De Novo Clustering of Long-Read Transcriptome Data Using a Greedy, Quality-Value Based Algorithm

Sahlin, Kristoffer; Medvedev, Paul

doi:10.1007/978-3-030-17083-7_14

Cited by 16 publications

(18 citation statements)

References 44 publications

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“…Optimizing the normalization protocol would be the best strategy for sequencing of a transcriptome where we expect transcripts are expressed at hugely variable levels. Specific bioinformatics tools including TAPIS pipeline (Abdel-Ghany et al, 2016), de novo AS (Liu et al, 2017), IsoCon (Sahlin et al, 2018), and isONclust (Sahlin and Medvedev, 2019) could be used to process and cluster read data prior further functional annotation at transcript isoform level.…”

Section: Discussionmentioning

confidence: 99%

The Impact of cDNA Normalization on Long-Read Sequencing of a Complex Transcriptome

et al. 2019

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Normalization of cDNA is widely used to improve the coverage of rare transcripts in analysis of transcriptomes employing next-generation sequencing. Recently, long-read technology has been emerging as a powerful tool for sequencing and construction of transcriptomes, especially for complex genomes containing highly similar transcripts and transcript-spliced isoforms. Here, we analyzed the transcriptome of sugarcane, a highly polyploidy plant genome, by PacBio isoform sequencing (Iso-Seq) of two different cDNA library preparations, with and without a normalization step. The results demonstrated that, while the two libraries included many of the same transcripts, many longer transcripts were removed, and many new generally shorter transcripts were detected by normalization. For the same input cDNA and data yield, the normalized library recovered more total transcript isoforms and number of predicted gene families and orthologous groups, resulting in a higher representation for the sugarcane transcriptome, compared to the non-normalized library. The non-normalized library, on the other hand, included a wider transcript length range with more longer transcripts above ∼1.25 kb and more transcript isoforms per gene family and gene ontology terms per transcript. A large proportion of the unique transcripts comprising ∼52% of the normalized library were expressed at a lower level than the unique transcripts from the non-normalized library, across three tissue types tested including leaf, stalk, and root. About 83% of the total 5,348 predicted long noncoding transcripts was derived from the normalized library, of which ∼80% was derived from the lowly expressed fraction. Functional annotation of the unique transcripts suggested that each library enriched different functional transcript fractions. This demonstrated the complementation of the two approaches in obtaining a complete transcriptome of a complex genome at the sequencing depth used in this study.

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Section: Discussionmentioning

confidence: 99%

The Impact of cDNA Normalization on Long-Read Sequencing of a Complex Transcriptome

et al. 2019

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“…The input to our algorithm is a cluster of reads originating from transcripts of a single gene family. Such clusters can be generated from a whole-transcriptome dataset by using our previously published tool isONclust (Sahlin and Medvedev 2019). Each cluster is then processed individually and in parallel with isONcorrect, with the goal of correcting all the sequencing errors.…”

Section: Resultsmentioning

confidence: 99%

“…This strategy is in sharp contrast to approaches which cluster based on the isoform of origin. Such clustering results in low read coverage per transcript (Sahlin and Medvedev 2019), particularly for genes expressing multiple isoforms with variable start and stop sites and makes error correction unable to utilize full coverage over shared exons. By using isONclust to cluster at the gene family level, each read retains more complete exon coverage and helps the correction process preserve allele- or copy-specific small variant differences between transcripts that otherwise share the same structure.…”

Section: Discussionmentioning

confidence: 99%

“…To achieve the potential of error correction on ONT transcriptomic data, custom algorithms have to be designed. Recent papers have tackled clustering (Sahlin and Medvedev 2019; Marchet et al 2019) and orientation problems for this data (Ruiz-Reche et al 2019) but there is currently no tool available for error correction of ONT transcriptomic reads.…”

Section: Introductionmentioning

confidence: 99%

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

Sahlin

Sipos

James

et al. 2020

Preprint

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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 it 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 an accuracy of 98.7-99.5%, demonstrating the feasibility of applying cost-effective cDNA full transcript length sequencing for reference-free transcriptome analysis.

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“…isONclust is one of the recent clustering tools that are designed to cluster PacBio and Oxford nanopore data efficiently. Clustering in isONclust is based on shared -mers and a minimizer scheme [20]. Although MMseqs2/Linclust is not designed to work for isoforms, we included this tool as the results in [22] showed that it is faster than CD-HIT.…”

Section: Introductionmentioning

confidence: 99%

MinIsoClust

Behera

Deogun

Moriyama

2020

Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

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De Novo Clustering of Long-Read Transcriptome Data Using a Greedy, Quality-Value Based Algorithm

Cited by 16 publications

References 44 publications

The Impact of cDNA Normalization on Long-Read Sequencing of a Complex Transcriptome

The Impact of cDNA Normalization on Long-Read Sequencing of a Complex Transcriptome

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

MinIsoClust

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