A comparative study of k-spectrum-based error correction methods for next-generation sequencing data analysis
© Akogwu et al. 2016
Published: 25 July 2016
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.
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 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.
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.
Rapid generation and availability of massive amounts of DNA sequence data generated using next-generation sequencing (NGS) technologies at lower cost in comparison to traditional Sanger sequencing has led to a genuine ability to decipher genomes and perform ground-breaking biological research . Some instances of the far reaching applications of NGS data include human genome profiling , microbiome research , de novo genome assembly , meta-genomics, and uncommon genetic variants identification . In practice, NGS data has its challenges due to its relatively shorter read length and higher error rates in comparison to traditional Sanger sequencing , consequently constituting an undesirable property for downstream investigation.
Sequence error rates for different NGS platforms in comparison with the traditional Sanger technology (updated as of 2012 at http://www.molecularecologist.com/next-gen-table-3c-2014/). See Glenn (2011)  for more details. Single-pass reads are those raw sequences that have not been subject to consensus adjustment incorporated in final base calling
Primary error type
Single-pass error rate (%)
Final error rate (%)
Sanger—ABI 3730XL capillary (benchmark)
Ion Torrent—all chips
The general idea for correcting sequencing errors is that erroneous bases (i.e., nucleotides) in a DNA sequence read can be corrected using the majority of reads that have these bases correctly since errors occur infrequently and independently. Many error correction methods have been developed, some implemented as an integral part of a computational tool for de novo genome assembly [10, 11], while others as standalone tools [12–14]. Based on the type of data structure applied, these methods can be categorized into four main classes [15, 16]: k-mer spectrum-based (or k-spectrum-based) (e.g., Quake , Reptile  and Hammer ), suffix t-ree/array-based (HiTEC , SHREC  and Hybrid-SHREC ), multiple sequence alignment (MSA)-based (ECHO  and Coral ) and hidden Markov model (HMM)-based (PREMIER  and SEECER ).
Since Yang et al.  last reviewed error correction methods available as of December 2011, many new algorithms have been developed, especially in the fast-growing category of k-spectrum-based methods. In the past 2 years, at least five new k-spectrum-based methods have emerged, including Musket , Bless , Trowel , Lighter , and Bloocoo . These new tools have made significant improvements in either memory usage, speed, or correction quality over previously existing tools. Authors of these new tools carried out a varying degree of comparison with other tools on synthetic and/or experimental datasets in order to demonstrate the superiority of their own tools. However, these authors chose different competitors for comparison and used different datasets and metrics for evaluation, making it hard to tell the relative strength and weakness between themselves.
The goal of this comparative study is to conduct an independent and unbiased assessment of the newly developed k-spectrum-based error correctors and to provide some guidance concerning how to choose a suitable error correction tool from the long list of existing methods. In particular, there is a lack of available studies that comprehensively investigate such factors as reference genome size, read length, and genome coverage depth that may differentially influence the performance of individual algorithm. Here, we first introduce the basics of k-spectrum error correction algorithms, and briefly review six sequence error correctors chosen for this study with an emphasis on distinctive features of each tool. Then, we present analytical results of method evaluation using simulated NGS datasets and make suggestions on which method is more suitable for a specific sequencing dataset. Finally, we discuss future perspectives and further studies required for assessing the performance of existing and newly developed error correction programs.
General framework of k-spectrum-based error correction methods
The majority of the methods investigated in this study apply Bloom filters as their data structure. As a space-efficient probabilistic data structure, a Bloom filter is used to test whether an element is a member of a set using binary array and multiple hash functions . It can accurately determine a non-member element of the set. A query may return false positives but no false negatives, thus a bloom filter has a 100 % recall rate. A Bloom filter does not store the elements themselves but allows testing whether an object is certainly absent in the filter or has been probably added to the Bloom filter. For sequence error correction purposes, most methods implement the counting Bloom filter variant where array positions are not single bits but an n-bit counter. The efficiency of Bloom filters relies upon the number of bits in the array, the number of hash functions, and most importantly the quality of the hash functions.
