{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T18:20:19Z","timestamp":1788373219167,"version":"build-2803163510"},"reference-count":75,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T00:00:00Z","timestamp":1788307200000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Human RNA sequencing (RNA-seq) data originally generated for human transcriptome profiling are overwhelmingly dominated by host sequences, yet they often contain a small fraction of non-human reads that can be exploited for microbial detection. When such datasets are repurposed for secondary microbiome-oriented analyses, extracting and accurately classifying this weak microbial signal becomes technically challenging, and no ready-to-use pipeline currently exists.<\/jats:p>\n                  <jats:p>In this study, we evaluate computational strategies for filtering host reads and classifying microbial transcripts in host-dominated RNA sequencing data. We compare assembly-based approaches similar to those used in a previous study focusing on microbial translocation with state-of-the-art assembly-free methods, and assess their respective strengths and limitations using simulated datasets reflecting low microbial abundance. Our results show that assembly-based methods yield accurate taxonomic predictions but struggle at low read depth, whereas assembly-free methods are more robust in sparse settings at the cost of reduced precision.<\/jats:p>\n                  <jats:p>To leverage the complementarity of both approaches, we propose a hybrid pipeline that integrates assembly-based and assembly-free classification. On simulated data, this hybrid strategy improves microbial classification performance compared with either approach alone. Application to a real human metatranscriptomic dataset analyzed in a microbial translocation context illustrates the broader microbial signal captured by the hybrid approach, despite intrinsic challenges related to the absence of reliable ground truth and the risk of host read misclassification.<\/jats:p>\n                  <jats:p>Our work provides a framework for extracting microbial signals from host-dominated human metatranscriptomes, enabling the reuse of existing transcriptomic datasets for microbiome-related analyses, including but not limited to microbial translocation studies.<\/jats:p>","DOI":"10.1093\/bib\/bbag454","type":"journal-article","created":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T18:05:56Z","timestamp":1788372356000},"source":"Crossref","is-referenced-by-count":0,"title":["Benchmarking methods for extracting microbial signal from host-dominated metatranscriptomes"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1523-3941","authenticated-orcid":false,"given":"Antonin","family":"Colajanni","sequence":"first","affiliation":[{"name":"INSERM, INRIA, BPH, U1219, SISTM, University of Bordeaux , 146 rue L\u00e9o Saignat, F-33000 Bordeaux ,","place":["France"]},{"name":"CNRS, Bordeaux INP, LaBRI, UMR 5800, University of Bordeaux , 351 cours de la Lib\u00e9ration, F-33405 Talence ,","place":["France"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5730-6428","authenticated-orcid":false,"given":"Raluca","family":"Uricaru","sequence":"additional","affiliation":[{"name":"CNRS, Bordeaux INP, LaBRI, UMR 5800, University of Bordeaux , 351 cours de la Lib\u00e9ration, F-33405 Talence ,","place":["France"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel","family":"Darko","sequence":"additional","affiliation":[{"name":"Human Immunology Section, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health , 40 Convent Drive, Bethesda, MD 20892 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rahul","family":"Subramanian","sequence":"additional","affiliation":[{"name":"Human Immunology Section, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health , 40 Convent Drive, Bethesda, MD 20892 ,","place":["United States"]},{"name":"Office of Data Science and Emerging Technologies, Office of Science Management and Operations, National Institute of Allergy and Infectious Diseases, National Institutes of Health , 5601 Fishers Lane, Rockville, MD 20852 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel C","family":"Douek","sequence":"additional","affiliation":[{"name":"Human Immunology Section, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health , 40 Convent Drive, Bethesda, MD 20892 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rodolphe","family":"Thi\u00e9baut","sequence":"additional","affiliation":[{"name":"INSERM, INRIA, BPH, U1219, SISTM, University of Bordeaux , 146 rue L\u00e9o Saignat, F-33000 Bordeaux ,","place":["France"]},{"name":"CHU de Bordeaux, Service d\u2019Information M\u00e9dicale, INSERM, U1219 , Place Am\u00e9lie Raba L\u00e9on, F-33076 Bordeaux Cedex, Bordeaux ,","place":["France"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patricia","family":"Thebault","sequence":"additional","affiliation":[{"name":"CNRS, Bordeaux INP, LaBRI, UMR 5800, University of Bordeaux , 351 cours de la Lib\u00e9ration, F-33405 Talence 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