ArticleBMC bioinformatics2022
Impact of gene annotation choice on the quantification of RNA-seq data.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Bramble: projection of spliced genomic alignments into transcriptomic space for improved transcript quantification.bioRxiv : the preprint server for biology · 2026Article
- SNPWay: streamlined SNP-to-function and pathway over-representation analysis.bioRxiv : the preprint server for biology · 2026Article
- From SNPs to pathways: a genome-wide benchmark of annotation discrepancies and their impact on protein- and pathway-level inference.BMC genomics · 2026Article
- Improving isoform-level eQTL and integrative genetic analyses of breast cancer risk with long-read RNA transcript assemblies.bioRxiv : the preprint server for biology · 2026Article
- From SNPs to Pathways: A genome-wide benchmark of annotation discrepancies and their impact on protein- and pathway-level inference.bioRxiv : the preprint server for biology · 2026Article
- The osteoclast intracellular environment fosters bacterial growth duringbioRxiv : the preprint server for biology · 2026Article
- Designing RNA sequencing experiments: A practical guide to reproducible gene expression analysis.Computational and structural biotechnology journal · 2026Review
- Tumor mutational burden, gene expression patterns, and immune-cell deconvolution in testicular germ cell tumors.Translational andrology and urology · 2025Article
- Comprehensive Transcriptome Annotation of Thousands of HIV-1 Genomes.bioRxiv : the preprint server for biology · 2025Article
- Quantification method affects replicability of eQTL analysis, colocalization, and TWAS.bioRxiv : the preprint server for biology · 2025Article
- An updated comparison of microarray and RNA-seq for concentration response transcriptomic study: case studies with two cannabinoids, cannabichromene and cannabinol.BMC genomics · 2025Article
- Stem-like memory and precursors of exhausted T cells share a common progenitor defined by ID3 expression.Science immunology · 2025Article
- High-quality sika deer omics data and integrative analysis reveal genic and cellular regulation of antler regeneration.Genome research · 2025Article
- Transipedia.org: k-mer-based exploration of large RNA sequencing datasets and application to cancer data.Genome biology · 2024Article
- A real-world multi-center RNA-seq benchmarking study using the Quartet and MAQC reference materials.Nature communications · 2024Article
- Impact of genome build on RNA-seq interpretation and diagnostics.American journal of human genetics · 2024Article
- Impact of genome build on RNA-seq interpretation and diagnostics.medRxiv : the preprint server for health sciences · 2024Article
- Detection of microRNAs expression signatures in vitreous humor of intraocular tuberculosis.Molecular biology reports · 2023Article
- TAGADA: a scalable pipeline to improve genome annotations with RNA-seq data.NAR genomics and bioinformatics · 2023Article
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3 authors.
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Abstract
backgroundRNA sequencing is currently the method of choice for genome-wide profiling of gene expression. A popular approach to quantify expression levels of genes from RNA-seq data is to map reads to a reference genome and then count mapped reads to each gene. Gene annotation data, which include chromosomal coordinates of exons for tens of thousands of genes, are required for this quantification process. There are several major sources of gene annotations that can be used for quantification, such as Ensembl and RefSeq databases. However, there is very little understanding of the effect that the choice of annotation has on the accuracy of gene expression quantification in an RNA-seq analysis.
resultsIn this paper, we present results from our comparison of Ensembl and RefSeq human annotations on their impact on gene expression quantification using a benchmark RNA-seq dataset generated by the SEQC consortium. We show that the use of RefSeq gene annotation models led to better quantification accuracy, based on the correlation with ground truths including expression data from >800 real-time PCR validated genes, known titration ratios of gene expression and microarray expression data. We also found that the recent expansion of the RefSeq annotation has led to a decrease in its annotation accuracy. Finally, we demonstrated that the RNA-seq quantification differences observed between different annotations were not affected by the use of different normalization methods.
conclusionIn conclusion, our study found that the use of the conservative RefSeq gene annotation yields better RNA-seq quantification results than the more comprehensive Ensembl annotation. We also found that, surprisingly, the recent expansion of the RefSeq database, which was primarily driven by the incorporation of sequencing data into the gene annotation process, resulted in a reduction in the accuracy of RNA-seq quantification.
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