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Optimizing Variant Calling for Human Genome Analysis: A Comprehensive Pipeline Approach

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Bioinformatics and Biomedical Engineering (IWBBIO 2023)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13920))

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Abstract

The identification of genetic variations in large cohorts is a critical issue to identify patient cohorts, disease risks, and to develop more effective treatments. To help this analysis, we improved a variant calling pipeline for the human genome using state-of-the-art tools, including GATK (Hard Filter/VQSR) and DeepVariant. The pipeline was tested in a computing cluster where it was possible to compare Illumina Platinum genomes using different approaches. Moreover, by using a secure data space we provide a solution to privacy and security concerns in genomics research. Overall, this variant calling pipeline has the potential to advance the field of genomics research significantly, improve healthcare outcomes, and simplify the analysis process. Therefore, it is critical to rigorously evaluate these pipelines’ performance before implementing them in clinical settings.

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Notes

  1. 1.

    ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/001/405/GCA_000001405.15_GRCh38/seqs_for_alignment_pipelines.ucsc_ids/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna.gz.

  2. 2.

    https://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/release/.

References

  1. US DOE Joint Genome Institute: Hawkins Trevor 4 Branscomb Elbert 4 Predki Paul 4 Richardson Paul 4 Wenning Sarah 4 Slezak Tom 4 Doggett Norman 4 Cheng Jan-Fang 4 Olsen Anne 4 Lucas Susan 4 Elkin Christopher 4 Uberbacher Edward 4 Frazier Marvin 4, RIKEN Genomic Sciences Center: Sakaki Yoshiyuki 9 Fujiyama Asao 9 Hattori Masahira 9 Yada Tetsushi 9 Toyoda Atsushi 9 Itoh Takehiko 9 Kawagoe Chiharu 9 Watanabe Hidemi 9 Totoki Yasushi 9 Taylor Todd 9, Genoscope, CNRS UMR-8030: Weissenbach Jean 10 Heilig Roland 10 Saurin William 10 Artiguenave Francois 10 Brottier Philippe 10 Bruls Thomas 10 Pelletier Eric 10 Robert Catherine 10 Wincker Patrick 10, Institute of Molecular Biotechnology: Rosenthal André 12 Platzer Matthias 12 Nyakatura Gerald 12 Taudien Stefan 12 Rump Andreas 12 Department of Genome Analysis, GTC Sequencing Center: Smith Douglas R. 11 Doucette-Stamm Lynn 11 Rubenfield Marc 11 Weinstock Keith 11 Lee Hong Mei 11 Dubois JoAnn 11, Beijing Genomics Institute/Human Genome Center: Yang Huanming 13 Yu Jun 13 Wang Jian 13 Huang Guyang 14 Gu Jun 15, et al. Initial sequencing and analysis of the human genome. nature, 409(6822):860–921, 2001

    Google Scholar 

  2. Sims, D., Sudbery, I., Ilott, N.E., Heger, A., Ponting, C.P.: Sequencing depth and coverage: key considerations in genomic analyses. Nat. Rev. Genet. 15(2), 121–132 (2014)

    Article  CAS  PubMed  Google Scholar 

  3. Kaye, J., Heeney, C., Hawkins, N., De Vries, J., Boddington, P.: Data sharing in genomics-re-shaping scientific practice. Nat. Rev. Genet. 10(5), 331–335 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Papageorgiou, L., Eleni, P., Raftopoulou, S., Mantaiou, M., Megalooikonomou, V., Vlachakis, D.: Genomic big data hitting the storage bottleneck. EMBnet J. 24 (2018)

    Google Scholar 

  5. Maojo, V., Martin-Sanchez, F., Kulikowski, C., Rodriguez-Paton, A., Fritts, M.: Nanoinformatics and DNA-based computing: catalyzing nanomedicine. Pediatr. Res. 67(5), 481–489 (2010)

    Article  CAS  PubMed  Google Scholar 

  6. European Genomic Data Infrastructure (GDI). https://gdi.onemilliongenomes.eu. Accessed 24 Feb 2023

  7. Beyond 1 Million Genomes. https://b1mg-project.eu. Accessed 24 Feb 2023

  8. Danecek, P., et al.: The variant call format and VCFtools. Bioinformatics 27(15), 2156–2158 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Martin, M., et al.: WhatsHAP: fast and accurate read-based phasing. BioRxiv, 085050 (2016)

    Google Scholar 

  10. Alipanahi, B., Delong, A., Weirauch, M.T., Frey, B.J.: Predicting the sequence specificities of DNA- and RNA- binding proteins by deep learning. Nat. Biotechnol. 33(8), 831–838 (2015)

    Article  CAS  PubMed  Google Scholar 

  11. Zou, J., Huss, M., Abid, A., Mohammadi, P., Torkamani, A., Telenti, A.: A primer on deep learning in genomics. Nat. Genet. 51(1), 12–18 (2019)

    Article  CAS  PubMed  Google Scholar 

  12. Johannes, K.: DNA-SEQ-GATK-variant-calling (2021). https://github.com/snakemake-workflows/dna-seq-gatk-variant-calling

  13. Van der Auwera, G.A., et al.: From fastQ data to high-confidence variant calls: the genome analysis toolkit best practices pipeline. Curr. Protoc. Bioinform. 43(1), 11–10 (2013)

