ArticleNucleic acids research2018
Improving the value of public RNA-seq expression data by phenotype prediction.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 70 citations in OpenAlex.
- A comparison of RNA-Seq data preprocessing pipelines for transcriptomic predictions across independent studies.BMC bioinformatics · 2024Article
- Genotype prediction of 336,463 samples from public expression data.bioRxiv : the preprint server for biology · 2024Article
- Differential Expression Enrichment Tool (DEET): an interactive atlas of human differential gene expression.NAR genomics and bioinformatics · 2023Article
- Application of Single-Cell RNA Sequencing in Ovarian Development.Biomolecules · 2022Review
- Systematic tissue annotations of genomics samples by modeling unstructured metadata.Nature communications · 2022Article
- Current progress and open challenges for applying deep learning across the biosciences.Nature communications · 2022Review
- recount3: summaries and queries for large-scale RNA-seq expression and splicing.Genome biology · 2021Article
- Bias-invariant RNA-sequencing metadata annotation.GigaScience · 2021Article
- Autosomal sex-associated co-methylated regions predict biological sex from DNA methylation.Nucleic acids research · 2021Article
- Expanding and Remixing the Metadata Landscape.Trends in cancer · 2021Review
- Large-scale labeling and assessment of sex bias in publicly available expression data.BMC bioinformatics · 2021Article
- Verifying explainability of a deep learning tissue classifier trained on RNA-seq data.Scientific reports · 2021Article
- Methods for correcting inference based on outcomes predicted by machine learning.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Maximizing the reusability of gene expression data by predicting missing metadata.PLoS computational biology · 2020Article
- The impact of sex on gene expression across human tissues.Science (New York, N.Y.) · 2020Article
- Recounting the FANTOM CAGE-Associated Transcriptome.Genome research · 2020Article
- Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data.BMC bioinformatics · 2020Article
- A Five-lncRNAs Signature-Derived Risk Score Based on TCGA and CGGA for Glioblastoma: Potential Prospects for Treatment Evaluation and Prognostic Prediction.Frontiers in oncology · 2020Article
- Investigating the effect of dependence between conditions with Bayesian Linear Mixed Models for motif activity analysis.PloS one · 2020Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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 OpenQuestion graph.