Quality scores and SNP detection in sequencing-by-synthesis systems.

By W. Brockman, P. Alvarez, Sarah K. Young, Manuel Garber, G. Giannoukos, William L Lee, C. Russ, Eric S. Lander, Chad Nusbaum, David B. Jaffe. Genome Research, 2008

296 citations recorded by the source corpus.

Promising new sequencing technologies, based on sequencing-by-synthesis (SBS), are starting to deliver large amounts of DNA sequence at very low cost. Polymorphism detection is a key application. We describe general methods for improved quality scores and accurate automated polymorphism detection, and apply them to data from the Roche (454) Genome Sequencer 20. We assess our methods using known-truth data sets, which is critical to the validity of the assessments. We developed informative, base-by-base error predictors for this sequencer and used a variant of the phred binning algorithm to combine them into a single empirically derived quality score. These quality scores are more useful than those produced by the system software: They both better predict actual error rates and identify many more high-quality bases. We developed a SNP detection method, with variants for low coverage, high coverage, and PCR amplicon applications, and evaluated it on known-truth data sets. We demonstrate good specificity in single reads, and excellent specificity (no false positives in 215 kb of genome) in high-coverage data.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/39d767d0523fe3f97a355af7ee44992612fd1024

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In Molecular Biology

Citations in the corpus

VarScan: variant detection in massively parallel sequencing of individual and pooled samples (2009, background): “The extraordinary read depth from massively parallel targeted resequencing makes it feasible to detect rare variants and accurately estimate allele frequencies in pools containing many samples (Brockman et al., 2008).”
Genotype and SNP calling from next-generation sequencing data (2011, background): “The raw Phred-scaled quality scores produced by base-calling algorithms may not accurately reflect the true base-calling error rate”
Ironing out the wrinkles in the rare biosphere through improved OTU clustering (2010, background): “Ironing out the wrinkles in the rare biosphere through improved OTU clusteringemi_2193 1889..1898”
Wrinkles in the rare biosphere: pyrosequencing errors can lead to artificial inflation of diversity estimates. (2009, methodology): “Recent improvements in error estimation of pyrosequence data (Brockman et al., 2008) allow the use of trimming programs, such as LUCY (Chou and Holmes, 2001), that are based on the per-nucleotide quality score.”
ViVaMBC: estimating viral sequence variation in complex populations from illumina deep-sequencing data using model-based clustering (2015, result): “Modern massive parallel sequencing (MPS) technologies, on the contrary, have the opportunity to allow in-depth characterization of sequence variation in more complex populations, including low-frequency viral strains.”
Applications of next-generation sequencing technologies in functional genomics. (2008, background): “This is currently not the case for the nextgeneration technologies ([2], but see [45]).”
Next-generation sequencing: from basic research to diagnostics. (2009, background): “Study of these error traits has led to examples of software that require no additional base calling but that improve quality-score accuracies and thus improve sequencing accuracy ( 65 , 66).”
High-resolution mapping of copy-number alterations with massively parallel sequencing (2008, background): “We anticipate that each massively parallel sequencing platform may be susceptible to particular biase”