    Google Scholar 

  14. O’Rawe, J., et al.: Low concordance of multiple variant-calling pipelines: practical implications for exome and genome sequencing. Genome Med. 5(3), 1–18 (2013)

    Google Scholar 

  15. McKenna, A., et al.: The genome analysis toolkit: a mapreduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20(9), 1297–1303 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Poplin, R., et al.: A universal SNP and small-indel variant caller using deep neural networks. Nat. Biotech. 36(10), 983–987 (2018)

    Article  CAS  Google Scholar 

  17. Olson, N.D., et al.: PrecisionFDA truth challenge v2: calling variants from short and long reads in difficult-to-map regions. Cell Genomics 2(5), 100129 (2022)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Li, H., et al.: The sequence alignment/map format and samtools. Bioinformatics 25(16), 2078–2079 (2009)

    Article  PubMed  PubMed Central  Google Scholar 

  19. Garrison, E., Marth, G.: Haplotype-based variant detection from short-read sequencing. arXiv preprint (2012). arXiv:1207.3907

  20. Koboldt, D.C., et al.: VarScan: variant detection in massively parallel sequencing of individual and pooled samples. Bioinformatics 25(17), 2283–2285 (2009)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Sadedin, S.P., Pope, B., Oshlack, A.: Bpipe: a tool for running and managing bioinformatics pipelines. Bioinformatics 28(11), 1525–1526 (2012)

    Article  CAS  PubMed  Google Scholar 

  22. Seven Bridges. Publications and case studies (2021)

    Google Scholar 

  23. Schneider, V.A., et al.: Evaluation of GRCh38 and de novo haploid genome assemblies demonstrates the enduring quality of the reference assembly. Genome Res. 27(5), 849–864 (2017)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Church, D.M., et al.: Modernizing reference genome assemblies. PLoS Biol. 9(7), e1001091 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Chaisson, M.J., et al.: Resolving the complexity of the human genome using single-molecule sequencing. Nature 517(7536), 608–611 (2015)

    Article  CAS  PubMed  Google Scholar 

  26. Sedlazeck, F.J., et al.: Accurate detection of complex structural variations using single-molecule sequencing. Nat. Methods 15(6), 461–468 (2018)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Eberle, M.A., et al.: A reference data set of 5.4 million phased human variants validated by genetic inheritance from sequencing a three-generation 17-member pedigree. Genome Res. 27(1), 157–164 (2017)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. 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)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Bolger, A.M., Lohse, M., Usadel, B.: Trimmomatic: a flexible trimmer for illumina sequence data. Bioinformatics 30(15), 2114–2120 (2014)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Andrews, S., et al.: FastQC: a quality control tool for high throughput sequence data (2010)

    Google Scholar 

  31. Broad Institute. Picard Toolkit. http://broadinstitute.github.io/picard/. Accessed 24 Feb 2023

  32. Ewels, P., Magnusson, M., Lundin, S., Käller, M.: MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32(19), 3047–3048 (2016)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Miller, R.R., Zhang, L., Chen, L.: Next-generation sequencing bioinformatics pipelines. Clin. Lab. News: A Publication of AACC, 46(3), 12–15 (2020)

    Google Scholar 

  34. Krusche, P., et al.: Best practices for benchmarking germline small-variant calls in human genomes. Nat. Biotechnol. 37(5), 555–560 (2019)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Barbitoff, Y.A., Abasov, R., Tvorogova, V.E., Glotov, A.S., Predeus, A.V.: Systematic benchmark of state-of-the-art variant calling pipelines identifies major factors affecting accuracy of coding sequence variant discovery. BMC Genomics 23(1), 155 (2022)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Lin, Y.-L., et al.: Comparison of GATK and DeepVariant by trio sequencing. Sci. Rep. 12(1), 1809 (2022)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work has received funding from the EC under grant agreement 101081813, Genomic Data Infrastructure.

Author information

Authors and Affiliations

  1. iBiMED, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal

    Miguel Pinheiro

  2. IEETA, DETI, LASI, University of Aveiro, Aveiro, Portugal

    Jorge Miguel Silva & José Luis Oliveira

Authors
  1. Miguel Pinheiro
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  2. Jorge Miguel Silva
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  3. José Luis Oliveira
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Corresponding author

Correspondence to Jorge Miguel Silva .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Granada, Granada, Spain

    Olga Valenzuela

  3. University of Granada, Granada, Spain

    Fernando Rojas Ruiz

  4. University of Granada, Granada, Spain

    Luis Javier Herrera

  5. University of Granada, Granada, Spain

    Francisco Ortuño

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Reproducibility

The repository for the replication of the results described in this paper are available at https://github.com/bioinformatics-ua/GDI_Pipeline.git.

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Pinheiro, M., Silva, J.M., Oliveira, J.L. (2023). Optimizing Variant Calling for Human Genome Analysis: A Comprehensive Pipeline Approach. In: Rojas, I., Valenzuela, O., Rojas Ruiz, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2023. Lecture Notes in Computer Science(), vol 13920. Springer, Cham. https://doi.org/10.1007/978-3-031-34960-7_6

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  • DOI: https://doi.org/10.1007/978-3-031-34960-7_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34959-1

  • Online ISBN: 978-3-031-34960-7

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