IVT-seq reveals extreme bias in RNA sequencing

Abstract

BackgroundRNA-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.ResultsWe present a method, in vitro transcription sequencing (IVT-seq), for identifying and assessing the technical biases in RNA-seq library generation and sequencing at scale. We created a pool of over 1,000 in vitro transcribed RNAs from a full-length human cDNA library and sequenced them with polyA and total RNA-seq, the most common protocols. Because each cDNA is full length, and we show in vitro transcription is incredibly processive, each base in each transcript should be equivalently represented. However, with common RNA-seq applications and platforms, we find 50% of transcripts have more than two-fold and 10% have more than 10-fold differences in within-transcript sequence coverage. We also find greater than 6% of transcripts have regions of dramatically unpredictable sequencing coverage between samples, confounding accurate determination of their expression. We use a combination of experimental and computational approaches to show rRNA depletion is responsible for the most significant variability in coverage, and several sequence determinants also strongly influence representation.ConclusionsThese 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.

Most Highly Expressed Protein-Coding Genes Have a Single Dominant Isoform

“…Recent large-scale comparisons have been critical of the accuracy of much of the transcript reconstruction process. 38,39 It may be that RNAseq deconvolution algorithms have a preferential bias for shorter transcripts. The set of approximately 3000 genes with reliably identified dominant isoforms that we have generated in this experiment would make an ideal gold standard set for validating RNAseq reconstruction algorithms.…”

Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation

“…At the transcript level, sample-specific biases that current methods correct for include the fragment length distribution induced by size selection, positional bias along the transcript due to RNA degradation and mRNA selection techniques, and sequence-based bias in read start positions arising from the differential binding efficiency of random hexamer primers 2,12-16 (Figure 1a and Supplementary Table 1). Even so, it is common to observe extreme variability in the coverage of RNA-seq fragments along transcripts that is purely technical and sample-specific 17 and not explained by current bias models, which confounds current methods designed to identify and quantify transcripts 18 (Figure 1b). …”

“…We used a benchmarking dataset of 1,062 human in vitro transcribed (IVT) and sequenced cDNA clones mixed at various concentrations with mouse total RNA 17 . We focused our analysis on 64 of the IVT transcripts as exhibiting “high unpredictable coverage” 17 .…”

“…We focused our analysis on 64 of the IVT transcripts as exhibiting “high unpredictable coverage” 17 . We compared the predictive power of four bias models implemented in alpine: a read start model, a fragment GC content model, a fragment GC content and GC stretches model, and a model including all of these biases (Supplementary Figure 2).…”

Quantitative bacterial transcriptomics with RNA-seq

“…The RNA was extracted by using the hot-phenol method [7] and DNase I treated to remove contaminating DNA. The RNA samples were not depleted for rRNA prior to sequencing, which tends to eliminate some experimental biases [8]. The RNA samples were shipped on dry ice to vertis Biotechnologie AG (Germany) for library preparation and Illumina HiSeq2000 sequencing, as described by others [7,9].…”