BLESS: Bloom filter-based error correction solution for high-throughput sequencing reads

Heo, Yun Seok; Wu, Xinzhou; Chen, Deming; Ma, Jian; Hwu, Wen mei

doi:10.1093/bioinformatics/btu030

Cited by 120 publications

(136 citation statements)

References 49 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 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%

Section: Introductionmentioning

confidence: 99%

RECKONER: read error corrector based on KMC

Dlugosz

Deorowicz

2016

Bioinformatics

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“…To accomplish this purpose it uses BLESS [16] for error correction in the reads, the kmers distribution in disk method through the hash…”

Section: Assessed Assemblers That Use Disk Partitioning Techniquesmentioning

confidence: 99%

Disk Partition Techniques Assesment and Analysis Applied to Genomic Assemblers Based on Bruijn Graphs

Vera-Parra¹,

Medina-Daza²,

Rojas-Quintero³

2016

IJBBB

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Abstract:In this paper an assessment of several de-novo genomic assembler tools based on de Bruijn graph is made, with the purpose to measure the impact of the use of disk partitioning techniques regarding the computational requirements and generate a framework for bioinformatics researchers to let them identify advantages, disadvantages, bottlenecks and challenges of the assemblers using those techniques.Assessed assemblers using disk partitioning techniques were: Minia and EPGA, the assessed assemblers that do not use disk partitioning were: ABySS and SOAPDenovo2. The parameters measured were the following: occupied space in RAM, processing time, parallelization and disk read and write access. A dataset was used with 36,504,800 short reads corresponding to 14th human chromosome. The assessment was made for two kmers size: 31 and 55. The results obtained were the following: The tools based on disk partitioning techniques showed the less RAM use. The tools with more I/O transfer intensity were the ones using disk partitioning techniques. The techniques that achieved more parallelization were the ones using disk partitioning.

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“…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.…”

Section: Type II Error Caused By Cross-contaminationmentioning

confidence: 99%

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

Zhan

MacIsaac

2014

Conserv Genet

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Identification of rare species and mapping their distributions is crucial for understanding natural species distributions and causes and consequences of accelerating species declines. However, detection of rare species in both terrestrial and especially aquatic communities typically dominated by numerous microscopic species (i.e. rare biosphere) represents a formidable technical challenge. Rapid advances in high-throughput sequencing (HTS) technologies have revolutionized biodiversity studies in the rare biosphere, and also stimulated associated debates. Here we summarize research progress, discuss debates and problems, and propose possible solutions and future studies to address these issues. In addition, we provide take-home messages for experimental design and data interpretation when utilizing HTS techniques for rare biosphere exploration in ecology and conservation biology.

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BLESS: Bloom filter-based error correction solution for high-throughput sequencing reads

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 120 publications

References 49 publications

RECKONER: read error corrector based on KMC

RECKONER: read error corrector based on KMC

Disk Partition Techniques Assesment and Analysis Applied to Genomic Assemblers Based on Bruijn Graphs

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

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