Efficient Hybrid De Novo Error Correction and Assembly for Long Reads

Kchouk, Mehdi; Elloumi, Mourad

doi:10.1109/dexa.2016.032

Cited by 5 publications

(3 citation statements)

References 39 publications

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“…We have selected what we believe is a representative set of tools but there also exist other tools that were not considered in this study, e.g. HG-Color [39], HECIL [40], MIRCA [41], Jabba [42], nanocorr [43], and Racon [44].…”

Section: Introductionmentioning

confidence: 99%

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

Lima

Marchet

Caboche

et al. 2019

Briefings in Bioinformatics

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Motivation Nanopore long-read sequencing technology offers promising alternatives to high-throughput short read sequencing, especially in the context of RNA-sequencing. However this technology is currently hindered by high error rates in the output data that affect analyses such as the identification of isoforms, exon boundaries, open reading frames and creation of gene catalogues. Due to the novelty of such data, computational methods are still actively being developed and options for the error correction of Nanopore RNA-sequencing long reads remain limited. Results In this article, we evaluate the extent to which existing long-read DNA error correction methods are capable of correcting cDNA Nanopore reads. We provide an automatic and extensive benchmark tool that not only reports classical error correction metrics but also the effect of correction on gene families, isoform diversity, bias toward the major isoform and splice site detection. We find that long read error correction tools that were originally developed for DNA are also suitable for the correction of Nanopore RNA-sequencing data, especially in terms of increasing base pair accuracy. Yet investigators should be warned that the correction process perturbs gene family sizes and isoform diversity. This work provides guidelines on which (or whether) error correction tools should be used, depending on the application type. Benchmarking software https://gitlab.com/leoisl/LR_EC_analyser

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Section: Introductionmentioning

confidence: 99%

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

Lima

Marchet

Caboche

et al. 2019

Briefings in Bioinformatics

View full text Add to dashboard Cite

show abstract

“…This type of methods have no special requirements for third-party alignment tools, but they are difficult to align short reads to repetitive and noisy regions of the long read due to their small length. To overcome this obstacle, short-read assembly-based methods, such as ECTools 13 , HALC 14 , and MiRCA 15 , assemble short reads into longer contigs in advance. In this way, the assembled contigs can be effectively aligned to repetitive and noisy regions in long reads.…”

Section: Related Workmentioning

confidence: 99%

RNNHC: A hybrid error correction algorithm for long reads based on Recurrent Neural Network

Wang,

Chen

2023

Preprint

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Long reads generated by third-generation sequencing technologies show irreplaceable advantages in downstream analysis by virtue of their ultra-long read lengths. However, their high error rate also affects the accuracy of downstream analysis. Meanwhile, deep learning has shown its talents in the processing of long gene sequence problems. In this work, a hybrid error correction algorithm is proposed, which uses the idea of processing multi-class tasks with a recurrent neural network to capture the long-term dependencies in the long reads to solve the problem of long-read error correction. RNNHC first aligns the long reads to the high-precision short reads to generate the corresponding feature vectors and labels, then adopts a recurrent neural network to capture the dependencies between bases, and finally trains the model so that it can be used later for prediction and error correction. We demonstrate that the performance of RNNHC is better than that of state-of-the-art hybrid error correction methods on real-world PacBio and ONT data sets including E. coli, S. cerevisiae, and Drosophila melanogaster. As shown in our experimental results that RNNHC can improve the alignment identity while maintaining read length and continuity of the sequence, and spending less user time than other hybrid error correction algorithms. Furthermore, RNNHC is applicable to data from two mainstream sequencing platforms.

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“…HALC [9], MiRCA [10] are based on this strategy, the context information of adjacent regions after assembly can make the contigs align to repetitive and noisy regions in long reads effectively. The DBG-based methods directly use the DBG constructed by short-read k-mers to avoid the complex assembly process, and then anchor the long read to the DBG and traverse the graph to obtain an optimal path.…”

Section: Introductionmentioning

confidence: 99%

NMTHC: a hybrid error correction method using a machine translation model based on the sequence-to-sequence framework

Wang

Chen

2023

Preprint

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Background With the development of sequencing technology, the reads produced by third-generation sequencing technologies have ultra-long lengths and cost-effectiveness and have drawn the favor of many application scenes. However, compared with the shorter reads obtained by next-generation sequencing, the high error rates of these long reads affect the accuracy in downstream analysis. Results In this work, a novel hybrid error correction algorithm (NMTHC) based on the sequence-to-sequence framework is proposed, which converts the long-read error correction problem into a machine translation problem in natural language processing. First, the high-precision short reads are aligned to the long reads to generate alignment information, and then the long reads and alignment information are tokenized to generate “long sentences” and “target sentences”. The generated sequences are padded to the same length and then divided into batches for training. Both the encoding and the decoding layer of the model use the bidirectional Long Short-term Memory (Bi-LSTM) network to encode and decode hidden state information, and the generated token and the updated state at the current time step are used to predict the token of the next time step. Conclusions We have tested our algorithm on subsets of long reads, and the results show that NMTHC is not only excellent in the number of aligned bases with the reference genome but also improves the alignment identity without any loss of the read length. In addition, it has been proved that NMTHC can be distributed in multiple GPUs to improve the correction speed. In summary, NMTHC can correct long reads more accurately while keeping read length and continuity. It can be applied to sequencing data from both mainstream platforms and provides a new perspective for hybrid error correction algorithms based on deep learning.

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Efficient Hybrid De Novo Error Correction and Assembly for Long Reads

Cited by 5 publications

References 39 publications

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

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

RNNHC: A hybrid error correction algorithm for long reads based on Recurrent Neural Network

NMTHC: a hybrid error correction method using a machine translation model based on the sequence-to-sequence framework

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