ArticleGenome biology2020
Alignment and mapping methodology influence transcript abundance estimation.
Article in Genome biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers, 1 of them a synthesis that pooled it.
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Who cites it
102 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- The impact of transcriptome assembly algorithms on downstream quantification in RNA-seq data analysis.Briefings in bioinformatics · 2026Article
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- Single-Cell Transcriptomic Analysis Reveals Multicellular Coordination and Signaling Rewiring During Fetal Goat Skeletal Muscle Development.Animals : an open access journal from MDPI · 2026Article
- Hookworm genes encoding intestinal excreted-secreted proteins are transcriptionally upregulated in response to the host's immune system.PLoS neglected tropical diseases · 2026Article
- Exploring Pinus nigra's induced defense arsenal against Diplodia sapinea through gene and metabolic pathway analysis.BMC genomics · 2026Article
- Utilisation of Machine Learning Approaches Improves RNA-Seq Transcriptome Analyses in Alzheimer's Disease Brain.Journal of molecular neuroscience : MN · 2026Article
- Blood transcriptomic immune signatures in herpes zoster and postherpetic neuralgia: parallel cohort analyses of IFN-related pathway patterns.Frontiers in cellular and infection microbiology · 2026Article
- Integrated physiological, transcriptomic and metabolomic analysis reveals differential cold response in wheat seedlings across varieties.Frontiers in plant science · 2026Article
42 more citing papers are in PubMed but not listed here.
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9 authors.
Funding
Abstract
backgroundThe accuracy of transcript quantification using RNA-seq data depends on many factors, such as the choice of alignment or mapping method and the quantification model being adopted. While the choice of quantification model has been shown to be important, considerably less attention has been given to comparing the effect of various read alignment approaches on quantification accuracy.
resultsWe investigate the influence of mapping and alignment on the accuracy of transcript quantification in both simulated and experimental data, as well as the effect on subsequent differential expression analysis. We observe that, even when the quantification model itself is held fixed, the effect of choosing a different alignment methodology, or aligning reads using different parameters, on quantification estimates can sometimes be large and can affect downstream differential expression analyses as well. These effects can go unnoticed when assessment is focused too heavily on simulated data, where the alignment task is often simpler than in experimentally acquired samples. We also introduce a new alignment methodology, called selective alignment, to overcome the shortcomings of lightweight approaches without incurring the computational cost of traditional alignment.
conclusionWe observe that, on experimental datasets, the performance of lightweight mapping and alignment-based approaches varies significantly, and highlight some of the underlying factors. We show this variation both in terms of quantification and downstream differential expression analysis. In all comparisons, we also show the improved performance of our proposed selective alignment method and suggest best practices for performing RNA-seq quantification.
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