Abstract
Next generation sequencing produces an ever increasing amount of data, requiring increasingly fast computing infrastructures to keep up. We present GNATY, a collection of tools for NGS data analysis, aimed at optimizing parts of the sequence analysis process to reduce the hardware requirements. The tools are developed with efficiency in mind, using multithreading and other techniques to speed up the analysis. The architecture has been verified by implementing a variant caller based on the Varscan 2 variant calling model, achieving a speedup of nearly 18 times. Additionally, the flexibility of the algorithm is also demonstrated by applying it to coverage analysis. Compared to BEDtools 2 the same analysis results were found but in only half the time by GNATY. The speed increase allows for a faster data analysis and more flexibility to analyse the same sample using multiple settings. The software is freely available for non-commercial usage at http://gnaty.phenosystems.com/.
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References
Li, H., Homer, N.: A survey of sequence alignment algorithms for next-generation sequencing. Briefings Bioinform. 11(5), 473–483 (2010)
Wolf, B., et al.: DNAseq workflow in a diagnostic context and an example of a user friendly implementation. BioMed Res. Int. 2015, 11 (2015). Article ID 403497
Li, H., et al.: The sequence alignment/map format and SAMtools. Bioinform. (Oxford, England) 25(16), 2078–2079 (2009)
DePristo, M.A., et al.: A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43(5), 491–498 (2011)
Koboldt, D.C., et al.: Somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22(3), 568–576 (2012)
Warden, C.D., et al.: Detailed comparison of two popular variant calling packages for exome and targeted exon studies. PeerJ 2, 600 (2014)
Quinlan, A.R., et al.: A flexible suite of utilities for comparing genomic features. Bioinformatics 26(6), 841–842 (2010)
Thorvaldsdottir, H., et al.: Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Briefings Bioinform. 14(2), 178–192 (2013)
Highnam, G., et al.: An analytical framework for optimizing variant discovery from personal genomes. Nat. Commun. 6, 16 (2015)
Li, H., et al.: Fast and accurate long-read alignment with burrows-wheeler transform. Bioinform. (Oxford, England) 26(5), 589–595 (2010)
O’Rawe, J., et al.: Low concordance of multiple variant-calling pipelines: practical implications for exome and genome sequencing. Genome Med. 5(3), 28 (2013)
Acknowledgements
The authors thank Phenosystems SA for the opportunity to release part of their software for free.
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© 2016 Springer International Publishing Switzerland
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Wolf, B., Kuonen, P., Dandekar, T. (2016). GNATY: Optimized NGS Variant Calling and Coverage Analysis. In: Ortuño, F., Rojas, I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2016. Lecture Notes in Computer Science(), vol 9656. Springer, Cham. https://doi.org/10.1007/978-3-319-31744-1_40
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DOI: https://doi.org/10.1007/978-3-319-31744-1_40
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