Selected k-spectrum-based error correction methods
Characteristic features of the six k-spectrum-based methods investigated in the present comparative study which distinguish one method from others
Target error type
Explore multiple alternative k-mer decompositions and contextual information of neighboring k-mers for error correction
Contextual information can help resolve errors without increasing k and lowering local coverage
Uses a single core (non-parallelized)
Multi-stage correction: two-sided conservative, one-sided aggressive and voting-based refinement
Multi-threading based on a master–slave model results in high parallel scalability
A single static coverage cut-off to differentiate trusted k-mers from weak ones
Count k-mer multiplicity; correct errors using Bloom filter; restore false positives
High memory efficiency; handle genome repeats better; correct read ends
Cannot automatically determine the optimal k value
Parallelized multi-stage correction algorithm (similar to Musket)
Blocked Bloom filter
Faster and lower memory usage than Musket
Not extensively evaluated
Rely on quality values to identify solid k-mers; use two algorithms (DBG and SBE) for error correction
Correct erroneous bases and boost base qualities
Only accept FASTQ files as input
Random sub-fraction sampling; parallelized error correction
Pattern-blocked Bloom filter
No k-mer counting; near constant accuracy and memory usage
A user must specify k-mer length, genome length, and sub-sampling fraction α
Reptile  explores multiple alternative k-mer decompositions of an erroneous read and corrects errors by simultaneously examining Hamming (the number of positions at which two strings differ) distance-based correction possibilities for potentially erroneous k-mers using neighboring k-mers from the same read for correct contextual information. It also incorporates quality score information when available and has functionality to deal with ambiguous bases labeled as N’s. Reptile achieved a significant reduction in run time and memory usage and improvement in correction quality when compared with such existing methods as SHREC, Coral, Hybrid-SHREC, Quake, HiTEC, and ECHO [13, 15].
Musket  uses a multi-stage workflow including two-sided conservative correction, one-sided aggressive correction, and voting-based refinement. It computes the multiplicity of each k-mer in the hash table in order to filter out the stored unique k-mers using Bloom filter. A parallelized slave-master k-mer counting method is implemented to sort out unique k-mers and then generates k-mer coverage histograms to determine a cut-off for a k-mer spectrum for the coverage of likely correct and erroneous k-mers. The error correction stage initially uses a two-sided correction that conservatively corrects one substitution error, at most, in any k-mer of a read with the intention of finding a unique alternative base that makes all k-mers covering the position trusted. Significant improvement in speed can be achieved by evaluating only the leftmost and rightmost k-mers that cover the position. It then applies a one-sided correction to aggressively correct errors in the case of more than one error occurring in a single k-mer. Furthermore, to confine the number of false positives, error correction is conducted for each integer value from 1 to the maximal allowable number of corrections. The drawback is its reliance on alternative selection in the event that a k-mer is wrongly called to be trusted even though it contains sequencing errors or incorrect corrections. To overcome this drawback, look-ahead validation and voting-based refinement are implemented to assess the trustiness of a predefined maximal number (default = 2) of neighboring k-mers that cover the base position at which a sequencing error likely occurs. If all evaluated k-mers are trusted for a certain alternative on that position, this alternative is reserved as one potential correction.
Bless  uses a single minimum-sized Bloom filter and disk-based k-mer counting algorithm like disk streaming of k-mers (DSK)  and k-mer counter (KMC)  to achieve high memory efficiency for error correction, sequence repeat handling, and read end correction by read extension. Briefly, it counts k-mer multiplicity to sort out solid k-mers from weak k-mers, creates a k-mer multiplicity histogram to determine the multiplicity threshold M, and programs those solid k-mers into a Bloom filter. Weak k-mers are converted to their canonical forms using consecutive solid k-mers (known as k-mer islands) in their neighborhood or read end through Bloom filter querying. Bases in a weak k-mer that do not overlap with solid k-mers are modified. For instance, weak k-mers that exist between two consecutive solid k-mer islands S 1 and S 2 are corrected by using the rightmost k-mer of S 1 and the leftmost k-mer of S 2. Bless has three distinctive features: high memory efficiency, better handling of genome repeats, and more accurate error correction at read ends.
