{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T13:47:57Z","timestamp":1791812877437,"version":"4.3.4"},"reference-count":22,"publisher":"F1000 Research Ltd","license":[{"start":{"date-parts":[[2020,2,12]],"date-time":"2020-02-12T00:00:00Z","timestamp":1581465600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01HG009937"],"award-info":[{"award-number":["R01HG009937"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008322","name":"CSL Behring","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008322","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000934","name":"Department of Industry, Innovation, Science, Research and Tertiary Education, Australian Government","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000934","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["f1000research.com"],"crossmark-restriction":false},"short-container-title":["F1000Res"],"abstract":"<ns4:p>\n                    We construct a simple workflow for fluent genomics data analysis using the R\/Bioconductor ecosystem. This involves three core steps:\n                    <ns4:bold>import<\/ns4:bold>\n                    \u00a0the data into an appropriate abstraction,\n                    <ns4:bold>model<\/ns4:bold>\n                    the data with respect to the biological questions of interest, and\n                    <ns4:bold>integrate<\/ns4:bold>\n                    the results with respect to their underlying genomic coordinates. Here we show how to implement these steps to integrate published RNA-seq and ATAC-seq experiments on macrophage cell lines. Using\n                    <ns4:italic>tximeta<\/ns4:italic>\n                    , we\n                    <ns4:bold>import<\/ns4:bold>\n                    \u00a0RNA-seq transcript quantifications into an analysis-ready data structure, called the\n                    <ns4:italic>SummarizedExperiment<\/ns4:italic>\n                    , that contains the ranges of the reference transcripts and metadata on their provenance. Using\n                    <ns4:italic>SummarizedExperiment<\/ns4:italic>\n                    s to represent the ATAC-seq and RNA-seq data, we\n                    <ns4:bold>model<\/ns4:bold>\n                    differentially accessible (DA) chromatin peaks and differentially expressed (DE) genes with existing Bioconductor packages. Using\n                    <ns4:italic>plyranges<\/ns4:italic>\n                    \u00a0we then\n                    <ns4:bold>integrate<\/ns4:bold>\n                    \u00a0the results to see if there is an enrichment of DA peaks near DE genes by finding overlaps and aggregating over log-fold change thresholds. The combination of these packages and their integration with the Bioconductor ecosystem provide a coherent framework for analysts to iteratively and reproducibly explore their biological data.\n                  <\/ns4:p>","DOI":"10.12688\/f1000research.22259.1","type":"journal-article","created":{"date-parts":[[2020,2,12]],"date-time":"2020-02-12T07:35:05Z","timestamp":1581492905000},"page":"109","update-policy":"https:\/\/doi.org\/10.12688\/f1000research.crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Fluent genomics with\u00a0plyranges\u00a0and\u00a0tximeta"],"prefix":"10.12688","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1179-8436","authenticated-orcid":false,"given":"Stuart","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Lawrence","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael I.","family":"Love","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2560","published-online":{"date-parts":[[2020,2,12]]},"reference":[{"key":"ref-1","doi-asserted-by":"publisher","first-page":"424-31","DOI":"10.1038\/s41588-018-0046-7","article-title":"Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response.","volume":"50","author":"K Alasoo","year":"2018","journal-title":"Nat Genet."},{"key":"ref-2","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.1188300","article-title":"Processed read counts from macrophage RNA-seq and ATAC-seq experiments.","author":"K Alasoo","year":"2017","journal-title":"Zenodo."},{"key":"ref-3","article-title":"Rmarkdown: Dynamic Documents for R","author":"J Allaire","year":"2019"},{"key":"ref-4","doi-asserted-by":"publisher","first-page":"D766-D773","DOI":"10.1093\/nar\/gky955","article-title":"GENCODE reference annotation for the human and mouse genomes.","volume":"47","author":"A Frankish","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"ref-5","doi-asserted-by":"publisher","first-page":"204-16","DOI":"10.1093\/biostatistics\/kxr054","article-title":"Removing technical variability in RNA-seq data using conditional quantile normalization.","volume":"13","author":"K Hansen","year":"2012","journal-title":"Biostatistics."},{"key":"ref-6","doi-asserted-by":"publisher","first-page":"115-21","DOI":"10.1038\/nmeth.3252","article-title":"Orchestrating high-throughput genomic analysis with Bioconductor.","volume":"12","author":"W Huber","year":"2015","journal-title":"Nat Methods."},{"key":"ref-7","doi-asserted-by":"publisher","first-page":"2520-2","DOI":"10.1093\/bioinformatics\/bts480","article-title":"Snakemake--a scalable bioinformatics workflow engine.","volume":"28","author":"J K\u00f6ster","year":"2012","journal-title":"Bioinformatics."},{"key":"ref-8","doi-asserted-by":"publisher","first-page":"1408","DOI":"10.12688\/f1000research.9005.3","article-title":"RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR [version 3; peer review: 3 approved].","volume":"5","author":"C Law","year":"2018","journal-title":"F1000 Res."},{"key":"ref-9","doi-asserted-by":"publisher","first-page":"e1003118","DOI":"10.1371\/journal.pcbi.1003118","article-title":"Software for computing and annotating genomic ranges.","volume":"9","author":"M Lawrence","year":"2013","journal-title":"PLoS Comput Biol."