ArticleJHEP reports : innovation in hepatology2026
LiRNA: An interactive atlas of human liver RNAseq databases.
Article in JHEP reports : innovation in hepatology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
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Abstract
BACKGROUND &
aimsWhile mouse models remain a cornerstone of mechanistic liver disease research, the translational relevance of mouse findings to human disease is frequently debated. To bridge this gap, studies increasingly attempt to validate mouse findings using human transcriptomic data. However, publicly available human liver RNA-sequencing (RNA-seq) databases are fragmented, inconsistent in their clinical phenotyping, and computationally inaccessible to many investigators. We sought to generate an interactive atlas of human liver RNA-seq datasets - LiRNA - to overcome these limitations.
methodsWe identified 17 RNA-seq datasets generated from over 3,000 human liver biopsies and integrated them into a unified, open-access interactive web application (LiRNA) following harmonized FASTQ processing. We inferred biological sex from transcriptomic markers and genotyped four MASLD-associated variants (PNPLA3 rs738409, GCKR rs1260326, TM6SF2 rs58542926, MTARC1 rs2642438). We then systematically evaluated the generalizability of 64 previously reported gene-phenotype and gene-gene correlation findings from eleven leading hepatology and general science journals.
resultsLiRNA accurately recapitulates established hepatic biology, capturing known fibrosis-associated transcripts, sexually dimorphic gene expression, HCV-response signatures, and genotype-specific transcriptional signatures across multiple datasets. When 64 recent translational findings were assessed against independent, larger cohorts in LiRNA, fewer than half generalized consistently.
conclusionsLiRNA is an open-access, interactive platform integrating harmonized RNA-sequencing data from over 3,000 human liver biopsies. It facilitates contextualizing translational findings across diverse human populations, disease contexts, and clinical settings. IMPACT AND IMPLICATIONS: Researchers increasingly attempt to validate mouse findings using human transcriptomic data. However, publicly available human liver RNA-sequencing (RNA-seq) databases are fragmented, inconsistent in their clinical phenotyping, and computationally inaccessible to many investigators. By lowering the computational barrier to multi-dataset analysis, LiRNA provides a resource for improving the rigor and generalizability of translational findings in liver disease research.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.