RACER: Rapid and accurate correction of errors in reads

Ilie, Lucian; Molnar, Michael

doi:10.1093/bioinformatics/btt407

Cited by 72 publications

(81 citation statements)

References 24 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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“…We compared the performance of Scrible against the state-of-the-art errorcorrection method Racer [5]. The authors of Racer performed extensive exper-imental evaluations and determined that Racer is superior in all aspects (performance, space, and execution time) to HiTec, Shrec, Reptile, Quake, and Coral.…”

Section: Results On Synthetic Reads For the Rice Genomementioning

confidence: 99%

“…The authors of Racer performed extensive exper-imental evaluations and determined that Racer is superior in all aspects (performance, space, and execution time) to HiTec, Shrec, Reptile, Quake, and Coral. We also compare Scrible against Sga [14], as it was not evaluated in [5] but we had evidence it performs well. For both tools we used their default parameter setting.…”

Section: Results On Synthetic Reads For the Rice Genomementioning

confidence: 99%

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Scrible: Ultra-Accurate Error-Correction of Pooled Sequenced Reads

Duma

Cordero

Beccuti

et al. 2015

Lecture Notes in Computer Science

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Abstract.We recently proposed a novel clone-by-clone protocol for de novo genome sequencing that leverages combinatorial pooling design to overcome the limitations of DNA barcoding when multiplexing a large number of samples on second-generation sequencing instruments. Here we address the problem of correcting the short reads obtained from our sequencing protocol. We introduce a novel algorithm called Scrible that exploits properties of the pooling design to accurately identify/correct sequencing errors and minimize the chance of "over-correcting". Experimental results on synthetic data on the rice genome demonstrate that our method has much higher accuracy in correcting short reads compared to state-of-the-art error-correcting methods. On real data on the barley genome we show that Scrible significantly improves the decoding accuracy of short reads to individual BACs.

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Generating CRISPR‐Cas9‐Mediated Null Mutations and Screening Targeting Efficiency in Human Pluripotent Stem Cells

et al. 2021

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CRISPR‐Cas9 mutagenesis facilitates the investigation of gene function in a number of developmental and cellular contexts. Human pluripotent stem cells (hPSCs), either embryonic or induced, are a tractable cellular model to investigate molecular mechanisms involved in early human development and cell fate decisions. hPSCs also have broad potential in regenerative medicine to model, investigate, and ameliorate diseases. Here, we provide an optimized protocol for efficient CRISPR‐Cas9 genome editing of hPSCs to investigate the functional role of genes by engineering null mutations. We emphasize the importance of screening single guide RNAs (sgRNAs) to identify those with high targeting efficiency for generation of clonally derived null mutant hPSC lines. We provide important considerations for targeting genes that may have a role in hPSC maintenance. We also present methods to evaluate the on‐target mutation spectrum and unintended karyotypic changes. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Selecting and ligating sgRNAs into expression plasmids Basic Protocol 2: Validation of sgRNA via in vitro transcription and cleavage assay Basic Protocol 3: Nucleofection of primed human embryonic stem cells Basic Protocol 4: MiSeq analysis of indel mutations Basic Protocol 5: Single cell cloning of targeted hPSCs Basic Protocol 6: Karyotyping of targeted hPSCs

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RACER: Rapid and accurate correction of errors in reads

Abstract: RACER is freely available for non-commercial use at www.csd.uwo.ca/∼ilie/RACER/.

Cited by 72 publications

References 24 publications

RECKONER: read error corrector based on KMC

RECKONER: read error corrector based on KMC

Scrible: Ultra-Accurate Error-Correction of Pooled Sequenced Reads

Generating CRISPR‐Cas9‐Mediated Null Mutations and Screening Targeting Efficiency in Human Pluripotent Stem Cells

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