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Split-Read Indel and Structural Variant Calling Using PINDEL

  • Kai YeEmail author
  • Li Guo
  • Xiaofei Yang
  • Eric-Wubbo Lamijer
  • Keiran Raine
  • Zemin Ning
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1833)

Abstract

Genetic variations are important evolutionary forces in all forms of life in nature. Accurate and efficient detection of various forms of genetic variants is crucial for understanding cell function, evolution and diseases in living organisms. In this chapter, we describe a detailed protocol that uses Pindel, a split-read algorithm, to discover indels and structural variants in a given genome, from Illumina short-read sequencing data produced from biological samples.

Key words

PINDEL Split-read Structural variants 

Notes

Acknowledgment

The authors gratefully acknowledge the financial support from General Program of National Natural Science Foundation of China (No. 31671372, 61702406).

References

  1. 1.
    Freeman JL, Perry GH, Feuk L et al (2006) Copy number variation: new insights in genome diversity. Genome Res 16:949–961CrossRefPubMedGoogle Scholar
  2. 2.
    Mills RE, Walter K, Stewart C et al (2011) Mapping copy number variation by population-scale genome sequencing. Nature 470:59–65CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Sudmant PH, Rausch T, Gardner EJ et al (2015) An integrated map of structural variation in 2,504 human genomes. Nature 526:75–81CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Zarrei M, MacDonald JR, Merico D et al (2015) A copy number variation map of the human genome. Nat Rev Genet 16:172–183CrossRefPubMedGoogle Scholar
  5. 5.
    Koboldt DC, Steinberg KM, Larson DE et al (2013) The next-generation sequencing revolution and its impact on genomics. Cell 155:27–38CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Lin K, Smit S, Bonnema G et al (2015) Making the difference: integrating structural variation detection tools. Brief Bioinform 16:852–864CrossRefPubMedGoogle Scholar
  7. 7.
    Tattini L, D’Aurizio R, Magi A (2015) Detection of genomic structural variants from next-generation sequencing data. Front Bioeng Biotechnol 3:92CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Guan P, Sung WK (2016) Structural variation detection using next-generation sequencing data: a comparative technical review. Methods 102:36–49CrossRefPubMedGoogle Scholar
  9. 9.
    Layer RM, Chiang C, Quinlan AR et al (2014) LUMPY: a probabilistic framework for structural variant discovery. Genome Biol 15:R84CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Rausch T, Zichner T, Schlattl A et al (2012) DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics 28:i333–i339CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Ye K, Schulz MH, Long Q, Apweiler R, Ning Z (2009) Pindel: a pattern growth approach to detect break points of large deletions and zshort reads. Bioinformatics 25:2865–2871Google Scholar
  12. 12.
    Li H, Handsaker B, Wysoker A et al (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Danecek P, Auton A, Abecasis G et al (2011) The variant call format and VCFtools. Bioinformatics 27:2156–2158CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Kai Ye
    • 1
    • 2
    • 3
    • 4
    Email author
  • Li Guo
    • 1
  • Xiaofei Yang
    • 1
  • Eric-Wubbo Lamijer
    • 1
  • Keiran Raine
    • 5
  • Zemin Ning
    • 5
  1. 1.MOE Key Lab for Intelligent Networks & Network SecurityXi’an Jiaotong UniversityXi’anChina
  2. 2.School of Electronics and Information EngineeringXi’an Jiaotong UniversityXi’anChina
  3. 3.The First Affiliated Hospital of Xi’an Jiaotong UniversityXi’anChina
  4. 4.Collaborative Innovation Center for Genetics and Development, School of Life SciencesFudan UniversityShanghaiChina
  5. 5.Wellcome Trust Sanger InstituteHinxtonUK

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