Abstract

QualitySNPng is a new software tool for the detection and interactive visualization of single-nucleotide polymorphisms (SNPs). It uses a haplotype-based strategy to identify reliable SNPs; it is optimized for the analysis of current RNA-seq data; but it can also be used on genomic DNA sequences derived from next-generation sequencing experiments. QualitySNPng does not require a sequenced reference genome and delivers reliable SNPs for di- as well as polyploid species. The tool features a user-friendly interface, multiple filtering options to handle typical sequencing errors, support for SAM and ACE files and interactive visualization. QualitySNPng produces high-quality SNP information that can be used directly in genotyping by sequencing approaches for application in QTL and genome-wide association mapping as well as to populate SNP arrays. The software can be used as a stand-alone application with a graphical user interface or as part of a pipeline system like Galaxy. Versions for Windows, Mac OS X and Linux, as well as the source code, are available from http://www.bioinformatics.nl/QualitySNPng.

Highlights

  • Recent developments in sequencing technology have revolutionized genetic research, as vast amounts of sequencing data are becoming available

  • Single-nucleotide polymorphism (SNP) information can be extracted that is useful for genetic analysis, including quantitative trait locus (QTL) mapping and genome-wide association studies [1,2]

  • The identified singlenucleotide polymorphisms (SNPs) can be viewed in the context of predicted haplotypes and per input sample, making it ideally suited for genotyping by sequencing approach [1]

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Summary

Introduction

Recent developments in sequencing technology have revolutionized genetic research, as vast amounts of sequencing data are becoming available. Because QualitySNP can use de novo assembled sequence alignments as input, it can be used for species without a reference genome. We present QualitySNPng that was tuned to identify SNPs in data from the current next-generation sequencing (NGS) platforms.

Results
Conclusion

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