ArticleACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine2015
The impact of RNA-seq aligners on gene expression estimation.
Article in ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A real-world multi-center RNA-seq benchmarking study using the Quartet and MAQC reference materials.Nature communications · 2024Article
- Ambiguous genes due to aligners and their impact on RNA-seq data analysis.Scientific reports · 2023Article
- NASA GeneLab RNA-seq consensus pipeline: standardized processing of short-read RNA-seq data.iScience · 2021Article
- Systematic comparison and assessment of RNA-seq procedures for gene expression quantitative analysis.Scientific reports · 2020Article
- Evaluation of STAR and Kallisto on Single Cell RNA-Seq Data Alignment.G3 (Bethesda, Md.) · 2020Article
- Genome-Wide Transcriptional Analysis Reveals Alternative Splicing Event Profiles in Hepatocellular Carcinoma and Their Prognostic Significance.Frontiers in genetics · 2020Article
- RNA sequencing: the teenage years.Nature reviews. Genetics · 2019Review
- Profiles of alternative splicing in colorectal cancer and their clinical significance: A study based on large-scale sequencing data.EBioMedicine · 2018Article
- Empirical assessment of analysis workflows for differential expression analysis of human samples using RNA-Seq.BMC bioinformatics · 2017Article
- Evaluating the impact of sequencing error correction for RNA-seq data with ERCC RNA spike-in controls.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2016Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
While numerous RNA-seq data analysis pipelines are available, research has shown that the choice of pipeline influences the results of differentially expressed gene detection and gene expression estimation. Gene expression estimation is a key step in RNA-seq data analysis, since the accuracy of gene expression estimates profoundly affects the subsequent analysis. Generally, gene expression estimation involves sequence alignment and quantification, and accurate gene expression estimation requires accurate alignment. However, the impact of aligners on gene expression estimation remains unclear. We address this need by constructing nine pipelines consisting of nine spliced aligners and one quantifier. We then use simulated data to investigate the impact of aligners on gene expression estimation. To evaluate alignment, we introduce three alignment performance metrics, (1) the percentage of reads aligned, (2) the percentage of reads aligned with zero mismatch (ZeroMismatchPercentage), and (3) the percentage of reads aligned with at most one mismatch (ZeroOneMismatchPercentage). We then evaluate the impact of alignment performance on gene expression estimation using three metrics, (1) gene detection accuracy, (2) the number of genes falsely quantified (FalseExpNum), and (3) the number of genes with falsely estimated fold changes (FalseFcNum). We found that among various pipelines, FalseExpNum and FalseFcNum are correlated. Moreover, FalseExpNum is linearly correlated with the percentage of reads aligned and ZeroMismatchPercentage, and FalseFcNum is linearly correlated with ZeroMismatchPercentage. Because of this correlation, the percentage of reads aligned and ZeroMismatchPercentage may be used to assess the performance of gene expression estimation for all RNA-seq datasets.
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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.