A comparative study of k-spectrum-based error correction methods for next-generation sequencing data analysis

Abstract

Background: Innumerable opportunities for new genomic research have been stimulated by advancement in high-throughput next-generation sequencing (NGS). However, the pitfall of NGS data abundance is the complication of distinction between true biological variants and sequence error alterations during downstream analysis. Many error correction methods have been developed to correct erroneous NGS reads before further analysis, but independent evaluation of the impact of such dataset features as read length, genome size, and coverage depth on their performance is lacking. This comparative study aims to investigate the strength and weakness as well as limitations of some newest k-spectrum-based methods and to provide recommendations for users in selecting suitable methods with respect to specific NGS datasets.

Methods: Six k-spectrum-based methods, i.e., Reptile, Musket, Bless, Bloocoo, Lighter, and Trowel, were compared using six simulated sets of paired-end Illumina sequencing data. These NGS datasets varied in coverage depth (10× to 120×), read length (36 to 100 bp), and genome size (4.6 to 143 MB). Error Correction Evaluation Toolkit (ECET) was employed to derive a suite of metrics (i.e., true positives, false positive, false negative, recall, precision, gain, and F-score) for assessing the correction quality of each method.

Results: Results from computational experiments indicate that Musket had the best overall performance across the spectra of examined variants reflected in the six datasets. The lowest accuracy of Musket (F-score = 0.81) occurred to a dataset with a medium read length (56 bp), a medium coverage (50×), and a small-sized genome (5.4 MB). The other five methods underperformed (F-score < 0.80) and/or failed to process one or more datasets.

Conclusions: This study demonstrates that various factors such as coverage depth, read length, and genome size may influence performance of individual k-spectrum-based error correction methods. Thus, efforts have to be paid in choosing appropriate methods for error correction of specific NGS datasets. Based on our comparative study, we recommend Musket as the top choice because of its consistently superior performance across all six testing datasets. Further extensive studies are warranted to assess these methods using experimental datasets generated by NGS platforms (e.g., 454, SOLiD, and Ion Torrent) under more diversified parameter settings (k-mer values and edit distances) and to compare them against other non-k-spectrum-based classes of error correction methods.

Keywords: Bloom filter; Error correction; Next-generation sequencing (NGS); Sequence analysis; k-mer; k-spectrum.

Fig. 1

General framework of k-spectrum-based error correctors

Fig. 2

Workflow of error correction performance analysis using ECET (Error Correction Evaluation Toolkit [15]). See http://aluru-sun.ece.iastate.edu/doku.php?id=ecr for more information

Fig. 3

Impact of read length (a), coverage depth (b), and genome size (c) on the performance of six k-spectrum-based error correction methods. The six datasets are reordered according to the factor examined in order to show visually the effect of each factor on F-score for each method (see Table 3 for dataset, method, and F-score information)