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

Abstract

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

Unveiling of a novel bifunctional photoreceptor, Dualchrome1, isolated from a cosmopolitan green alga

“…The raw reads were assembled into 47 scaffolds (22.9 Mbp) with one gap using MaSuRCA 3.3.2 49 . The nanopore reads were corrected using CONSENT v1.1.2 50 . The paired-end reads were corrected using Trimmomatic version 0.36 51 .…”

Long-read error correction: a survey and qualitative comparison

“…As for hybrid correction, other methods, such as CONSENT [54], combine these two approaches, in order to counterbalance their advantages and their drawbacks. We describe each approach more into details, and list the related tools, in the following subsections.…”

“…As for hybrid correction, some methods also rely on combinations of the two previously described strategies. For instance, CONSENT [54] relies on both multiple sequence alignment and de Bruijn graphs, which it combines into a two-step error correction process.…”

Error correction enables use of Oxford Nanopore technology for reference-free transcriptome analysis

“…As (Lima et al 2019) found that applying genomic error correctors to ONT transcriptome data had undesirable downstream effects, we only evaluated the genomic error corrector CONSENT (Morisse et al 2019) that was not evaluated in (Lima et al 2019) as well as canu (Koren et al 2017) and concluded similarly to (Lima et al 2019) that they are not suitable for long transcriptomic reads (see Supplementary note C). We instead focused on comparing our tool to a recent long transcriptomic read error correcting tool RATTLE (de la Rubia et al, 2020 ) .…”