As part of the Genome Assembly & Analysis Took Box (GATB) , Bloocoo was developed to correct large datasets with low memory footprints by using DSK , a counting algorithm that requires a user to define a fixed amount of memory and disk space. Its error correction process is similar to CUSHAW , a procedure also used by Musket. In Bloocoo, the multi-set of all k-mers present in the reads is partitioned, and partitions are saved to disk. Then, each partition is separately loaded into memory in a temporary hash table. The k-mer counts are returned by traversing each hash table. Low-abundance k-mers are optionally filtered and solid k-mers are inserted in the Bloom filter based on a given threshold. With a multi-stage correction approach similar to Musket , correction is performed by scanning k-mers of a read, trying the other three different possible nucleotides at the error site, and checking if corresponding k-mers are in the set of solid k-mers. When several close errors occur, the pattern is more complex, and errors are corrected via a voting algorithm. Bloocoo distinguishes itself from other error correctors in the k-mer counting stage and the way that solid k-mers are stored in memory. By using only 11 bits of memory per solid k-mers, Bloocoo requires only 4-GB memory for the entire human genome re-sequencing read correction at 70× coverage.
Different from other tools, Lighter  samples k-mers randomly, i.e., sub-sampling fraction α rather than counting k-mers. It uses a pattern-blocked Bloom filter  to decrease the overall number of cache misses and improve memory efficiency. Lighter populates Bloom filter A with a k-mer subsample, followed by a simple test applied to each position of each read to compile a set of solid k-mers, and then stores the solid k-mers in Bloom filter B. A sequenced k-mer survives sub-sampling with probability of α, a user determined sub-sampling fraction that is set to be 0.10(70/C) with C being average coverage. For error correction, Lighter applies a greedy approach like that used in Bless  and extends a read when an error is located near the end of the read. Error correction is parallelized by using concurrent threads to handle subsets of the reads. Lighter maintains near constant accuracy and Bloom filter size as long as the sampling fraction is adjusted in inverse proportion to the coverage depth. However, a user has to specify k-mer length, genome length, and sub-sampling fraction α.
Trowel  is a highly parallelized and efficient error correction module for Illumina sequencing reads. The key difference to other tools is that Trowel relies on contiguity of high quality values instead of a k-mer coverage distribution to differentiate between solid and weak k-mers. The algorithm not only improves low quality bases but also iteratively expands the trusted k-mer set by including corrected k-mers. Trowel applies two different algorithms, Double Bricks & Gap (DBG) and Single Brick & Edges (SBE), to increase the likelihood that a correction can be made and to boost quality values. The DBG algorithm exploits an asymmetric k 1-gap-k 2 structure, where a gap is a single base, k = k 1 + k 2. The quality of the gap is boosted to the maximum quality value when the index relevant to gap-enclosing bricks contains the gap with high quality. The SBE algorithm is used because bases at read ends cannot be accessed by the brick index. Hence, a new edge-k-edge index is used to correct edges, where an edge is a single base, or increase their quality values as in the DBG algorithm.
Synthetic paired-end Illumina sequencing datasets simulated using ART
Organism (dataset ID)
Accession number of reference genome assembly
ART simulation parameter
Genome size (MB)
Read length (bp)
Error rate (%)
Escherichia coli (EC-1)
Escherichia coli (EC-2)
Escherichia coli (EC-3)
Bacillus cereus (BC-1)
Bacillus cereus (BC-2)
Drosophila melanogaster (DM)
GCF_000001215.4 (Release 6)
Evaluation workflow and metrics
As shown in Fig. 2, the workflow consists of the following steps: sequencing dataset simulation, pre- and post-correction alignment to reference genome, parameter optimization for error correctors, and derivation of evaluation statistics and metrics (see http://aluru-sun.ece.iastate.edu/doku.php?id=ecr for more details). Briefly, both error-free and error-containing paired-end sequences were generated in FASTQ format by ART simulation. The error-free data served for the QA/QC purpose throughout the workflow. After converting FASTQ to FASTA (pre-process due to ECET’s header requirements before alignment), simulated sequences were aligned to a reference genome using BWA. The SAM alignment files produced by BWA were then converted to TEF format using ECET . Error-containing datasets were corrected using error correction tools. The error correction outputs from these tools were converted to TEF files in ECET. The TEF files that generated pre- and post-correction were compared using the Comp2PCAlign script provided in ECET to produce statistics and metrics for performance assessment. Since the quality and accuracy of error correction tools are highly dependent on parameter (particularly k-mer) settings, we introduced an iterative optimization loop to select an optimal k value by implementing KmerGenie. In this loop, we also tweaked other parameters while maintaining the same optimal k-mer selected for a specific dataset. For instance, α is a user-defined parameter in Lighter and its default value is set by the formula 0.1(70/C), where C is the coverage depth.
