{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T18:07:26Z","timestamp":1790273246129,"version":"4.1.0"},"reference-count":38,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Human Glycome Atlas Project"},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["JP20H04282"],"award-info":[{"award-number":["JP20H04282"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Glycan biosynthesis requires the coordinated expression of glycosyltransferases, modifying enzymes, and nucleotide-sugar synthesis and transport machinery. Existing computational tools predict glycan structures from gene expression using binary thresholds, losing quantitative information about relative biosynthetic capacity across tissues. Here we present glycan biosynthetic reachability analysis, which integrates expression-based Z-scores across curated pathway steps using AND\/OR logic and minimum aggregation to produce continuous, tissue-comparable scores and an explicit expression-limiting step. Applied to 17,382 GTEx v8 RNA-seq samples across 54 human tissue types, reachability resolved quantitative differences hidden by presence\/absence calls; for example, pancreas was 96% binary-positive for the sLeX pathway but had low median reachability (\n                    <jats:inline-formula id=\"pcbi.1014655.e001\">\n                      <jats:alternatives>\n                        <jats:graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" id=\"pcbi.1014655.e001g\" mimetype=\"image\" position=\"anchor\" xlink:href=\"info:doi\/10.1371\/journal.pcbi.1014655.e001\" xlink:type=\"simple\"\/>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" id=\"M1\">\n                          <mml:mrow>\n                            <mml:mi>Z<\/mml:mi>\n                            <mml:mo>=<\/mml:mo>\n                            <mml:mo>\u2212<\/mml:mo>\n                            <mml:mn>1.86<\/mml:mn>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    ). Bottleneck stability was evaluated across all 19 multi-step metrics. In independent HEK293 knockout glycomics, the minimum score contained information beyond a within-knockout permutation null but did not outperform naive mean aggregation or binary topology. Nested leave-one-knockout-out selection favored a relaxed low quantile (\n                    <jats:italic>q<\/jats:italic>\n                    \u2009=\u20090.2) rather than validating the strict minimum. Within-GTEx associations between reachability and signaling-response transcripts are reported only as transcriptomic coherence because predictors and readouts share the same RNA-seq source. The mouse tissue-glycome comparison remained null. Reachability is therefore a hypothesis-generating rank of transcriptomic potential, not a measure of enzymatic activity or glycan abundance.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1014655","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T17:27:37Z","timestamp":1790270857000},"page":"e1014655","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Glycan reachability analysis: A bottleneck-aware framework for inferring tissue-specific glycan biosynthetic potential from transcriptomics"],"prefix":"10.1371","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3977-4313","authenticated-orcid":true,"given":"Yusuke","family":"Matsui","sequence":"first","affiliation":[{"name":"Nagoya University Graduate School of Medicine","place":["Nagoya, Aichi, Japan"],"department":["Biomedical and Health Informatics Unit, Department of Integrated Health Sciences"]},{"name":"Institute for Glyco-core Research (iGCORE), Nagoya University","place":["Nagoya, Aichi, Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"issue":"9","key":"pcbi.1014655.ref001","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1093\/glycob\/cwz045","article-title":"Updates to the symbol nomenclature for glycans guidelines","volume":"29","author":"S Neelamegham","year":"2019","journal-title":"Glycobiology"},{"issue":"24","key":"pcbi.1014655.ref002","doi-asserted-by":"crossref","first-page":"5413","DOI":"10.1093\/bioinformatics\/btac704","article-title":"GlycoEnzOnto: a GlycoEnzyme pathway and molecular function ontology","volume":"38","author":"T Groth","year":"2022","journal-title":"Bioinformatics"},{"issue":"1","key":"pcbi.1014655.ref003","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1093\/glycob\/cww086","article-title":"Biological roles of 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