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BMC Bioinformatics

, 16:382

Sequence analysis methods

Abstract

BackgroundSingle Nucleotide Polymorphisms SNPs are widely used molecular markers, and their use has increased massively since the inception of Next Generation Sequencing NGS technologies, which allow detection of large numbers of SNPs at low cost. However, both NGS data and their analysis are error-prone, which can lead to the generation of false positive FP SNPs. We explored the relationship between FP SNPs and seven factors involved in mapping-based variant calling — quality of the reference sequence, read length, choice of mapper and variant caller, mapping stringency and filtering of SNPs by read mapping quality and read depth. This resulted in 576 possible factor level combinations. We used error- and variant-free simulated reads to ensure that every SNP found was indeed a false positive.

ResultsThe variation in the number of FP SNPs generated ranged from 0 to 36,621 for the 120 million base pairs Mbp genome. All of the experimental factors tested had statistically significant effects on the number of FP SNPs generated and there was a considerable amount of interaction between the different factors. Using a fragmented reference sequence led to a dramatic increase in the number of FP SNPs generated, as did relaxed read mapping and a lack of SNP filtering. The choice of reference assembler, mapper and variant caller also significantly affected the outcome. The effect of read length was more complex and suggests a possible interaction between mapping specificity and the potential for contributing more false positives as read length increases.

ConclusionsThe choice of tools and parameters involved in variant calling can have a dramatic effect on the number of FP SNPs produced, with particularly poor combinations of software and-or parameter settings yielding tens of thousands in this experiment. Between-factor interactions make simple recommendations difficult for a SNP discovery pipeline but the quality of the reference sequence is clearly of paramount importance. Our findings are also a stark reminder that it can be unwise to use the relaxed mismatch settings provided as defaults by some read mappers when reads are being mapped to a relatively unfinished reference sequence from e.g. a non-model organism in its early stages of genomic exploration.

KeywordsFalse positive SNP NGS Read mismapping Misassembly Mapping stringency Read length AbbreviationsANOVAAnalysis of Variance

APIApplication Programming Interface

bpbase pairs

CDSCoding sequence

ChrChromosome

CPUCentral Processing Unit

FPFalse positive

Java SEJava Standard Edition

MAPQMapping quality

Mbpmillion base pairs

NGSNext-generation sequencing

SAMSequence Alignment-Map file format

Sedstandard error of the difference

SNPSingle-nucleotide polymorphism

VCFVariant Call Format

d.f.degrees of freedom

s.s.sum of squares

m.s.mean square

v.r.variance ratio

F prob.F probability

perm prob.permutation probability

Percentage SSpercentage of sum of squares

Electronic supplementary materialThe online version of this article doi:10.1186-s12859-015-0801-z contains supplementary material, which is available to authorized users.

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Author: Antonio Ribeiro - Agnieszka Golicz - Christine Anne Hackett - Iain Milne - Gordon Stephen - David Marshall - Andrew J. Fl

Source: https://link.springer.com/



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