Long-read error correction: a survey and qualitative comparison

Abstract

Third generation sequencing technologies Pacific Biosciences and Oxford Nanopore Technologies were respectively made available in 2011 and 2014. In contrast with second generation sequencing technologies such as Illumina, these new technologies allow the sequencing of long reads of tens to hundreds of kbps. These so called long reads are particularly promising, and are especially expected to solve various problems such as contig and haplotype assembly or scaffolding, for instance. However, these reads are also much more error prone than second generation reads, and display error rates reaching 10 to 30%, according to the sequencing technology and to the version of the chemistry. Moreover, these errors are mainly composed of insertions and deletions, whereas most errors are substitutions in Illumina reads. As a result, long reads require efficient error correction, and a plethora of error correction tools, directly targeted at these reads, were developed in the past nine years. These methods can adopt a hybrid approach, using complementary short reads to perform correction, or a self-correction approach, only making use of the information contained in the long reads sequences. Both these approaches make use of various strategies such as multiple sequence alignment, de Bruijn graphs, hidden Markov models, or even combine different strategies. In this paper, we describe a complete survey of long-read error correction, reviewing all the different methodologies and tools existing up to date, for both hybrid and self-correction. Moreover, the long reads characteristics, such as sequencing depth, length, error rate, or even sequencing technology, can have an impact on how well a given tool or strategy performs, and can thus drastically reduce the correction quality. We thus also present an in-depth benchmark of available long-read error correction tools, on a wide variety of datasets, composed of both simulated and real data, with various error rates, coverages, and read lengths, ranging from small bacterial to large mammal genomes.2. Alignment of long reads and contigs obtained from short reads assembly. In the same fashion, long reads can also be corrected with the help of the contig they align to, by computing consensus sequences from these contigs. ECTools [43], HALC [6], and MiRCA [30] adopt this methodology. 3. Use of de Bruijn graphs, built from the short reads' k-mers. Once built, the long reads can indeed be anchored to the graph. It can then be traversed, in order to find paths allowing to link together anchored regions of the long reads, and thus correct unanchored regions. LoRDEC [59], Jabba [52], FMLRC [67], and ParLECH [16] rely on this strategy.4. Use of Hidden Markov Models. These can indeed be used in order to represent the long reads. The models can then be trained with the help of short reads, in order to extract consensus sequences, representing the corrected long reads. Hercules [21] is based on this approach.Other methods, such as NaS [49] and HG-CoLoR [53], combine different of the aforementioned ...

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