ReorientExpress: reference-free orientation of nanopore cDNA reads with deep learning

Ruiz-Reche, Angel; Srivastava, Akanksha; Indi, Joel A.; Rubia, Ivan de la; Eyras, Eduardo

doi:10.1186/s13059-019-1884-z

Cited by 10 publications

(8 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To achieve the potential of error correction on ONT transcriptomic data, custom algorithms have to be designed. Recent papers have tackled clustering 24 , 25 and orientation problems for these data 26 but there is currently no tool available for error correction of ONT transcriptomic reads.…”

Section: Introductionmentioning

confidence: 99%

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

2021

View full text Add to dashboard Cite

Oxford Nanopore (ONT) is a leading long-read technology which has been revolutionizing transcriptome analysis through its capacity to sequence the majority of transcripts from end-to-end. This has greatly increased our ability to study the diversity of transcription mechanisms such as transcription initiation, termination, and alternative splicing. However, ONT still suffers from high error rates which have thus far limited its scope to reference-based analyses. When a reference is not available or is not a viable option due to reference-bias, error correction is a crucial step towards the reconstruction of the sequenced transcripts and downstream sequence analysis of transcripts. In this paper, we present a novel computational method to error correct ONT cDNA sequencing data, called isONcorrect. IsONcorrect is able to jointly use all isoforms from a gene during error correction, thereby allowing it to correct reads at low sequencing depths. We are able to obtain a median accuracy of 98.9–99.6%, demonstrating the feasibility of applying cost-effective cDNA full transcript length sequencing for reference-free transcriptome analysis.

show abstract

Section: Introductionmentioning

confidence: 99%

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

2021

View full text Add to dashboard Cite

show abstract

“…The similarity value is defined as the number of bases covered by these co-linear common k-mers over the length of the shortest read in the pair. If the orientation for cDNA reads is unknown [28], RATTLE tests both relative orientations for each pair of reads by default, or only the given strand if the -rna option is used. As a result, all reads within a cluster have the same orientation.…”

Section: Rattle Clustering Algorithmmentioning

confidence: 99%

RATTLE: reference-free reconstruction and quantification of transcriptomes from Nanopore sequencing

et al. 2022

Self Cite

View full text Add to dashboard Cite

Nanopore sequencing enables the efficient and unbiased measurement of transcriptomes. Current methods for transcript identification and quantification rely on mapping reads to a reference genome, which precludes the study of species with a partial or missing reference or the identification of disease-specific transcripts not readily identifiable from a reference. We present RATTLE, a tool to perform reference-free reconstruction and quantification of transcripts using only Nanopore reads. Using simulated data and experimental data from isoform spike-ins, human tissues, and cell lines, we show that RATTLE accurately determines transcript sequences and their abundances, and shows good scalability with the number of transcripts.

show abstract

“…Differential isoform expression between cell types and across conditions plays a major role in the diversification of the proteome ( Nilsen and Graveley, 2010 ) and functionality of transcripts in the cell ( Yang et al , 2016 ). Long-read sequencing has become widely used to address this problem ( Au et al , 2013 ; Bolisetty et al , 2015 ; Koren et al , 2012 ; Leung et al , 2021 ; Oikonomopoulos et al , 2016 ; Ruiz-Reche et al , 2019 ; Schulz et al , 2021 ; Sharon et al , 2013 ; Tilgner et al , 2015 ), and with applications to single-cell isoform sequencing studies ( Arzalluz-Luque et al , 2022 ; Gupta et al , 2018 ; Hardwick et al , 2022 ; Joglekar et al , 2021 ; Volden and Vollmers, 2022 ). These approaches have been reviewed in Hardwick et al (2019) .…”

Section: Introductionmentioning

confidence: 99%

ScisorWiz: visualizing differential isoform expression in single-cell long-read data

et al. 2022

View full text Add to dashboard Cite

RNA isoforms contribute to the diverse functionality of the proteins they encode within the cell. Visualizing how isoform expression differs across cell types and brain regions can inform our understanding of disease and gain or loss of functionality caused by alternative splicing with potential negative impacts. However, the extent to which this occurs in specific cell types and brain regions is largely unknown. This is the kind of information that ScisorWiz plots can provide in an informative and easily communicable manner. ScisorWiz affords its user the opportunity to visualize specific genes across any number of cell types, and provides various sorting options for the user to gain different ways to understand their data. ScisorWiz provides a clear picture of differential isoform expression through various clustering methods and highlights features such as alternative exons and single nucleotide variants (SNVs). Tools like ScisorWiz are key for interpreting single-cell isoform sequencing data. This tool applies to any single-cell long-read RNA sequencing data in any cell type, tissue, or species.

show abstract

ReorientExpress: reference-free orientation of nanopore cDNA reads with deep learning

Cited by 10 publications

References 21 publications

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

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

RATTLE: reference-free reconstruction and quantification of transcriptomes from Nanopore sequencing

ScisorWiz: visualizing differential isoform expression in single-cell long-read data

Contact Info

Product

Resources

About