A survey of best practices for RNA-seq data analysis

By Ana Conesa, Pedro Madrigal, Sonia Tarazona, David Gomez-Cabrero, A. Cervera, Andrew W. McPherson, M. Szcześniak, Daniel J. Gaffney, Laura L. Elo, Xuegong Zhang and 1 more. Genome Biology, 2016

2,568 citations recorded by the source corpus.

RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/36e00caa724a4f8865b6f675fa27f7297e51ba41

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Citations in the corpus

Transcriptomics technologies (2017, methodology): “Raw data are examined for high quality scores for base calls, guanine-cytosine content matches the expected distribution, the over representation of particularly short sequence motifs (k-mers), and an unexpectedly high read duplication rate [85].”
Gene co-expression analysis for functional classification and gene–disease predictions (2017, methodology): “Different tools and methods to obtain reliable expression counts from RNA-seq data were recently reviewed in [55], and these will not be reviewed here.”
Gaining comprehensive biological insight into the transcriptome by performing a broad-spectrum RNA-seq analysis (2017, methodology): “Several efforts have tried to compare the performance of different RNA-seq analysis tools 1 – 9 .”
Integrating single-cell transcriptomic data across different conditions, technologies, and species (2018, methodology): “We next compared Seurat’s performance to widely used batch correction tools that have been applied to both bulk 47 and single-cell genomics data 5 .”
Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi (2017, methodology): “In addition to genome assembly and gene annotation, we also use publicly available RNA-seq data of these four nematode species, which have been mapped by Wormbase using STAR (Dobin et al., 2013).”
ggtranscript: an R package for the visualization and interpretation of transcript isoforms using ggplot2 (2022, background): “It is well-recognised that standard transcriptomic assays relying on short-read RNAsequencing are unable to comprehensively capture the full spectrum of alternative splicing.(2) However, long-read sequencing platforms such as PacBio and Oxford Nanopore have transformed the field and enabled the disc”
Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols (2020, background): “The algorithms and challenges associated with each step have been reviewed elsewhere (Garber et al. 2011; Conesa et al. 2016; Zhao et al. 2016).”