ArticlePloS one2014
RNA-Seq gene profiling--a systematic empirical comparison.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 1 of them a synthesis that pooled it.
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Who cites it
52 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Here We Are, But Where Do We Go? A Systematic Review of Crustacean Transcriptomic Studies from 2014-2015.Integrative and comparative biology · 2016Pooled it
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- Analysis of Gene Regulatory Network and Transcription Factors in Different Tissues of theJournal of fungi (Basel, Switzerland) · 2025Article
- A preliminary study of gene expression changes in Koalas Infected with Koala Retrovirus (KoRV) and identification of potential biomarkers for KoRV pathogenesis.BMC veterinary research · 2024Article
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- DOT1L promotes spermatid differentiation by regulating expression of genes required for histone-to-protamine replacement.Development (Cambridge, England) · 2023Article
- Robust and rigorous identification of tissue-specific genes by statistically extending tau score.BioData mining · 2022Article
- The miR-429 suppresses proliferation and migration in glioblastoma cells and induces cell-cycle arrest and apoptosis via modulating several target genes of ERBB signaling pathway.Molecular biology reports · 2022Article
- Systematic comparison and assessment of RNA-seq procedures for gene expression quantitative analysis.Scientific reports · 2020Article
- Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction.Scientific reports · 2020Article
- Factorial study of the RNA-seq computational workflow identifies biases as technical gene signatures.NAR genomics and bioinformatics · 2020Article
- Short paired-end reads trump long single-end reads for expression analysis.BMC bioinformatics · 2020Article
- Evaluation of Seven Different RNA-Seq Alignment Tools Based on Experimental Data from the Model PlantInternational journal of molecular sciences · 2020Article
- RNA sequencing: the teenage years.Nature reviews. Genetics · 2019Review
- Empirical assessment of the impact of sample number and read depth on RNA-Seq analysis workflow performance.BMC bioinformatics · 2018Article
- Common and differential transcriptional responses to different models of traumatic stress exposure in rats.Translational psychiatry · 2018Article
- Review
- Mononuclear cell transcriptome changes associated with dimethyl fumarate in MS.Neurology(R) neuroimmunology & neuroinflammation · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurately quantifying gene expression levels is a key goal of experiments using RNA-sequencing to assay the transcriptome. This typically requires aligning the short reads generated to the genome or transcriptome before quantifying expression of pre-defined sets of genes. Differences in the alignment/quantification tools can have a major effect upon the expression levels found with important consequences for biological interpretation. Here we address two main issues: do different analysis pipelines affect the gene expression levels inferred from RNA-seq data? And, how close are the expression levels inferred to the "true" expression levels? We evaluate fifty gene profiling pipelines in experimental and simulated data sets with different characteristics (e.g, read length and sequencing depth). In the absence of knowledge of the 'ground truth' in real RNAseq data sets, we used simulated data to assess the differences between the "true" expression and those reconstructed by the analysis pipelines. Even though this approach does not take into account all known biases present in RNAseq data, it still allows to estimate the accuracy of the gene expression values inferred by different analysis pipelines. The results show that i) overall there is a high correlation between the expression levels inferred by the best pipelines and the true quantification values; ii) the error in the estimated gene expression values can vary considerably across genes; and iii) a small set of genes have expression estimates with consistently high error (across data sets and methods). Finally, although the mapping software is important, the quantification method makes a greater difference to the results.
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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 OpenQuestion graph.