scholarly journals ELECTOR: Evaluator for long reads correction methods

2019 ◽  
Author(s):  
Camille Marchet ◽  
Pierre Morisse ◽  
Lolita Lecompte ◽  
Arnaud Lefebvre ◽  
Thierry Lecroq ◽  
...  
Keyword(s):  
Error Correction ◽  
State Of The Art ◽  
Error Rates ◽  
Sequencing Data ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Wide Range ◽  
Downstream Processes ◽  
Generation Sequencing

AbstractMotivationIn the last few years, the error rates of third generation sequencing data have been capped above 5%, including many insertions and deletions. Thereby, an increasing number of long reads correction methods have been proposed to reduce the noise in these sequences. Whether hybrid or self-correction methods, there exist multiple approaches to correct long reads. As the quality of the error correction has huge impacts on downstream processes, developing methods allowing to evaluate error correction tools with precise and reliable statistics is therefore a crucial need. Since error correction is often a resource bottleneck in long reads pipelines, a key feature of assessment methods is therefore to be efficient, in order to allow the fast comparison of different tools.ResultsWe propose ELECTOR, a reliable and efficient tool to evaluate long reads correction, that enables the evaluation of hybrid and self-correction methods. Our tool provides a complete and relevant set of metrics to assess the read quality improvement after correction and scales to large datasets. ELECTOR is directly compatible with a wide range of state-of-the-art error correction tools, using whether simulated or real long reads. We show that ELECTOR displays a wider range of metrics than the state-of-the-art tool, LRCstats, and additionally importantly decreases the runtime needed for assessment on all the studied datasets.AvailabilityELECTOR is available at https://github.com/kamimrcht/[email protected] or [email protected]

scholarly journals ELECTOR: evaluator for long reads correction methods

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Camille Marchet ◽  
Pierre Morisse ◽  
Lolita Lecompte ◽  
Arnaud Lefebvre ◽  
Thierry Lecroq ◽  
...  
Keyword(s):  
Error Correction ◽  
Error Rates ◽  
Sequencing Data ◽  
Multiple Sequence ◽  
Long Reads ◽  
Wide Range ◽  
Unique Method ◽  
Algorithmic Strategy ◽  
Downstream Processes

Abstract The error rates of third-generation sequencing data have been capped >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 ultra-long reads (>100 k 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.

Quality of Third Generation Sequencing

2020 ◽  
Vol 17 (12) ◽  
pp. 5205-5209
Author(s):  
Ali Elbialy ◽  
M. A. El-Dosuky ◽  
Ibrahim M. El-Henawy
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Gc Content ◽  
Error Rates ◽  
Third Generation ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Generation Sequencing

Third generation sequencing (TGS) relates to long reads but with relatively high error rates. Quality of TGS is a hot topic, dealing with errors. This paper combines and investigates three quality related metrics. They are basecalling accuracy, Phred Quality Scores, and GC content. For basecalling accuracy, a deep neural network is adopted. The measured loss does not exceed 5.42.

scholarly journals Estimating DNA methylation potential energy landscapes from nanopore sequencing data

2021 ◽  
Author(s):  
Jordi Abante ◽  
Sandeep Kambhampati ◽  
Andrew P. Feinberg ◽  
John Goutsias
Keyword(s):  
Dna Methylation ◽  
New Technology ◽  
Third Generation ◽  
Sequencing Data ◽  
Modeling And Analysis ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Wide Range ◽  
Potential Energy Landscapes ◽  
Generation Sequencing

AbstractHigh-throughput third-generation sequencing devices, such as the Oxford Nanopore Technologies (ONT) MinION sequencer, can generate long reads that span thousands of bases. This new technology opens the possibility of considering a wide range of epigenetic modifications and provides the capability of interrogating previously inaccessible regions of the genome, such as highly repetitive regions, as well as of performing comprehensive allele-specific methylation analysis, among other applications. It is well-known, however, that detection of DNA methylation from nanopore data results in a substantially reduced per-read accuracy when comparing to WGBS, due to noise introduced by the sequencer and its underlying chemistry. It is therefore imperative that methods are developed for the reliable modeling and analysis of the DNA methylation landscape using nanopore data. Here we introduce such method that takes into account the presence of noise introduced by the ONT sequencer and, by using simulations, we provide evidence of its potential. The proposed approach establishes a solid foundation for the development of a comprehensive framework for the statistical analysis of DNA methylation, and possibly of other epigenetic marks, using third-generation sequencing.

