Comparative assessment of long-read error correction software applied to Nanopore RNA-sequencing data

Lima, Leandro; Marchet, Camille; Caboche, Ségolène; Silva, Corinne Da; Istace, Benjamin; Aury, Jean‐Marc; Touzet, Hélène; Chikhi, Rayan

doi:10.1093/bib/bbz058

Cited by 35 publications

(45 citation statements)

References 63 publications

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“…Consistent with conclusions from (42), LoRDEC demonstrates very good results on most the transcriptomespecific indicators. Its main drawback is a lower base accuracy due to the inclusion of many sequencing errors from the SRs into the LRs, especially across high SR coverage regions.…”

Section: Discussionsupporting

confidence: 83%

“…Though it was well suited for RNA-seq data, according to (42), we could not include NaS (43) as it depends on a third-party proprietary software (Newbler assembler) that is no longer available.…”

Section: )mentioning

confidence: 99%

“…We wished to use the recently published evaluation tool, LC_EC_analyser (42). Unfortunately, it does not scale to our real size datasets (seemingly because of AlignQC issues in memory management).…”

Section: Measures Of Evaluationmentioning

confidence: 99%

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TALC: Transcript-level Aware Long Read Correction

Broseus

Thomas

Oldfield

et al. 2020

Preprint

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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. We have created a novel algorithm called TALC (Transcription 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 tested TALC on a dataset of short and long reads generated for this study. TALC correction results in more accurate reads with less structural errors than existing methods. TALC is implemented in C++ and available at https://gitlab.igh.cnrs.fr/lbroseus/TALC. INTRODUCTIONRecent advances in RNA sequencing (RNA-seq) technologies have revealed that transcription is more pervasive(1), more diverse (2) and more cryptic (3) than expected. Given the major role that RNA processing plays in disease and normal biology, it is crucial to ascertain the existence of novel isoforms and to accurately quantify their abundance. Second generation RNA-seq technologies such as Illumina are well suited to the tasks of assessing gene expression levels and determining proximal exon connectivity. They produce numerous sequencing reads at a low cost ensuring sufficient representation of most transcripts. However, because the RNA or cDNA is fragmented during short RNA-seq protocols, long range connectivity can only be computationally inferred. These predictions based on short reads struggle to correctly identify transcript isoforms that contain multiple alternative exons (4) or that contain retained introns (5). In these cases, long-read (LR) sequencing technologies are invaluable because they can sequence entire molecules in one pass and thus capture long-range connectivity of complex isoforms. LR technologies however produce less reads than short read (SR) sequencing approaches (6) for similar costs and have higher error rates. In many cases these higher error rates can prevent the correct identification of isoforms.Although several alignment software (7-9) are optimized to handle long erroneous sequences, they still display several issues which confound transcript identification and annotation. Many reads fail to be aligned and regions where the sequencing error rates are higher such as UTRs frequently produce ambiguous alignments. Secondly, they struggle to identify splice junctions, notably those flanking small structural variants such as small exons or alternative 5' and 3' splice sites. This impacts the evaluation of exon skipping events and worsens the quality of . CC-BY-NC-ND 4.0

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Section: Discussionsupporting

confidence: 83%

Section: )mentioning

confidence: 99%

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TALC: Transcript-level Aware Long Read Correction

Broseus

Thomas

Oldfield

et al. 2020

Preprint

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“…A future application is the evaluation of correction methods directly targeted at RNA long reads sequencing. As shown in a recent study (24), RNA long reads have specific requirements that are not met by current methods, which calls for new correctors in the future. ELECTOR could be coupled with a reference transcriptome or a RNA long read simulator, although, currently, such a simulation software does not exist to our knowledge.…”

Section: Discussionmentioning

confidence: 99%

ELECTOR: Evaluator for long reads correction methods

Marchet

Morisse

Lecompte

et al. 2019

Preprint

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The error rates of third-generation sequencing data have been capped above 5%, mainly containing insertions and deletions. Thereby, an increasing number of diverse long reads correction methods have been proposed. The quality of the correction has huge impacts on downstream processes. Therefore, developing methods allowing to evaluate error correction tools with precise and reliable statistics is a crucial need. These evaluation methods rely on costly alignments to evaluate the quality of the corrected reads. Thus, key features must allow the fast comparison of different tools, and scale to the increasing length of the long reads. Our tool, ELECTOR, evaluates long reads correction and is directly compatible with a wide range of error correction tools. As it is based on multiple sequence alignment, we introduce a new algorithmic strategy for alignment segmentation, which enables us to scale to large instances using reasonable resources. To our knowledge, we provide the unique method that allows producing reproducible correction benchmarks on the latest ultralong reads (longer than 100k bases). It is also faster than the current state-of-the-art on other datasets and provides a wider set of metrics to assess the read quality improvement after correction. ELECTOR is available on GitHub (https://github.com/kamimrcht/ELECTOR) and Bioconda.

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“…177,236 bp) totalling 5,8 Gbp representing 10.2x coverage. The error rate of Nanopore sequencing was evaluated by matching all reads against the coding regions of the turbot genome and 90.4 % homology was observed on average, thus within the lowest error rate range for this technology (Lima et al, 2019).…”

Section: Reassembling the Major Sd Region Through Hybrid Long-/short-mentioning

confidence: 99%

Multiple evidences suggest sox2 as the main driver of a young and complex sex determining ZW/ZZ system in turbot (Scophthalmus maximus)

Martı́nez

Robledo

Taboada

et al. 2019

Preprint

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A major challenge in evolutionary biology is to find an explanation for the variation in sex-determining (SD) systems across taxa and to understand the mechanisms driving sex chromosome differentiation. We studied the turbot, holding a ZW/ZZ SD system and no sex chromosome heteromorphism, by combining classical genetics and genomics approaches to disentangle the genetic architecture of this trait. RAD-Seq was used to genotype 18,214 SNPs on 1,135 fish from 36 families and a genome wide association study (GWAS) identified a ~ 6 Mb region on LG5 associated with sex (P < 0.05). The most significant associated markers were located close to sox2, dnajc19 and fxr1 genes. A segregation analysis enabled narrowing down the associated region and evidenced recombination suppression in a region overlapping the candidate genes. A Nanopore/Illumina assembly of the SD region using ZZ and WW individuals identified a single SNP fully associated with Z and W chromosomes. RNA-seq from 5-90 day-old fish detected the expression along the gonad differentiation period of a short non-coding splicing variant (ncRNA) included in a vertebrate-conserved long non-coding RNA overlapping sox2. qPCR showed that sox2 was the only differentially expressed gene between males and females at 50-55 days post fertilization, just prior the beginning of gonad differentiation. More refined information on the involvement of secondary genetic and environmental factors and their interactions on SD was gathered after the analysis of a broad sample of families. Our results confirm the complex nature of SD in turbot and support sox2 as its main driver.

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Comparative assessment of long-read error correction software applied to Nanopore RNA-sequencing data

Cited by 35 publications

References 63 publications

TALC: Transcript-level Aware Long Read Correction

TALC: Transcript-level Aware Long Read Correction

ELECTOR: Evaluator for long reads correction methods

Multiple evidences suggest sox2 as the main driver of a young and complex sex determining ZW/ZZ system in turbot (Scophthalmus maximus)

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