Evaluation of haplotype-aware long-read error correction with hifieval.
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ABSTRACT: Summary
The PacBio High-Fidelity (HiFi) sequencing technology produces long reads of >99% in accuracy. It has enabled the development of a new generation of de novo sequence assemblers, which all have sequencing error correction (EC) as the first step. As HiFi is a new data type, this critical step has not been evaluated before. Here, we introduced hifieval, a new command-line tool for measuring over- and under-corrections produced by EC algorithms. We assessed the accuracy of the EC components of existing HiFi assemblers on the CHM13 and the HG002 datasets and further investigated the performance of EC methods in challenging regions such as homopolymer regions, centromeric regions, and segmental duplications. Hifieval will help HiFi assemblers to improve EC and assembly quality in the long run.Availability and implementation
The source code is available at https://github.com/magspho/hifieval.
SUBMITTER: Guo Y
PROVIDER: S-EPMC10612404 | biostudies-literature | 2023 Oct
REPOSITORIES: biostudies-literature
Publications
Evaluation of haplotype-aware long-read error correction with hifieval.
Guo Yujie Y Feng Xiaowen X Li Heng H
Bioinformatics (Oxford, England) 20231001 10
<h4>Summary</h4>The PacBio High-Fidelity (HiFi) sequencing technology produces long reads of >99% in accuracy. It has enabled the development of a new generation of de novo sequence assemblers, which all have sequencing error correction (EC) as the first step. As HiFi is a new data type, this critical step has not been evaluated before. Here, we introduced hifieval, a new command-line tool for measuring over- and under-corrections produced by EC algorithms. We assessed the accuracy of the EC com ...[more]
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Project description:The PacBio High-Fidelity (HiFi) sequencing technology produces long reads of >99% in accuracy. It has enabled the development of a new generation of de novo sequence assemblers, which all have sequencing error correction as the first step. As HiFi is a new data type, this critical step has not been evaluated before. Here, we introduced hifieval, a new command-line tool for measuring over- and under-corrections produced by error correction algorithms. We assessed the accuracy of the error correction components of existing HiFi assemblers on the CHM13 and the HG002 datasets and further investigated the performance of error correction methods in challenging regions such as homopolymer regions, centromeric regions, and segmental duplications. Hifieval will help HiFi assemblers to improve error correction and assembly quality in the long run.
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Project description:The single nucleotide polymorphism (SNP) is the most widely studied type of genetic variation. A haplotype is defined as the sequence of alleles at SNP sites on each haploid chromosome. Haplotype information is essential in unravelling the genome-phenotype association. Haplotype assembly is a well-known approach for reconstructing haplotypes, exploiting reads generated by DNA sequencing devices. The Minimum Error Correction (MEC) metric is often used for reconstruction of haplotypes from reads. However, problems with the MEC metric have been reported. Here, we investigate the MEC approach to demonstrate that it may result in incorrectly reconstructed haplotypes for devices that produce error-prone long reads. Specifically, we evaluate this approach for devices developed by Illumina, Pacific BioSciences and Oxford Nanopore Technologies. We show that imprecise haplotypes may be reconstructed with a lower MEC than that of the exact haplotype. The performance of MEC is explored for different coverage levels and error rates of data. Our simulation results reveal that in order to avoid incorrect MEC-based haplotypes, a coverage of 25 is needed for reads generated by Pacific BioSciences RS systems.
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Project description:BackgroundThird-generation single molecule sequencing technologies can sequence long reads, which is advancing the frontiers of genomics research. However, their high error rates prohibit accurate and efficient downstream analysis. This difficulty has motivated the development of many long read error correction tools, which tackle this problem through sampling redundancy and/or leveraging accurate short reads of the same biological samples. Existing studies to asses these tools use simulated data sets, and are not sufficiently comprehensive in the range of software covered or diversity of evaluation measures used.ResultsIn this paper, we present a categorization and review of long read error correction methods, and provide a comprehensive evaluation of the corresponding long read error correction tools. Leveraging recent real sequencing data, we establish benchmark data sets and set up evaluation criteria for a comparative assessment which includes quality of error correction as well as run-time and memory usage. We study how trimming and long read sequencing depth affect error correction in terms of length distribution and genome coverage post-correction, and the impact of error correction performance on an important application of long reads, genome assembly. We provide guidelines for practitioners for choosing among the available error correction tools and identify directions for future research.ConclusionsDespite the high error rate of long reads, the state-of-the-art correction tools can achieve high correction quality. When short reads are available, the best hybrid methods outperform non-hybrid methods in terms of correction quality and computing resource usage. When choosing tools for use, practitioners are suggested to be careful with a few correction tools that discard reads, and check the effect of error correction tools on downstream analysis. Our evaluation code is available as open-source at https://github.com/haowenz/LRECE .
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Project description:MotivationHigh-throughput sequencing has transformed the study of gene expression levels through RNA-seq, a technique that is now routinely used by various fields, such as genetic research or diagnostics. The advent of third generation sequencing technologies providing significantly longer reads opens up new possibilities. However, the high error rates common to these technologies set new bioinformatics challenges for the gapped alignment of reads to their genomic origin. In this study, we have explored how currently available RNA-seq splice-aware alignment tools cope with increased read lengths and error rates. All tested tools were initially developed for short NGS reads, but some have claimed support for long Pacific Biosciences (PacBio) or even Oxford Nanopore Technologies (ONT) MinION reads.ResultsThe tools were tested on synthetic and real datasets from two technologies (PacBio and ONT MinION). Alignment quality and resource usage were compared across different aligners. The effect of error correction of long reads was explored, both using self-correction and correction with an external short reads dataset. A tool was developed for evaluating RNA-seq alignment results. This tool can be used to compare the alignment of simulated reads to their genomic origin, or to compare the alignment of real reads to a set of annotated transcripts. Our tests show that while some RNA-seq aligners were unable to cope with long error-prone reads, others produced overall good results. We further show that alignment accuracy can be improved using error-corrected reads.Availability and implementationhttps://github.com/kkrizanovic/RNAseqEval, https://figshare.com/projects/RNAseq_benchmark/24391.Contactmile.sikic@fer.hr.Supplementary informationSupplementary data are available at Bioinformatics online.
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