IsoDetect: Detection of splice isoforms from third generation long reads based on short feature sequences

2020 ◽  
Vol 15 ◽  
Author(s):  
Hongdong Li ◽  
Wenjing Zhang ◽  
Yuwen Luo ◽  
Jianxin Wang
Keyword(s):  
Sequence Similarity ◽  
Detection Methods ◽  
Sequence Information ◽  
Third Generation ◽  
Sequencing Data ◽  
Splice Isoforms ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Feature Sequence ◽  
Generation Sequencing

Aims: Accurately detect isoforms from third generation sequencing data. Background: Transcriptome annotation is the basis for the analysis of gene expression and regulation. The transcriptome annotation of many organisms such as humans is far from incomplete, due partly to the challenge in the identification of isoforms that are produced from the same gene through alternative splicing. Third generation sequencing (TGS) reads provide unprecedented opportunity for detecting isoforms due to their long length that exceeds the length of most isoforms. One limitation of current TGS reads-based isoform detection methods is that they are exclusively based on sequence reads, without incorporating the sequence information of known isoforms. Objective: Develop an efficient method for isoform detection. Method: Based on annotated isoforms, we propose a splice isoform detection method called IsoDetect. First, the sequence at exon-exon junction is extracted from annotated isoforms as the “short feature sequence”, which is used to distinguish different splice isoforms. Second, we aligned these feature sequences to long reads and divided long reads into groups that contain the same set of feature sequences, thereby avoiding the pair-wise comparison among the large number of long reads. Third, clustering and consensus generation are carried out based on sequence similarity. For the long reads that do not contain any short feature sequence, clustering analysis based on sequence similarity is performed to identify isoforms. Result: Tested on two datasets from Calypte Anna and Zebra Finch, IsoDetect showed higher speed and compelling accuracy compared with four existing methods. Conclusion: IsoDetect is a promising method for isoform detection. Other: This paper was accepted by the CBC2019 conference.

scholarly journals A Sequence-Based Novel Approach for Quality Evaluation of Third-Generation Sequencing Reads

Genes ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 44 ◽  
Author(s):  
Wenjing Zhang ◽  
Neng Huang ◽  
Jiantao Zheng ◽  
Xingyu Liao ◽  
Jianxin Wang ◽  
...  
Keyword(s):  
Quality Evaluation ◽  
Training Data ◽  
Third Generation ◽  
Contig Assembly ◽  
High Quality ◽  
Promising Alternative ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Generation Sequencing

The advent of third-generation sequencing (TGS) technologies, such as the Pacific Biosciences (PacBio) and Oxford Nanopore machines, provides new possibilities for contig assembly, scaffolding, and high-performance computing in bioinformatics due to its long reads. However, the high error rate and poor quality of TGS reads provide new challenges for accurate genome assembly and long-read alignment. Efficient processing methods are in need to prioritize high-quality reads for improving the results of error correction and assembly. In this study, we proposed a novel Read Quality Evaluation and Selection Tool (REQUEST) for evaluating the quality of third-generation long reads. REQUEST generates training data of high-quality and low-quality reads which are characterized by their nucleotide combinations. A linear regression model was built to score the quality of reads. The method was tested on three datasets of different species. The results showed that the top-scored reads prioritized by REQUEST achieved higher alignment accuracies. The contig assembly results based on the top-scored reads also outperformed conventional approaches that use all reads. REQUEST is able to distinguish high-quality reads from low-quality ones without using reference genomes, making it a promising alternative sequence-quality evaluation method to alignment-based algorithms.

scholarly journals Versatile Quality Control Methods for Nanopore Sequencing

2019 ◽  
Vol 15 ◽  
pp. 117693431986306
Author(s):  
Davide Bolognini ◽  
Roberto Semeraro ◽  
Alberto Magi
Keyword(s):  
Quality Control ◽  
Control Method ◽  
Nanopore Sequencing ◽  
Control Methods ◽  
Sequencing Data ◽  
Third Generation Sequencing ◽  
Quality Control Method ◽  
Sequencing Platforms ◽  
Generation Sequencing

Third-generation sequencing using nanopores as biosensors has recently emerged as a strategy capable to overcome next-generation sequencing drawbacks and pitfalls. Assessing the quality of the data produced by nanopore sequencing platforms is essential to decide how useful these may be in making biological discoveries. Here, we briefly contextualized NanoR, a quality control method for nanopore sequencing data we developed, in the scenario of preexistent similar tools. We also illustrated 2 quality control pipelines, readily applicable to nanopore sequencing data, respectively, based on NanoR and PyPore, a second quality control method published by our group.

scholarly journals A hybrid correcting method considering heterozygous variations by a comprehensive probabilistic model

BMC Genomics ◽  
2020 ◽  
Vol 21 (S10) ◽  
Author(s):  
Jiaqi Liu ◽  
Jiayin Wang ◽  
Xiao Xiao ◽  
Xin Lai ◽  
Daocheng Dai ◽  
...  
Keyword(s):  
Error Correction ◽  
Correction Method ◽  
Reference Sequence ◽  
Third Generation ◽  
Next Generation ◽  
Sequencing Data ◽  
Sequencing Errors ◽  
The Third ◽  
Third Generation Sequencing ◽  
Generation Sequencing

