Publications 2018-02-11T16:57:55+00:00

Publications

Publication Title: Lahens N., et al. IVT-seq reveals extreme bias in RNA sequencing Genome Biology 15(6):R86 (2014). doi:10.1186/gb-2014-15-6-r86 OPEN ACCESS

RNA-Seq is a powerful technique for identifying and quantifying transcription and splicing events, both known and novel. However, given its recent development and the proliferation of library construction methods, understanding the bias it introduces is incomplete but critical to realizing its value

These results show the utility of IVT-seq for promoting better understanding of bias introduced by RNA-Seq. We find rRNA depletion is responsible for substantial, unappreciated biases in coverage introduced during library preparation. These biases suggest exon-level expression analysis may be inadvisable, and we recommend caution when interpreting RNA-Seq results.

To assess the technical variability in RNA sequencing technologies, Lahens et al. utilized an in vitro transcription sequencing (IVT-seq) method. By using in vitro transcribed (IVT) RNAs of known identity and sequence, technical biases were identified during RNA-Seq library preparation and sequencing.
Three fundamental observations were noted:
Due to significant biases introduced in library preparation, interpreting exon-level RNA-Seq results, especially when looking for alternative splicing events, should be done with caution.
Biases contain a high degree of variability and are introduced at all phases of the RNA-Seq library preparation and sequencing processes.
The method used to determine bias in RNA-Seq is not specific to Illumina sequencing and applies to biases from other sequencing preparations and platforms.

IVT-seq, a tool for exploring RNA-Seq variability, has revealed the substantial, variable, and highly reproducible biases introduced during all stages of library preparation and sequencing. Although much progress has been made in producing and analyzing RNA-Seq results, this paper demonstrates that caution is wise when interpreting that data.

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