We chose the following widely used metrics to evaluate correction quality [13, 15, 16, 24–27]: true positives (TP)—an erroneous base correctly changed to its true base; false positives (FP)—a true base incorrectly changed; false negative (FN)—incorrect base left unchanged; true negative (TN)—true base left unchanged; recall or sensitivity = TP/(TP + FN), precision = TP/(TP + FP), gain = (TP − FP)/(TP + FN), and F-score = 2 × ((precision × recall)/(precision + recall)).
Computational experiments were conducted using multiple machines due to specific requirements of individual tools and varied sizes of synthetic testing datasets; hence, consideration was not given to the performance in terms of run time and memory usage but rather effectiveness and accuracy of read error correction. Due to requirement of Message Passing Interface (MPI), Bless was run on a Red Hat Enterprise Linux MPI cluster with 12 nodes, and each node had 12-GB memory and 8 cores running at a core speed of 2.93 GHz. For all other tools, datasets with a genome size >5 MB were run on a 64-bit Ubuntu 12.04 LTS Intel Core i7-3770 CPU@ 3.40 GHz machine with 8 cores and 8-GB memory. The E. coli datasets (genome size <5 MB) were run on a CentOS—64-bit Intel(R) Xeon(R) CPU E5630@ 2.53 GHz machine with 16 processors and a total memory of 296 GB.
Performance analysis of six k-spectrum-based error correctors as evaluated using six synthetic Illumina datasets
k = 19
k = 17
k = 24
k = 27
k = 31
k = 21
Influence of read length on performance
Influence of genome coverage depth on performance
A medium coverage depth (50- and 70-fold) appeared to be preferred by all tested tools except Trowel (Fig. 3b). At a low depth (10- and 20-fold), Reptile and Bless performed well except for the long-read dataset EC-3. Lighter seemed to require a medium-to-high coverage depth (50-fold or higher). In case of low depth (20-fold), a longer read length might compensate for the loss of coverage depth, resulting in a satisfactory performance. At the highest coverage depth (120-fold), two tools failed (Bless) or underperformed (Bloocoo). Again, Musket showed the strongest resistance to variation in coverage depth.
Influence of genome size on performance
Genome size is most likely a covariant that interacts with the other two factors (read length and coverage depth) because instances of underperformance occurred across the full spectrum of genome size (Fig. 3c). For small genomes (EC and BC), Musket was the best method, followed by Lighter and Reptile (both performed well in four of five datasets), then Bless and Bloocoo (three of five datasets), and Trowel ranked the last. For the large genome (DM), only three methods (Reptile, Bless and Musket) performed well.
Different Bloom filter variants were implemented in four of the six investigated methods to allow compression of the filter, storage of count data, and representation of maps in addition to sets  (also see Table 2). The other two methods (Reptile and Trowel) used hash tables, which do not yield false positive. Although Bloom filter’s space efficiency comes at the cost of false positives, all major error correction programs have reduced or minimized false positive rate by implementing various algorithms. Authors who developed these six tools had put lots of efforts in increasing speed and reducing memory footprint while maintaining or improving their correction quality. In the present study, we chose to focus solely on correction quality because speed and memory are no longer bottlenecking factors that limit the application of these tools.
Simulated datasets were used because correction accuracy could be directly measured. When real experimental datasets are used, only indirect evaluation metrics (e.g., N50 contig size and genome coverage of de novo assemblies and percentage of mapped reads in genome alignment) can be derived for performance assessment. We believe that the usage of real datasets in tool evaluation can provide insights that cannot be obtained from simulation studies. Nevertheless, extensive evaluations should be conducted using simulated datasets before moving on to real datasets. Authors of the six tools investigated in our study have performed evaluations using both synthetic and real datasets. In general, tools that perform well with synthetic datasets also work well with real datasets (see publications featuring Bless , Trowel , and Lighter ). There is a good correlation between performance metrics for simulated and real datasets.
