. Rapid downregulation of H3K4me3 binding to immunoregulatory genes in altered gravity in primary human M1 macrophages. Int J Mol Sci. 2022;24(1):603.","journal-title":"Int J Mol Sci"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-024-05647-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-024-05647-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-024-05647-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T10:03:00Z","timestamp":1705053780000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-024-05647-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,12]]},"references-count":23,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["5647"],"URL":"https:\/\/doi.org\/10.1186\/s12859-024-05647-3","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,12]]},"assertion":[{"value":"18 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2024","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 no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"21"}}