Musket: a multistage <i>k-</i>mer spectrum-based error corrector for Illumina sequence data

Liu, Yongchao; Schröder, Jan; Schmidt, Bertil

doi:10.1093/bioinformatics/bts690

Cited by 268 publications

(251 citation statements)

References 29 publications

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“…For testing we picked data from different Illumina sequencers. We compared RECKONER and the following state-of-the-art algorithms: Ace (Sheikhizadeh and de Ridder, 2015), BFC (Li, 2015), BLESS (Heo et al, 2014), Blue (Greenfield et al, 2014), Karect (Allam et al, 2015), Lighter (Li et al, 2014), Musket (Liu et al, 2013), Pollux (Marinier et al, 2015), RACER (Ilie and Molnar, 2013), Trowel (Lim et al, 2014). We excluded from this group the popular Quake due to its poor results according to the recent studies (Heo et al, 2014;Lim et al, 2014;Liu et al, 2013;Marinier et al, 2015).…”

Section: Resultsmentioning

confidence: 99%

“…The applied method of reads generation was proposed and used in (Kelley et al, 2010) and (Liu et al, 2013). First of all, we needed to choose the expected coverage and read length.…”

Section: Simulated Data Evaluationmentioning

confidence: 99%

“…The algorithms tend to introduce the least possible number of changes, or, more precisely, such changes that repair most likely errors. This category includes: Quake (Kelley et al, 2010), RACER (Ilie and Molnar, 2013), BLESS (Heo et al, 2014), Blue (Greenfield et al, 2014), Musket (Liu et al, 2013), Lighter (Li et al, 2014), Trowel (Lim et al, 2014), Pollux (Marinier et al, 2015), BFC (Li, 2015), Ace (Sheikhizadeh and de Ridder, 2015).…”

Section: Introductionmentioning

confidence: 99%

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RECKONER: read error corrector based on KMC

Dlugosz

Deorowicz

2016

Bioinformatics

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Motivation: Next-generation sequencing tools have enabled producing of huge amount of genomic information at low cost. Unfortunately, presence of sequencing errors in such data affects quality of downstream analyzes. Accuracy of them can be improved by performing error correction. Because of huge amount of such data correction algorithms have to: be fast, memory-frugal, and provide high accuracy of error detection and elimination for variously-sized organisms. Results: We introduce a new algorithm for genomic data correction, capable of processing eucaryotic 300 Mbp-genome-size, high error-rated data using less than 4 GB of RAM in less than 40 minutes on 16-core CPU. The algorithm allows to correct sequencing data at better or comparable level than competitors. This was achieved by using very robust KMC 2 k-mer counter, new method of erroneous regions correction based on both k-mer counts and FASTQ quality indicators as well as careful optimization. Availability: Program is freely available at http://sun.aei.posl.pl/REFRESH.

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Section: Resultsmentioning

confidence: 99%

“…The applied method of reads generation was proposed and used in (Kelley et al, 2010) and (Liu et al, 2013). First of all, we needed to choose the expected coverage and read length.…”

Section: Simulated Data Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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RECKONER: read error corrector based on KMC

Dlugosz

Deorowicz

2016

Bioinformatics

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“…For example, in [22] the authors show a highly accurate method for NGS reads comparison. Another example may be [13] where a method eliminating outlier reads during the data filtration stage of the processing pipeline was presented. To be more precise, the reads containing unique k-mers are treated by the method as invalid and are either corrected or discarded.…”

Section: The Use Of K-mer In Biological Sequence Comparisonmentioning

confidence: 99%

G-MAPSEQ – a new method for mapping reads to a reference genome

Wojciechowski¹,

Frohmberg²,

Kierzynka³

et al. 2016

Foundations of Computing and Decision Sciences

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Abstract. The problem of reads mapping to a reference genome is one of the most essential problems in modern computational biology. The most popular algorithms used to solve this problem are based on the Burrows-Wheeler transform and the FM-index. However, this causes some issues with highly mutated sequences due to a limited number of mutations allowed. G-MAPSEQ is a novel, hybrid algorithm combining two interesting methods: alignment-free sequence comparison and an ultra fast sequence alignment. The former is a fast heuristic algorithm which uses k-mer characteristics of nucleotide sequences to find potential mapping places. The latter is a very fast GPU implementation of sequence alignment used to verify the correctness of these mapping positions. The source code of G-MAPSEQ along with other bioinformatic software is available at: http://gpualign.cs.put.poznan.pl.

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“…Some papers [6,12] specifically list this as an area where error correction has a very strong and positive impact. Finally, the short reads alignment is also improved after error correction [13,14,15,16]. Variant calling is an application of NGS directly related to alignment.…”

Section: Motivationmentioning

confidence: 99%

Improved Error Correction of NGS Data

Alic¹

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SummaryThe work done for this doctorate thesis focuses on error correction of Next Generation Sequencing (NGS) data in the context of High Performance Computing (HPC).Due to the reduction in sequencing cost, the increasing output of the sequencers and the advancements in the biological and medical sciences, the amount of NGS data has increased tremendously. Humans alone are not able to keep pace with this explosion of information, therefore computers must assist them to ease the handle of the deluge of information generated by the sequencing machines. Since NGS is no longer just a research topic (used in clinical routine to detect cancer mutations, for instance), requirements in performance and accuracy are more stringent. For sequencing to be useful outside research, the analysis software must work accurately and fast. This is where HPC comes into play. NGS processing tools should leverage the full potential of multi-core and even distributed computing, as those platforms are extensively available. Moreover, as the performance of the individual core has hit a barrier, current computing tendencies focus on adding more cores and explicitly split the computation to take advantage of them.This thesis starts with a deep analysis of all these problems in a general and comprehensive way (to reach out to a very wide audience), in the form of an exhaustive and objective review of the NGS error correction field. We dedicate a chapter to this topic to introduce the reader gradually and gently into the world of sequencing. It presents real problems and applications of NGS that demonstrate the impact this technology has on science. The review results in the following conclusions: the need of understanding of the specificities of NGS data samples (given the high variety of technologies and features) and the need of flexible, efficient and accurate tools for error correction as a preliminary step of any NGS postprocessing.As a result of the explosion of NGS data, we introduce MuffinInfo. It is a piece of software capable of extracting information from the raw data produced by the sequencer to help the user understand the data. MuffinInfo uses HTML5, therefore it runs in almost any software and hardware environment. It supports custom statistics to mould itself to specific requirements. MuffinInfo can reload the results of a run which are stored in JSON format for easier integration with third party applications. Finally, our application uses threads to perform the calculations, to load the data from the disk and to handle the UI.In continuation to our research and as a result of the single core performance limitation, we leverage the power of multi-core computers to develop a new error correction tool. The error correction of the NGS data is normally the first step of any analysis targeting NGS. As we conclude from the review performed within the frame of this thesis, many projects in different real-life applications have opted for this step before further analysis. In this sense, we propose MuffinEC, a multi-technology (Illu...

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Musket: a multistage k-mer spectrum-based error corrector for Illumina sequence data

Abstract: Musket is available at http://musket.sourceforge.net.

Cited by 268 publications

References 29 publications

RECKONER: read error corrector based on KMC

RECKONER: read error corrector based on KMC

G-MAPSEQ – a new method for mapping reads to a reference genome

Improved Error Correction of NGS Data

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