Error correction enables use of Oxford Nanopore technology for reference-free transcriptome analysis

Sahlin, Kristoffer; Sipos, Botond; James, Phillip; Turner, Daniel J.; Medvedev, Paul

doi:10.1101/2020.01.07.897512

Cited by 6 publications

(7 citation statements)

References 50 publications

(76 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These limitations notwithstanding, as long-read sequencing technologies continue to improve, both native RNA and single-cell ONT strategies are likely to become increasingly accurate, informative and practical, providing unprecedented insight into transcriptome complexity and cell-to-cell heterogeneity ( Lebrigand et al, 2020 ). In fact, recent efforts to computationally correct sequencing errors in ONT data are capable of reducing the error rate from 14% ( Workman et al, 2019 ) to about 1% ( Sahlin et al, 2020 ), such that it should be possible for future studies to use ONT sequencing for reference-free de novo transcriptome analysis.…”

Section: Discussionmentioning

confidence: 99%

Large-Scale Multiplexing Permits Full-Length Transcriptome Annotation of 32 Bovine Tissues From a Single Nanopore Flow Cell

et al. 2021

View full text Add to dashboard Cite

A comprehensive annotation of transcript isoforms in domesticated species is lacking. Especially considering that transcriptome complexity and splicing patterns are not well-conserved between species, this presents a substantial obstacle to genomic selection programs that seek to improve production, disease resistance, and reproduction. Recent advances in long-read sequencing technology have made it possible to directly extrapolate the structure of full-length transcripts without the need for transcript reconstruction. In this study, we demonstrate the power of long-read sequencing for transcriptome annotation by coupling Oxford Nanopore Technology (ONT) with large-scale multiplexing of 93 samples, comprising 32 tissues collected from adult male and female Hereford cattle. More than 30 million uniquely mapping full-length reads were obtained from a single ONT flow cell, and used to identify and characterize the expression dynamics of 99,044 transcript isoforms at 31,824 loci. Of these predicted transcripts, 21% exactly matched a reference transcript, and 61% were novel isoforms of reference genes, substantially increasing the ratio of transcript variants per gene, and suggesting that the complexity of the bovine transcriptome is comparable to that in humans. Over 7,000 transcript isoforms were extremely tissue-specific, and 61% of these were attributed to testis, which exhibited the most complex transcriptome of all interrogated tissues. Despite profiling over 30 tissues, transcription was only detected at about 60% of reference loci. Consequently, additional studies will be necessary to continue characterizing the bovine transcriptome in additional cell types, developmental stages, and physiological conditions. However, by here demonstrating the power of ONT sequencing coupled with large-scale multiplexing, the task of exhaustively annotating the bovine transcriptome – or any mammalian transcriptome – appears significantly more feasible.

show abstract

Section: Discussionmentioning

confidence: 99%

Large-Scale Multiplexing Permits Full-Length Transcriptome Annotation of 32 Bovine Tissues From a Single Nanopore Flow Cell

et al. 2021

View full text Add to dashboard Cite

show abstract

“…To evaluate the efficiency of these algorithms, we made use of two publicly available and one in-house SR and LR matched real datasets. The first dataset comes from a large GridION MinION cDNA sequencing experiment of SIRV E0 Spike-Ins (Sahlin et al, 2020). The second one is provided by the Nanopore consortium (Workman et al, 2019) and was generated on the GM12878 B-Lymphocyte cell line.…”

Section: Benchmarked Datasetsmentioning

confidence: 99%

TALC: Transcript-level Aware Long-read Correction

et al. 2020

View full text Add to dashboard Cite

Motivation Long-read sequencing technologies are invaluable for determining complex RNA transcript architectures but are error-prone. Numerous “hybrid correction” algorithms have been developed for genomic data that correct long reads by exploiting the accuracy and depth of short reads sequenced from the same sample. These algorithms are not suited for correcting more complex transcriptome sequencing data. Results We have created a novel reference-free algorithm called TALC (Transcript-level Aware Long Read Correction) which models changes in RNA expression and isoform representation in a weighted De-Bruijn graph to correct long reads from transcriptome studies. We show that transcript-level aware correction by TALC improves the accuracy of the whole spectrum of downstream RNA-seq applications and is thus necessary for transcriptome analyses that use long read technology. Availability TALC is implemented in C ++ and available at https://github.com/lbroseus/TALC. Supplementary information Supplementary data are available at Bioinformatics online.

show abstract

“…We used the subset of 59 isoforms with distinct splice site positions from the ONT SIRV dataset (Sahlin et al 2020) to investigate alignment performance around splice sites (for details see Suppl. Note C).…”

Section: Splice Site Annotation Performance On Sirvmentioning

confidence: 99%

Accurate spliced alignment of long RNA sequencing reads

Sahlin

Mäkinen

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Long-read RNA sequencing techniques are quickly establishing themselves as the primary sequencing technique to study the transcriptome landscape. Many such analyses are dependent upon splice alignment of reads to the genome. However, the error rate and sequencing length of long-read technologies create new challenges for accurately aligning these reads. We present an alignment method uLTRA that, on simulated and synthetic data, shows higher accuracy over state-of-the-art with substantially higher accuracy for small exons. We show several examples on biological data where uLTRA aligns to known and novel isoforms with exon structures that are not detected with other aligners. uLTRA is available at https://github.com/ksahlin/ultra.

show abstract

Error correction enables use of Oxford Nanopore technology for reference-free transcriptome analysis

Cited by 6 publications

References 50 publications

Large-Scale Multiplexing Permits Full-Length Transcriptome Annotation of 32 Bovine Tissues From a Single Nanopore Flow Cell

Large-Scale Multiplexing Permits Full-Length Transcriptome Annotation of 32 Bovine Tissues From a Single Nanopore Flow Cell

TALC: Transcript-level Aware Long-read Correction

Accurate spliced alignment of long RNA sequencing reads

Contact Info

Product

Resources

About