Improving the value of public RNA-seq expression data by phenotype prediction.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:29514223", "kind": "science-record", "revision": 8, "content_hash": "sha256:a3cb7e002c1ea2c40b8255a12e14851cb95c78df1c946087ec34f254671878a2", "created_at": "2026-11-01T15:40:32Z", "updated_at": "2026-11-01T22:43:18Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Improving the value of public RNA-seq expression data by phenotype prediction.", "doi": "10.1093/nar/gky102", "pmid": "29514223", "authors": ["Shannon E Ellis", "Leonardo Collado-Torres", "Andrew Jaffe", "Jeffrey T Leek"], "journal": "Nucleic acids research", "year": 2018, "abstract": "Publicly available genomic data are a valuable resource for studying normal human variation and disease, but these data are often not well labeled or annotated. The lack of phenotype information for public genomic data severely limits their utility for addressing targeted biological questions. We develop an in silico phenotyping approach for predicting critical missing annotation directly from genomic measurements using well-annotated genomic and phenotypic data produced by consortia like TCGA and GTEx as training data. We apply in silico phenotyping to a set of 70 000 RNA-seq samples we recently processed on a common pipeline as part of the recount2 project. We use gene expression data to build and evaluate predictors for both biological phenotypes (sex, tissue, sample source) and experimental conditions (sequencing strategy). We demonstrate how these predictions can be used to study cross-sample properties of public genomic data, select genomic projects with specific characteristics, and perform downstream analyses using predicted phenotypes. The methods to perform phenotype prediction are available in the phenopredict R package and the predictions for recount2 are available from the recount R package. With data and phenotype information available for 70,000 human samples, expression data is available for use on a scale that was not previously feasible.", "claims": [{"id": "claim-method", "statement": "DGEA was carried out using the edgeR (24) R package, which assumes a negative binomial distribution for expression to detect differentially expressed genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29514223", "locator": "sections[25] \"Use case #3: Using predicted phenotypes in analyses\" paragraph 3", "quote": "DGEA was carried out using the edgeR (24) R package, which assumes a negative binomial distribution for expression to detect differentially expressed genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Analyses were all carried out in R 3.4.1. Code for the R package phenopredict is available at https://github.com/leekgroup/phenopredict. Code for prediction of each phenotype is available at https://github.com/ShanEllis/phenopredict_phenotypes. Use case code is available at https://github.com/ShanEllis/phenopredict_usecase. The R package recount.bwtool to extract region level data from samples in recount2 is available at https://github.com/LieberInstitute/recount.bwtool. The GitHub repositories that contain code used in this manuscript can be viewed at the precise version that was used at time of publication. The full code for these repositories can be downloaded via GitHub using the particular commit versions in Supplementary Table S8.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29514223", "locator": "sections[26] \"Availability of data and materials\" paragraph 2", "quote": "Analyses were all carried out in R 3.4.1. Code for the R package phenopredict is available at https://github.com/leekgroup/phenopredict. Code for prediction of each phenotype is available at https://github.com/ShanEllis/phenopredict_phenotypes. Use case code is available at https://github.com/ShanEllis/phenopredict_usecase. The R package recount.bwtool to extract region level data from samples in recount2 is available at https://github.com/LieberInstitute/recount.bwtool. The GitHub repositories that contain code used in this manuscript can be viewed at the precise version that was used at time of publication. 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