{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T13:30:12Z","timestamp":1773495012773,"version":"3.50.1"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2019,11,5]],"date-time":"2019-11-05T00:00:00Z","timestamp":1572912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"National Institute of General Medical Sciences of the National Institutes of Health","award":["GM075742"],"award-info":[{"award-number":["GM075742"]}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The increasing availability of annotated genome sequences enables construction of genome-scale metabolic networks, which are useful tools for studying organisms of interest. However, due to incomplete genome annotations, draft metabolic models contain gaps that must be filled in a time-consuming process before they are usable. Optimization-based algorithms that fill these gaps have been developed, however, gap-filling algorithms show significant error rates and often introduce incorrect reactions.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Here, we present a new gap-filling method that computes the costs of candidate gap-filling reactions from a universal reaction database (MetaCyc) based on taxonomic information. When gap-filling a metabolic model for an organism M (such as Escherichia coli), the cost for reaction R is based on the frequency with which R occurs in other organisms within the phylum of M (in this case, Proteobacteria). The assumption behind this method is that different taxonomic groups are biased toward using different metabolic reactions. Evaluation of the new gap-filler on randomly degraded variants of the EcoCyc metabolic model for E.coli showed an increase in the average F1-score to 99.0 (when using the variable weights by frequency method at the phylum level), compared to 91.0 using the previous MetaFlux gap-filler and 80.3 using a basic gap-filler. Evaluation on two other microbial metabolic models showed similar improvements.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The Pathway Tools software (including MetaFlux) is free for academic use and is available at http:\/\/pathwaytools.com. Additional code for reproducing the results presented here is available at www.ai.sri.com\/pkarp\/pubs\/taxgap\/supplementary.zip.<\/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\/btz813","type":"journal-article","created":{"date-parts":[[2019,11,1]],"date-time":"2019-11-01T12:59:29Z","timestamp":1572613169000},"page":"1823-1830","source":"Crossref","is-referenced-by-count":6,"title":["Taxonomic weighting improves the accuracy of a gap-filling algorithm for metabolic models"],"prefix":"10.1093","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0972-1499","authenticated-orcid":false,"given":"Wai Kit","family":"Ong","sequence":"first","affiliation":[{"name":"Bioinformatics Research Group, Artificial Intelligence Center, SRI International , Menlo Park, CA 94025, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter E","family":"Midford","sequence":"additional","affiliation":[{"name":"Bioinformatics Research Group, Artificial Intelligence Center, SRI International , Menlo Park, CA 94025, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter D","family":"Karp","sequence":"additional","affiliation":[{"name":"Bioinformatics Research Group, Artificial Intelligence Center, SRI International , Menlo Park, CA 94025, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2019,11,5]]},"reference":[{"key":"2023060911591346400_btz813-B1","doi-asserted-by":"crossref","first-page":"D633","DOI":"10.1093\/nar\/gkx935","article-title":"The MetaCyc database of metabolic pathways and enzymes","volume":"46","author":"Caspi","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2023060911591346400_btz813-B2","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1186\/1471-2105-8-139","article-title":"Toward the automated generation of genome-scale metabolic networks in the SEED","volume":"8","author":"DeJongh","year":"2007","journal-title":"BMC Bioinformatics"},{"key":"2023060911591346400_btz813-B3","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1042\/BST20170246","article-title":"Methods for automated genome-scale metabolic model reconstruction","volume":"46","author":"Faria","year":"2018","journal-title":"Biochem. 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