Versatile genome assembly evaluation with QUAST-LG

Mikheenko, Alla; Prjibelski, Andrey D.; Saveliev, Vladislav; Antipov, Dmitry; Gurevich, Alexey

doi:10.1093/bioinformatics/bty266

Cited by 888 publications

(728 citation statements)

References 54 publications

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“…We obtained alignment statistics using ELECTOR's second module, which performs alignment to the reference genome with Minimap2. We performed assemblies using Minimap2 and Miniasm (Li, 2015), and obtained statistics with QUAST-LG (Mikheenko et al, 2018). Results are given in Table 3 for the alignment assessment, and in Table 4 for the assembly assessment.…”

Section: Comparison On Real Datamentioning

confidence: 99%

CONSENT: Scalable long read self-correction and assembly polishing with multiple sequence alignment

Morisse

Marchet

Limasset

et al. 2019

Preprint

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Motivation: Third-generation sequencing technologies Pacific Biosciences and Oxford Nanopore allow the sequencing of long reads of tens of kbp, that are expected to solve various problems, such as contig and haplotype assembly, scaffolding, and structural variant calling. However, they also display high error rates that can reach 10 to 30%, for basic ONT and non-CCS PacBio reads. As a result, error correction is often the first step of projects dealing with long reads. As first long reads sequencing experiments produced reads displaying error rates higher than 15% on average, most methods relied on the complementary use of short reads data to perform correction, in a hybrid approach. However, these sequencing technologies evolve fast, and the error rate of the long reads now reaches 10 to 12%. As a result, self-correction is now frequently used as the first step of third-generation sequencing data analysis projects. As of today, efficient tools allowing to perform self-correction of the long reads are available, and recent observations suggest that avoiding the use of second-generation sequencing reads could bypass their inherent bias. Results: We introduce CONSENT, a new method for the self-correction of long reads that combines different strategies from the state-of-the-art. More precisely, we combine a multiple sequence alignment strategy with the use of local de Bruijn graphs. Moreover, the multiple sequence alignment benefits from an efficient segmentation strategy based on k-mer chaining, which allows a considerable speed improvement. Our experiments show that CONSENT compares well to the latest state-of-the-art selfcorrection methods, and even outperforms them on real Oxford Nanopore datasets. In particular, they show that CONSENT is the only method able to efficiently scale to the correction of Oxford Nanopore ultra-long reads, and is able to process a full human dataset, containing reads reaching lengths up to 1.5 Mbp, in 15 days. Additionally, CONSENT also implements an assembly polishing feature, and is thus able to correct errors directly from raw long read assemblies. Our experiments show that CONSENT outperforms state-of-the-art polishing tools in terms of resource consumption, and provides comparable results. Moreover, we also show that, for a full human dataset, assembling the raw data and polishing the assembly afterwards is less time consuming than assembling the corrected reads, while providing better quality results. Availability and implementation: CONSENT is implemented in C++, supported on Linux platforms and freely available at https://github.com/morispi/CONSENT. Contact: pierre.morisse2@univ-rouen.fr "consent" -2020/3/17 -page 2 -#2

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Section: Comparison On Real Datamentioning

confidence: 99%

CONSENT: Scalable long read self-correction and assembly polishing with multiple sequence alignment

Morisse

Marchet

Limasset

et al. 2019

Preprint

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“…Although several tools exist for evaluating and visualizing the quality of genome assemblies like QUAST-LG [11], Icarus [12], LASER [13], REAPR [14], they are challenging to install and configure, do not support assessment of gene structure annotations, and do not determine the completeness of the repetitive fraction of the genome based on LTR retrotransposon content.…”

Section: Comparison With Similar Software Programsmentioning

confidence: 99%

GenomeQC: A quality assessment tool for genome assemblies and gene structure annotations

Manchanda

Portwood²,

Woodhouse³

et al. 2019

Preprint

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BackgroundGenome assemblies are foundational for understanding the biology of a species. They provide a physical framework for mapping additional sequences, thereby enabling characterization of, for example, genomic diversity and differences in gene expression across individuals and tissue types. Quality metrics for genome assemblies gauge both the completeness and contiguity of an assembly and help provide confidence in downstream biological insights. To compare quality across multiple assemblies, a set of common metrics are typically calculated and then compared to one or more gold standard reference genomes. While several tools exist for calculating individual metrics, applications providing comprehensive evaluations of multiple assembly features are, perhaps surprisingly, lacking. Here, we describe a new toolkit that integrates multiple metrics to characterize both assembly and gene annotation quality in a way that enables comparison across multiple assemblies and assembly types. FindingsOur application, named GenomeQC, is an easy-to-use and interactive web framework that integrates various quantitative measures to characterize genome assemblies and annotations. GenomeQC provides researchers with a comprehensive summary of these statistics and allows for benchmarking against gold standard reference assemblies.

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“…As for the metrics of the assembly, we output the overall number of contigs, the number of contigs that could be aligned, the number of breakpoints of the aligned contigs, the NGA50 and NGA75 sizes of the aligned contigs, as well as the genome coverage. Using the assemblies that we provide, further analyses can be performed using dedicated software such as QUAST-LG (18).…”

Section: Inference Of Quality Evaluation Metrics From Msamentioning

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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Versatile genome assembly evaluation with QUAST-LG

Cited by 888 publications

References 54 publications

CONSENT: Scalable long read self-correction and assembly polishing with multiple sequence alignment

CONSENT: Scalable long read self-correction and assembly polishing with multiple sequence alignment

GenomeQC: A quality assessment tool for genome assemblies and gene structure annotations

ELECTOR: Evaluator for long reads correction methods

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