Correcting errors in short reads by multiple alignments

Salmela, Leena; Schröder, Jan

doi:10.1093/bioinformatics/btr170

Cited by 152 publications

(167 citation statements)

References 21 publications

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“…The suffix tree-based methods [7,8] generalize the k-spectrum methods by handling multiple k values at once, while the multiple sequence alignment-based methods [9] correct the reads after aligning several similar reads.…”

Section: Related Workmentioning

confidence: 99%

Jabba: Hybrid Error Correction for Long Sequencing Reads Using Maximal Exact Matches

Miclotte

Heydari

Demeester

et al. 2015

Lecture Notes in Computer Science

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Abstract. Third generation sequencing platforms produce longer reads with higher error rates than second generation sequencing technologies. While the improved read length can provide useful information for downstream analysis, underlying algorithms are challenged by the high error rate. Error correction methods in which accurate short reads are used to correct noisy long reads appear to be attractive to generate high-quality long reads. Methods that align short reads to long reads do not optimally use the information contained in the second generation data, and suffer from large runtimes. Recently, a new hybrid error correcting method has been proposed, where the second generation data is first assembled into a de Bruijn graph, on which the long reads are then aligned. In this context we present Jabba, a hybrid method to correct long third generation reads by mapping them on a corrected de Bruijn graph that was constructed from second generation data. Unique to our method is that this mapping is constructed with a seed and extend methodology, using maximal exact matches as seeds. In addition to benchmark results, certain theoretical results concerning the possibilities and limitations of the use of maximal exact matches in the context of third generation reads are presented.

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Section: Related Workmentioning

confidence: 99%

Jabba: Hybrid Error Correction for Long Sequencing Reads Using Maximal Exact Matches

Miclotte

Heydari

Demeester

et al. 2015

Lecture Notes in Computer Science

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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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“…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.…”

Section: Introductionmentioning

confidence: 99%

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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Correcting errors in short reads by multiple alignments

Abstract: The source code of Coral is freely available at http://www.cs.helsinki.fi/u/lmsalmel/coral/.

Cited by 152 publications

References 21 publications

Jabba: Hybrid Error Correction for Long Sequencing Reads Using Maximal Exact Matches

Jabba: Hybrid Error Correction for Long Sequencing Reads Using Maximal Exact Matches

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

RECKONER: read error corrector based on KMC

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