Previous evaluations showed that Musket was consistently one of the top performing correctors for both simulated and real datasets when it was compared with several well-regarded programs: HiTEC, SHREC, Coral, Quake, Reptile, DecGPU, and SGA . Here, we also demonstrated that Musket yielded better performance metrics than Reptile. When authors of Bless , Trowel , and Lighter  performed their comparative evaluations, they claimed that their own tools slightly outperformed Musket. However, if looking more specifically into simulated datasets, Musket performed equally well as the other three tools did (e.g., the synthetic 40× human chromosome 1 dataset used in ). Bloocoo shares a great deal of similarity with Musket, especially in the multi-stage error correction algorithm [24, 27]. They reportedly achieved similar correction accuracy as measured by recall and precision on a simulated dataset with 1 % error rate from human chromosome 1 at 70× coverage (see “Supplementary Material” in ). In the current study, these two programs did perform equally well on three datasets (EC-1, EC-2, and BC-1 with read length of 36 or 56 bp). However, Bloocoo underperformed or failed on the remaining three datasets with longer reads (100 bp), suggesting the existence of potential bottleneck factors in the scripts of Bloocoo that limit its application to longer reads.
An inherent difficulty in using any corrector is the challenge of choosing optimal parameters . Very few tools have implemented automated choice of parameters sensitive to datasets being processed. Although Bless  can automatically choose an appropriate value for M, k-mer multiplicity threshold, it cannot select an optimal k and nor can other tools evaluated in this study (except for Reptile , which chooses k = log4|G|, where G is the genome length). We used KmerGenie  to determine an optimal k for each dataset. While it is possible that the k picked by KmerGenie may not be the optimal value for all six evaluated tools, we performed limited tests in tweaking k and other user-defined, tool-specific parameters but did not observe significant deviations in terms of performance metrics (data not shown). For similar reasons, we set edit distance to 2 (36/56-bp reads) or 4 (100-bp reads) for read alignment based on the recommendation of 4 % read length (see http://bio-bwa.sourceforge.net/bwa.shtml). Therefore, we only reported in Table 4 the results obtained under default settings for tool-specific parameters, the chosen k values (determined by KmerGenie), and the fixed edit distances.
Identifying and correcting errors in NGS data is an important step before carrying out any further downstream in-depth analysis. This comparative study aimed to provide an independent and unbiased evaluation of the effects of three NGS dataset features on the performance of six recently published k-spectrum-based error correction methods with an emphasis on correction accuracy. We observed that performance of six selected methods was dependent on such factors as read length, genome size and coverage depth. Our experimental results suggest that good performance of a method for a specific dataset does not guarantee its ability to perform as well for another type of dataset, hence careful consideration should be given to selecting appropriate tools. Among the six tested methods, Musket appeared to be the front runner, whereas Trowel showed the worst performance. We recommend Musket as the top choice because of its consistently superior performance across all six testing datasets. In future studies, we will expand to other classes of error correction methods and more diversified datasets such as those generated by different NGS platforms (e.g., 454, SOLiD, and Ion Torrent), a wider spectrum of genome size and complexity (e.g., human and mouse genomes), and longer reads (e.g., 300 to 500 bp). More in-depth evaluation is also warranted to investigate other factors like real datasets generated from a wide range of applications (e.g., transcriptome mapping, SNP genotyping, and de novo genome assembly) as well as data structure (e.g., Cucoo filter vs. Bloom filter) on error correction outcomes.
This work was supported by a National Science Foundation award (EPS 0903787) to CZ and NW and an intramural grant from the US Army Environmental Quality/Installation (EQI) Basic Research Program to PG. Permission was granted by the Chief of Engineer to publish this paper.
Funding for publication of the article came from the US Army EQI Basic Research Program.
This article has been published as part of Human Genomics Volume 10 Supplement 2, 2016: From genes to systems genomics: human genomics. The full contents of the supplement are available online at http://humgenomics.biomedcentral.com/articles/supplements/volume-10-supplement-2.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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