ArticleGigaScience2019
Benchmark of long non-coding RNA quantification for RNA sequencing of cancer samples.
Article in GigaScience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- StCoExpNet: a global co-expression network analysis facilitates identifying genes underlying agronomic traits in potatoes.Plant cell reports · 2024Article
- Pattern discovery of long non-coding RNAs associated with the herbal treatments in breast and prostate cancers.Quantitative biology (Beijing, China) · 2023Article
- Noncoding RNA circuitry in melanoma onset, plasticity, and therapeutic response.Pharmacology & therapeutics · 2023Review
- EpiMix is an integrative tool for epigenomic subtyping using DNA methylation.Cell reports methods · 2023Article
- Integrative Pan-Cancer Genomic and Transcriptomic Analyses of Refractory Metastatic Cancer.Cancer discovery · 2023Article
- Identification of long non-coding RNA-mRNA interactions and genome-wide lncRNA annotation in animal transcriptome profiling.Journal of animal science and technology · 2023Review
- EpiMix: an integrative tool for epigenomic subtyping using DNA methylation.bioRxiv : the preprint server for biology · 2023Article
- NSD1 mutations deregulate transcription and DNA methylation of bivalent developmental genes in Sotos syndrome.Human molecular genetics · 2022Article
- Benchmarking UMI-based single-cell RNA-seq preprocessing workflows.Genome biology · 2021Article
- Comparative transcriptomic analysis of THP-1-derived macrophages infected with Mycobacterium tuberculosis H37Rv, H37Ra and BCG.Journal of cellular and molecular medicine · 2021Article
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4 authors.
Funding
Abstract
backgroundLong non-coding RNAs (lncRNAs) are emerging as important regulators of various biological processes. While many studies have exploited public resources such as RNA sequencing (RNA-Seq) data in The Cancer Genome Atlas to study lncRNAs in cancer, it is crucial to choose the optimal method for accurate expression quantification.
resultsIn this study, we compared the performance of pseudoalignment methods Kallisto and Salmon, alignment-based transcript quantification method RSEM, and alignment-based gene quantification methods HTSeq and featureCounts, in combination with read aligners STAR, Subread, and HISAT2, in lncRNA quantification, by applying them to both un-stranded and stranded RNA-Seq datasets. Full transcriptome annotation, including protein-coding and non-coding RNAs, greatly improves the specificity of lncRNA expression quantification. Pseudoalignment methods and RSEM outperform HTSeq and featureCounts for lncRNA quantification at both sample- and gene-level comparison, regardless of RNA-Seq protocol type, choice of aligners, and transcriptome annotation. Pseudoalignment methods and RSEM detect more lncRNAs and correlate highly with simulated ground truth. On the contrary, HTSeq and featureCounts often underestimate lncRNA expression. Antisense lncRNAs are poorly quantified by alignment-based gene quantification methods, which can be improved using stranded protocols and pseudoalignment methods.
conclusionsConsidering the consistency with ground truth and computational resources, pseudoalignment methods Kallisto or Salmon in combination with full transcriptome annotation is our recommended strategy for RNA-Seq analysis for lncRNAs.
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