4/1/2023 0 Comments Ck modules poly step seqThe bars at the bottom left of the plot indicate the number of genes identified in each library (as shown in Subfigure A and B). C Upset plot showing the coordinance of genes identified in each library. Standard deviation of the mean at each subsample point was also plotted but is not visible at this scale. Each line indicates the mean of 100 repeated subsamples for each integer percentage of the overall number of reads in that library. B Saturation analysis of the number of unique genes identified in each library. The right plot zooms into the “other” (orange) biotypes from the left plot. A Comparison of the number of unique genes identified (having 1 or more reads mapped) and their biotypes in each library. Unselected libraries capture slightly less complex transcriptomes due to their reduced read depth. The omission of poly(A) selection minimizes poly(A)-tail-derived biases and allows for a substantial reduction of input sample RNA, enhancing the accuracy and expanding the applications of dRNA-seq. We extend our analyses to include samples from another lab to show that poly(A) selection introduces undue noise in published data. Here, by analyzing libraries with and without poly(A) selection, we show that the use of oligo (dT)-based poly(A) selection introduces bias and variability attributable to mRNA poly(A) tail differences. An ideal dRNA-seq protocol would explicitly avoid biases inherent in poly(A) selection. Work from several groups highlights examples where poly(A)-tail lengths and deadenylation rates differ between mRNAs according to their age or gene-of-origin. In other sequencing applications, poly(A) selection artificially enriches for longer tailed RNA species and therefore would be expected to also bias the dRNA-seq technique. With the recent introduction of total RNA as input for ONT’s dRNA-seq protocol, the opportunity to avoid additional handling by omitting poly(A) selection has increased the utility of the technique, and provides the opportunity to directly answer questions regarding the potential biases introduced by the previous standard of oligo (dT)-based selection methods. Among transcriptomic techniques, the Oxford Nanopore Technologies (ONT) direct RNA-sequencing (dRNA-seq) platform stands out by avoiding nearly all of these steps, and a prior study detected less bias in Oxford’s dRNA-seq protocol compared to several others. These biases can arise at a number of steps, including: RNA fragmentation, reverse transcription priming, PCR amplification, and capture on the sequencing platform. Indeed, comparison of several RNA-seq techniques on identical samples demonstrates biases from each protocol. But before RNAs can be sequenced, they must first be captured in a manner amenable to sequencing.īiases inherent in RNA capture protocols can skew the view of the transcriptome (reviewed in ). Several techniques of genome-wide RNA sequencing (RNA-seq) exist to survey the transcriptome, broadly falling into sequencing-by-synthesis (Illumina, 454, PacBio) or sequencing-by-current (Oxford Nanopore). Identification of RNAs in a biological sample is central to diverse research applications including mechanistic studies, diagnostics, and high-dimensional phenotyping. Our work expands the utility of direct RNA-seq by validating the use of total RNA as input, and demonstrates important technical artifacts from poly(A) selection that inconsistently skew mRNA expression and poly(A) tail length measurements. Importantly, we show poly(A) selection is dispensable for Oxford Nanopore’s direct RNA-seq technique, and demonstrate successful library construction without poly(A) selection, with decreased input, and without loss of quality. Interestingly, we identify a population of mRNAs (> 10% of genes’ mRNAs) that are inconsistently captured by poly(A) selection due to highly variable poly(A) tails, and demonstrate this phenomenon in our hands and in published data. As expected, poly(A) selection skews sequenced mRNAs toward longer poly(A) tail lengths. Here, we show that poly(A) selection biases Oxford Nanopore direct RNA sequencing. In many applications it is well-known that poly(A) selection biases the view of the transcriptome by selecting for longer tailed mRNA species. RNA-seq users often first enrich for mRNA, with the most popular enrichment method being poly(A) selection. Genome-wide RNA-sequencing technologies are increasingly critical to a wide variety of diagnostic and research applications.
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