A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control consortium

By Z. Su, Paweł P. Łabaj, Sheng Li, Jean Thierry-Mieg, Danielle Thierry-Mieg, Wei Shi, Charles Wang, Gary P. Schroth, R. Setterquist, Johnf . Thompson and 151 more. Nature Biotechnology, 2014

938 citations recorded by the source corpus.

We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the US Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed for all examined platforms, including qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0446839238dc22746e99eb746ce11bc7e18f33b1

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Citations in the corpus

RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, methodology): “BitSeq focused on sampling from the posterior distributions of transcript abundances, given the fragment alignments, giving accurate estimates [94] and useful information about posterior uncertainty and posterior correlation, which is used in the DE step [93] .”
Salmon: fast and bias-aware quantification of transcript expression using dual-phase inference (2017, methodology): “SEQC/MAQ-III Consortium et al. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium.”
Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package (2015, background): “The relevance of sufficient replication has also been brought to attention in recent work that evaluated experimental design considerations (20,56,57), a conclusion that was echoed by the SEQC project (58).”
Gaining comprehensive biological insight into the transcriptome by performing a broad-spectrum RNA-seq analysis (2017, methodology): “In terms of speed, STARlong was 68× faster than GMAP (Supplementary Table 8), while IDP took around 170 CPU hours to process each sample.”
Geospatial Resolution of Human and Bacterial Diversity with City-Scale Metagenomics (2015, neutral): “0) (Segata et al., 2012).”
Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses (2015, methodology): “In order to provide a data set where the FCs would follow a predictable dose-response, we designed a mixture experiment (23,24) between two lung adenocarcinoma cell lines (NCI-H1975 and HCC827, both obtained from ATCC).”
A survey of best practices for RNA-seq data analysis (2016, background): “In a recent comparison, BitSeq compared favorably to other transcript-level packages such as Cuffdiff2 [196].”
Normalization of RNA-seq data using factor analysis of control genes or samples (2014, background): “Unusual RLE distributions could reveal suspicious samples (e.g., problematic library preparation) or batch effects.”