Abstract Background The emergence of the third generation sequencing technology, featuring longer read lengths, has demonstrated great advancement compared to the next generation sequencing technology and greatly promoted the biological research. However, the third generation sequencing data has a high level of the sequencing error rates, which inevitably affects the downstream analysis. Although the issue of sequencing error has been improving these years, large amounts of data were produced at high sequencing errors, and huge waste will be caused if they are discarded. Thus, the error correction for the third generation sequencing data is especially important. The existing error correction methods have poor performances at heterozygous sites, which are ubiquitous in diploid and polyploidy organisms. Therefore, it is a lack of error correction algorithms for the heterozygous loci, especially at low coverages. Results In this article, we propose a error correction method, named QIHC. QIHC is a hybrid correction method, which needs both the next generation and third generation sequencing data. QIHC greatly enhances the sensitivity of identifying the heterozygous sites from sequencing errors, which leads to a high accuracy on error correction. To achieve this, QIHC established a set of probabilistic models based on Bayesian classifier, to estimate the heterozygosity of a site and makes a judgment by calculating the posterior probabilities. The proposed method is consisted of three modules, which respectively generates a pseudo reference sequence, obtains the read alignments, estimates the heterozygosity the sites and corrects the read harboring them. The last module is the core module of QIHC, which is designed to fit for the calculations of multiple cases at a heterozygous site. The other two modules enable the reads mapping to the pseudo reference sequence which somehow overcomes the inefficiency of multiple mappings that adopt by the existing error correction methods. Conclusions To verify the performance of our method, we selected Canu and Jabba to compare with QIHC in several aspects. As a hybrid correction method, we first conducted a groups of experiments under different coverages of the next-generation sequencing data. QIHC is far ahead of Jabba on accuracy. Meanwhile, we varied the coverages of the third generation sequencing data and compared performances again among Canu, Jabba and QIHC. QIHC outperforms the other two methods on accuracy of both correcting the sequencing errors and identifying the heterozygous sites, especially at low coverage. We carried out a comparison analysis between Canu and QIHC on the different error rates of the third generation sequencing data. QIHC still performs better. Therefore, QIHC is superior to the existing error correction methods when heterozygous sites exist.

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

2019 ◽  
Vol 21 (4) ◽  
pp. 1164-1181 ◽  
Author(s):  
Leandro Lima ◽  
Camille Marchet ◽  
Ségolène Caboche ◽  
Corinne Da Silva ◽  
Benjamin Istace ◽  
...  
Keyword(s):  
Error Correction ◽  
Rna Sequencing ◽  
Gene Families ◽  
Error Rates ◽  
Open Reading Frames ◽  
Sequencing Data ◽  
Isoform Diversity ◽  
Long Reads ◽  
Long Read ◽  
Read Error Correction

Abstract 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

scholarly journals Quantifying the Benefit Offered by Transcript Assembly on Single-Molecule Long Reads

2019 ◽  
Author(s):  
Laura H. Tung ◽  
Mingfu Shao ◽  
Carl Kingsford
Keyword(s):  
Single Molecule ◽  
Error Rates ◽  
Human Transcriptome ◽  
Sequencing Technologies ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Long Read ◽  
Transcript Assembly ◽  
Novel Isoforms ◽  
Generation Sequencing

AbstractThird-generation sequencing technologies benefit transcriptome analysis by generating longer sequencing reads. However, not all single-molecule long reads represent full transcripts due to incomplete cDNA synthesis and the sequencing length limit of the platform. This drives a need for long read transcript assembly. We quantify the benefit that can be achieved by using a transcript assembler on long reads. Adding long-read-specific algorithms, we evolved Scallop to make Scallop-LR, a long-read transcript assembler, to handle the computational challenges arising from long read lengths and high error rates. Analyzing 26 SRA PacBio datasets using Scallop-LR, Iso-Seq Analysis, and StringTie, we quantified the amount by which assembly improved Iso-Seq results. Through combined evaluation methods, we found that Scallop-LR identifies 2100–4000 more (for 18 human datasets) or 1100–2200 more (for eight mouse datasets) known transcripts than Iso-Seq Analysis, which does not do assembly. Further, Scallop-LR finds 2.4–4.4 times more potentially novel isoforms than Iso-Seq Analysis for the human and mouse datasets. StringTie also identifies more transcripts than Iso-Seq Analysis. Adding long-read-specific optimizations in Scallop-LR increases the numbers of predicted known transcripts and potentially novel isoforms for the human transcriptome compared to several recent short-read assemblers (e.g. StringTie). Our findings indicate that transcript assembly by Scallop-LR can reveal a more complete human transcriptome.

scholarly journals CoLoRd: Compressing long reads

2021 ◽  
Author(s):  
Marek Kokot ◽  
Adam Gudys ◽  
Heng Li ◽  
Sebastian Deorowicz
Keyword(s):  
General Purpose ◽  
Third Generation ◽  
Sequencing Data ◽  
The Third ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Order Of Magnitude ◽  
Generation Sequencing

The costs of maintaining exabytes of data produced by sequencing experiments every year has become a major issue in today's genomics. In spite of the increasing popularity of the third generation sequencing, the existing algorithms for compressing long reads exhibit minor advantage over general purpose gzip. We present CoLoRd, an algorithm able to reduce 3rd generation sequencing data by an order of magnitude without affecting the accuracy of downstream analyzes.

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