{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:04Z","timestamp":1772138044642,"version":"3.50.1"},"reference-count":19,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T00:00:00Z","timestamp":1626048000000},"content-version":"vor","delay-in-days":11,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation Graduate Research Fellowship Program","award":["DGE1745016"],"award-info":[{"award-number":["DGE1745016"]}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"US National Science Foundation","doi-asserted-by":"crossref","award":["1937540"],"award-info":[{"award-number":["1937540"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000936","name":"Gordon and Betty Moore Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000936","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Data-Driven Discovery Initiative","award":["GBMF4554"],"award-info":[{"award-number":["GBMF4554"]}]},{"DOI":"10.13039\/100000002","name":"US National Institutes of Health","doi-asserted-by":"crossref","award":["R01GM122935"],"award-info":[{"award-number":["R01GM122935"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Despite numerous RNA-seq samples available at large databases, most RNA-seq analysis tools are evaluated on a limited number of RNA-seq samples. This drives a need for methods to select a representative subset from all available RNA-seq samples to facilitate comprehensive, unbiased evaluation of bioinformatics tools. In sequence-based approaches for representative set selection (e.g. a k-mer counting approach that selects a subset based on k-mer similarities between RNA-seq samples), because of the large numbers of available RNA-seq samples and of k-mers\/sequences in each sample, computing the full similarity matrix using k-mers\/sequences for the entire set of RNA-seq samples in a large database (e.g. the SRA) has memory and runtime challenges; this makes direct representative set selection infeasible with limited computing resources.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We developed a novel computational method called \u2018hierarchical representative set selection\u2019 to handle this challenge. Hierarchical representative set selection is a divide-and-conquer-like algorithm that breaks representative set selection into sub-selections and hierarchically selects representative samples through multiple levels. We demonstrate that hierarchical representative set selection can achieve summarization quality close to that of direct representative set selection, while largely reducing runtime and memory requirements of computing the full similarity matrix (up to 8.4\u00d7 runtime reduction and 5.35\u00d7 memory reduction for 10 000 and 12 000 samples respectively that could be practically run with direct subset selection). We show that hierarchical representative set selection substantially outperforms random sampling on the entire SRA set of RNA-seq samples, making it a practical solution to representative set selection on large databases like the SRA.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The code is available at https:\/\/github.com\/Kingsford-Group\/hierrepsetselection and https:\/\/github.com\/Kingsford-Group\/jellyfishsim.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab315","type":"journal-article","created":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T15:18:09Z","timestamp":1619795889000},"page":"i334-i341","source":"Crossref","is-referenced-by-count":4,"title":["Practical selection of representative sets of RNA-seq samples using a hierarchical approach"],"prefix":"10.1093","volume":"37","author":[{"given":"Laura H","family":"Tung","sequence":"first","affiliation":[{"name":"Computational Biology Department, School of Computer Science, Carnegie Mellon University , Pittsburgh, PA 15213, USA"}]},{"given":"Carl","family":"Kingsford","sequence":"additional","affiliation":[{"name":"Computational Biology Department, School of Computer Science, Carnegie Mellon University , Pittsburgh, PA 15213, USA"}]}],"member":"286","published-online":{"date-parts":[[2021,7,12]]},"reference":[{"issue":"(D1)","key":"2023062410305155900_btab315-B1","doi-asserted-by":"crossref","first-page":"D57","DOI":"10.1093\/nar\/gkr1163","article-title":"BioProject and BioSample databases at NCBI: facilitating capture and organization of metadata","volume":"40","author":"Barrett","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2023062410305155900_btab315-B2","author":"Boutsidis","year":"2009"},{"key":"2023062410305155900_btab315-B3","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-73301-2","volume-title":"Numerical Geometry of Non-Rigid Shapes","author":"Bronstein","year":"2009"},{"issue":"2","key":"2023062410305155900_btab315-B4","doi-asserted-by":"crossref","first-page":"giy165","DOI":"10.1093\/gigascience\/giy165","article-title":"Libra: scalable k-mer-based tool for massive all-vs-all metagenome comparisons","volume":"8","author":"Choi","year":"2019","journal-title":"GigaScience"},{"issue":"1","key":"2023062410305155900_btab315-B5","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/S0003-2670(02)00651-7","article-title":"Representative subset selection","volume":"468","author":"Daszykowski","year":"2002","journal-title":"Anal. 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