Improving RNA-Seq expression estimates by correcting for fragment bias

By Adam Roberts, Cole Trapnell, Julie Donaghey, Johannes Rinn, Lior S. Pachter. Genome Biology, 2011

1,616 citations recorded by the source corpus.

The biochemistry of RNA-Seq library preparation results in cDNA fragments that are not uniformly distributed within the transcripts they represent. This non-uniformity must be accounted for when estimating expression levels, and we show how to perform the needed corrections using a likelihood based approach. We find improvements in expression estimates as measured by correlation with independently performed qRT-PCR and show that correction of bias leads to improved replicability of results across libraries and sequencing technologies.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5f51e76c41509ff687e52b9fff1ef641eb2bbd72

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Citations in the corpus

Organoid cultures derived from patients with advanced prostate cancer (2014, neutral): “1) (Roberts et al., 2011).”
Developmental regulation of human cortex transcription and its clinical relevance at base resolution (2014, neutral): “RNA-seq data processing and analysis involves a number of well-documented technical biases 53–56 , but we found little evidence for the significant DERs originating from technical or computational artifacts.”
An RNA-Sequencing Transcriptome and Splicing Database of Glia, Neurons, and Vascular Cells of the Cerebral Cortex (2014, methodology): “Cufflinks implements a likelihood-based bias correction step that considers fragment size and GC content bias, among others, and has been shown to significantly improve expression estimation (Roberts et al., 2011).”
TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions (2013, background): “Assuming that sequencing reads are uniformly distributed along a transcript [3], we would expect 33 to 38% of 100 bp reads from an RNAseq experiment to span two or more exons.”
Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks (2012, methodology): “CummeRbund drastically simplifies common data exploration tasks, such as plotting and cluster analysis of expression data ( Fig.”