ECHO: A reference-free short-read error correction algorithm

Abstract

Developing accurate, scalable algorithms to improve data quality is an important computational challenge associated with recent advances in high-throughput sequencing technology. In this study, a novel error-correction algorithm, called ECHO, is introduced for correcting base-call errors in short-reads, without the need of a reference genome. Unlike most previous methods, ECHO does not require the user to specify parameters of which optimal values are typically unknown a priori. ECHO automatically sets the parameters in the assumed model and estimates error characteristics specific to each sequencing run, while maintaining a running time that is within the range of practical use. ECHO is based on a probabilistic model and is able to assign a quality score to each corrected base. Furthermore, it explicitly models heterozygosity in diploid genomes and provides a reference-free method for detecting bases that originated from heterozygous sites. On both real and simulated data, ECHO is able to improve the accuracy of previous error-correction methods by several folds to an order of magnitude, depending on the sequence coverage depth and the position in the read. The improvement is most pronounced toward the end of the read, where previous methods become noticeably less effective. Using a wholegenome yeast data set, it is demonstrated here that ECHO is capable of coping with nonuniform coverage. Also, it is shown that using ECHO to perform error correction as a preprocessing step considerably facilitates de novo assembly, particularly in the case of low-to-moderate sequence coverage depth.[Supplemental material is available for this article. ECHO is publicly available at http://uc-echo.sourceforge.net under the Berkeley Software Distribution License.]Over the past few years, next-generation sequencing (NGS) technologies have introduced a rapidly growing wave of information in biological sciences; see Metzker (2010) for a recent review of NGS platforms and their applications. Exploiting massive parallelization, NGS platforms generate high-throughput data at very low cost per base. An important computational challenge associated with this rapid technological advancement is to develop efficient algorithms to extract accurate sequence information. In comparison with traditional Sanger sequencing (Sanger et al. 1977), NGS data have shorter read lengths and higher error rates, and these characteristics create many challenges for computation, especially when a reference genome is not available. Reducing the error rate of base-calls and improving the accuracy of base-specific quality scores have important practical implications for assembly (Sundquist et al.

Rare biosphere exploration using high-throughput sequencing: research progress and perspectives

“…As the awareness of sequencing errors increases, many new pipelines have been developed to either correct or remove sequencing errors, such as Blue (Greenfield et al 2014), BLESS (Heo et al 2014), UPARSE (Edgar 2013), Coral (Salmela and Schröder 2011), ECHO (Kao et al 2011), HiTEC (Ilie et al 2011), HSHREC (Salmela 2010), Reptile ) and others (see review by Yang et al 2013). These pipelines have their own advantages to handle certain types of data.…”

Rule-based Assembly for Short-read Datasets Obtained with Multiple Assemblers and <i>k</i>-mer Sizes

“…Despite the efficacy of giga-sequence technology, the short reads obtained often contain reading errors, resulting in misassembly. Therefore, various methods have also been developed to remove such erroneous reads, such as Trimmomatic [4], ECHO [5], and Quake [6].…”

RECKONER: read error corrector based on KMC

“…Then they perform one of multiple sequence alignment algorithms on these reads with aim of finding the consensus form of the reads. This category involves Coral (Salmela and Schröder, 2011), ECHO (Kao et al, 2011), Karect (Allam et al, 2015). Fiona (Schulz et al, 2014) is a hybrid approach, as it uses suffix tree and also performs multiple sequence alignment.…”