Abstract
Next Generation sequencing is widely used today in several applications such as in studying hereditary diseases or in prenatal genetic testing. Genome Analysis ToolKit (GATK) workflow is currently the best practice flow in use in industry and academia. Variant Calling, the last step in the GATK pipeline, is performed by GATK HaplotypeCaller. It is one of the most time consuming steps in the whole pipeline. In this paper, we investigated the Smith-Waterman implementation of HaplotyeCaller and achieved an up to 40% reduction in Smith-Waterman execution of the HaplotypeCaller by proposing a new optimization where when possible we conclude the Smith-Waterman results by running a simpler linear comparison function. The optimization reduces the HaplotypeCaller run time by up to 10%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
DePristo, M.A., et al.: A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011)
McKenna, A., et al.: The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010)
Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. Mol. Biol. 147, 195–195 (1981)
Li, H., Durbin, R.: Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010)
Li, H., Durbin, R.: Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 5, 1754–1760 (2009)
Roodi, M., Moshovos, A.: Gene sequencing: where time goes. In: 2018 IEEE International Symposium on Workload Characterization (IISWC) (2018)
SMASH Dataset. http://smash.cs.berkeley.edu/datasets.html
Tangherloni, A., Spolaor, S., Rundo, L., et al.: GenHap: a novel computational method based on genetic algorithms for haplotype assembly. BMC Bioinformatics 20, 172 (2019). https://doi.org/10.1186/s12859-019-2691-y
Tangherloni, A., et al.: High performance computing for haplotyping: models and platforms. In: Mencagli, G., et al. (eds.) Euro-Par 2018. LNCS, vol. 11339, pp. 650–661. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-10549-5_51
Bracciali, A., Aldinucci, M., Patterson, M., et al.: PWHATSHAP: efficient haplotyping for future generation sequencing. BMC Bioinformatics 17, 342 (2016). https://doi.org/10.1186/s12859-016-1170-y
Acknowledgements
The authors would like to thank Huawei Canada Research Center for providing the opportunity and facilities for conducting this research. The rights of the technology developed in this work belongs to Huawei Technologies. Patent filing procedure of this research is in progress.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Roodi, M., Moshovos, A. (2020). SW+: On Accelerating Smith-Waterman Execution of GATK HaplotypeCaller. In: Cazzaniga, P., Besozzi, D., Merelli, I., Manzoni, L. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2019. Lecture Notes in Computer Science(), vol 12313. Springer, Cham. https://doi.org/10.1007/978-3-030-63061-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-63061-4_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63060-7
Online ISBN: 978-3-030-63061-4
eBook Packages: Computer ScienceComputer Science (R0)