},{"key":"ref-10","article-title":"sa-lee\/fluentGenomics: Bioconductor @ accepted version.","author":"M Love","year":"2020","journal-title":"Zenodo."},{"key":"ref-11","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1186\/s13059-018-1597-8","article-title":"plyranges: a grammar of genomic data transformation.","volume":"20","author":"S Lee","year":"2019","journal-title":"Genome Biol."},{"key":"ref-12","doi-asserted-by":"publisher","first-page":"1070","DOI":"10.12688\/f1000research.7035.1","article-title":"RNA-Seq workflow: gene-level exploratory analysis and differential expression [version 1; peer review: 2 approved].","volume":"4","author":"M Love","year":"2016","journal-title":"F1000 Res."},{"key":"ref-13","doi-asserted-by":"publisher","first-page":"777888","DOI":"10.1101\/777888","article-title":"Tximeta: Reference Sequence Checksums for Provenance Identification in RNA-seq.","author":"M Love","year":"2019","journal-title":"bioRxiv."},{"key":"ref-14","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1186\/s13059-014-0550-8","article-title":"Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.","volume":"15","author":"M Love","year":"2014","journal-title":"Genome Biol."},{"key":"ref-15","doi-asserted-by":"publisher","first-page":"417-19","DOI":"10.1038\/nmeth.4197","article-title":"Salmon provides fast and bias-aware quantification of transcript expression.","volume":"14","author":"R Patro","year":"2017","journal-title":"Nat Methods."},{"key":"ref-16","article-title":"R: A Language and Environment for Statistical Computing.","year":"2019"},{"key":"ref-17","article-title":"BiocFileCache: Manage Files Across Sessions","author":"L Shepherd","year":"2019"},{"key":"ref-18","doi-asserted-by":"publisher","DOI":"10.2202\/1544-6115.1027","article-title":"Linear models and empirical bayes methods for assessing differential expression in microarray experiments.","volume":"3","author":"G Smyth","year":"2004","journal-title":"Stat Appl Genet Mol Biol."},{"key":"ref-19","doi-asserted-by":"publisher","first-page":"1521","DOI":"10.12688\/f1000research.7563.2","article-title":"Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences [version 2; peer review: 2 approved].","volume":"4","author":"C Soneson","year":"2015","journal-title":"F1000Res."},{"key":"ref-20","doi-asserted-by":"publisher","first-page":"1686","DOI":"10.21105\/joss.01686","article-title":"Welcome to the tidyverse.","volume":"4","author":"H Wickham","year":"2019","journal-title":"J Open Source Softw."},{"key":"ref-21","doi-asserted-by":"publisher","DOI":"10.1201\/9781315382487","article-title":"Dynamic Documents with R and Knitr.","author":"Y Xie","year":"2015"},{"key":"ref-22","article-title":"Knitr: A General-Purpose Package for Dynamic Report Generation in R","author":"Y Xie","year":"2019"}],"container-title":["F1000Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/f1000research.com\/articles\/9-109\/v1\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/f1000research.com\/articles\/9-109\/v1\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/f1000research.com\/articles\/9-109\/v1\/iparadigms","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,5,21]],"date-time":"2020-05-21T09:55:18Z","timestamp":1590054918000},"score":1,"resource":{"primary":{"URL":"https:\/\/f1000research.com\/articles\/9-109\/v1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,12]]},"references-count":22,"URL":"https:\/\/doi.org\/10.12688\/f1000research.22259.1","relation":{"has-review":[{"id-type":"doi","id":"10.5256\/f1000research.24553.r59959","asserted-by":"subject"},{"id-type":"doi","id":"10.5256\/f1000research.24553.r60092","asserted-by":"subject"},{"id-type":"doi","id":"10.5256\/f1000research.24553.r61441","asserted-by":"subject"},{"id-type":"doi","id":"10.5256\/f1000research.24553.r60092","asserted-by":"object"},{"id-type":"doi","id":"10.5256\/f1000research.24553.r59959","asserted-by":"object"},{"id-type":"doi","id":"10.5256\/f1000research.24553.r61441","asserted-by":"object"}]},"ISSN":["2046-1402"],"issn-type":[{"value":"2046-1402","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,12]]},"assertion":[{"value":"Indexed","URL":"https:\/\/f1000research.com\/articles\/9-109\/v1#article-reports","order":0,"name":"referee-status","label":"Referee status","group":{"name":"current-referee-status","label":"Current Referee Status"}},{"value":"10.5256\/f1000research.24553.r60092, Mark Dunning, Sheffield Bioinformatics Core, University of Sheffield, Sheffield, UK, 10 Mar 2020, version 1, indexed","URL":"https:\/\/f1000research.com\/articles\/9-109\/v1#referee-response-60092","order":0,"name":"referee-response-60092","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.5256\/f1000research.24553.r61441, Davis J. McCarthy, Ruqian Lyu, PuXue Qiao, St Vincent\u2019s Institute of Medical Research, Fitzroy, Victoria, Australia, 30 Apr 2020, version 1, indexed","URL":"https:\/\/f1000research.com\/articles\/9-109\/v1#referee-response-61441","order":1,"name":"referee-response-61441","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.5256\/f1000research.24553.r59959, Michael M. Hoffman, Micka\u00ebl Mendez, Princess Margaret Cancer Centre, Toronto, ON, Canada, 21 May 2020, version 1, indexed","URL":"https:\/\/f1000research.com\/articles\/9-109\/v1#referee-response-59959","order":2,"name":"referee-response-59959","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"SL is supported by an Australian Government Research Training Program (RTP)\nscholarship with a top up scholarship from CSL Limited. MIL is supported by NIH grant R01 HG009937. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","order":3,"name":"grant-information","label":"Grant Information"},{"value":"This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","order":0,"name":"copyright-info","label":"Copyright"}]}}