Quake: quality-aware detection and correction of sequencing errors

By David R. Kelley, Michael C. Schatz, Steven L. Salzberg. Genome Biology, 2010

632 citations recorded by the source corpus.

We introduce Quake, a program to detect and correct errors in DNA sequencing reads. Using a maximum likelihood approach incorporating quality values and nucleotide specific miscall rates, Quake achieves the highest accuracy on realistically simulated reads. We further demonstrate substantial improvements in de novo assembly and SNP detection after using Quake. Quake can be used for any size project, including more than one billion human reads, and is freely available as open source software from http://www.cbcb.umd.edu/software/quake.

Subfields: Cell Biology, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/74ef479b409b4ad1be8ad475f7c841317de9e2cc

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Citations in the corpus

Toward almost closed genomes with GapFiller (2012, methodology): “For this study we used BWA on all datasets.”
FLASH: fast length adjustment of short reads to improve genome assemblies (2011, methodology): “Next, we used FLASH to merge read pairs from the 180bp paired-end sequencing, the second read in each pair is typically of lower quality, which probably explains why FLASH could merge a higher the 180bp fragment library, and as before, we used Quake to correct As Table 3 shows, using FLASH to merge ”
Studying and modelling dynamic biological processes using time-series gene expression data (2012, methodology): “Although researchers still face several problems when using RNA-seq (including higher costs and sequencing read errors 27 ), we expect that over the next few years most time-series expression experiments will migrate to the RNA-seq technology.”
An Extensive Evaluation of Read Trimming Effects on Illumina NGS Data Analysis (2013, methodology): “The first approach aims at correcting reads after superimposing them to each other, whereas low frequency patterns are modified based on the most frequent sequences; this approach usually works at the level of k-mers and is adopted by several preprocessing tools, such as Quake [17] and ALLPATHS-LG [”
GapFiller: a de novo assembly approach to fill the gap within paired reads (2012, methodology): “Our approach, when we are given raw data, is to first trim (and possibly filter) the reads on the ground of quality information using a specific rNA option (refer to [20] for details), and to subsequently correct them with an error correction tool like QUAKE [23].”
BayesHammer: Bayesian clustering for error correction in single-cell sequencing (2012, methodology): “Since BAYESHAMMER trims only bad quality bases and does not, like QUAKE, trim bases that it has not been able to correct (it has been proven detrimental for single-cell assembly in our experiments), it does produce a much larger set of k-mers than Quake on a multi-cell dataset.”
An Integrated Pipeline for de Novo Assembly of Microbial Genomes (2012, methodology): “Choice of the number of clusters K in the EM-clustering algorithm is derived from a preliminary estimation of the library insert size using the method implemented in BWA [31].”
Rcorrector: efficient and accurate error correction for Illumina RNA-seq reads (2015, methodology): “Methods in this category include Quake [5], Hammer [6], Musket [7], Bless [1], BFC [2], and Lighter [3].”
The MaSuRCA genome assembler (2013, methodology): “One may substitute another error corrector, such as Quake (Kelley et al., 2010), as long as the output is processed in such a way that read names are preserved and the mates are reported together.”
Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes (2014, result): “The MGS-augmented assembly of MGS:337 covered 95% of the reference genome of the benchmark species B. animalis subsp. lactis CNCM I-2494 with 99.9% identity ( Fig.”
Next-generation transcriptome assembly (2011, background): “Reads containing errors can either be removed or trimmed to improve assembly quality and decrease the computational memory required 10,16,42 .”
Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species (2013, methodology): “Kelley DR, Schatz MC, Salzberg SL: Quake: quality-aware detection and correction of sequencing errors.”