{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":["Lee EM"],"pubmed_abstract":["Metatranscriptomic (MTX) sequencing quantifies gene expression from the collective genomes of microbial communities (microbiomes), enabling assessment of functional activity rather than functional potential. While differential expression testing is instrumental to RNA-sequencing analysis, current metatranscriptomic approaches have been benchmarked only on simulated data and not under real operating conditions, resulting in a lack of standard practices. Here, we evaluate the performance of statistical differential expression methods on both simulated datasets and data collected from real bacterial 'mock communities' designed for this purpose. We assess the robustness of individual methods to organisms' low relative abundance, differential abundance, low prevalence, and transcription rate ch"],"journal":["bioRxiv : the preprint server for biology"],"pagination":["2025.09.11.675436"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12458460"],"repository":["biostudies-literature"],"pubmed_title":["Systematic evaluation of metatranscriptomic differential gene expression <i>in silico</i>, <i>in vitro</i>, and <i>in vivo</i> enables elucidation of inter-species cross-feeding."],"pmcid":["PMC12458460"],"pubmed_authors":["Chang HW","Gordon JI","Lee EM","McNulty NP","Cohen BA","Hibberd MC","Cheng J"],"additional_accession":[]},"is_claimable":false,"name":"Systematic evaluation of metatranscriptomic differential gene expression <i>in silico</i>, <i>in vitro</i>, and <i>in vivo</i> enables elucidation of inter-species cross-feeding.","description":"Metatranscriptomic (MTX) sequencing quantifies gene expression from the collective genomes of microbial communities (microbiomes), enabling assessment of functional activity rather than functional potential. While differential expression testing is instrumental to RNA-sequencing analysis, current metatranscriptomic approaches have been benchmarked only on simulated data and not under real operating conditions, resulting in a lack of standard practices. Here, we evaluate the performance of statistical differential expression methods on both simulated datasets and data collected from real bacterial 'mock communities' designed for this purpose. We assess the robustness of individual methods to organisms' low relative abundance, differential abundance, low prevalence, and transcription rate ch","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Sep","modification":"2026-05-04T03:21:03.235Z","creation":"2026-05-04T03:14:25.815Z"},"accession":"S-EPMC12458460","cross_references":{"pubmed":["41000806"],"doi":["10.1101/2025.09.11.675436"]}}