Journal of Applied Genetics

, Volume 57, Issue 1, pp 71–79 | Cite as

Review of alignment and SNP calling algorithms for next-generation sequencing data

  • M. Mielczarek
  • J. Szyda
Animal Genetics • Review


Application of the massive parallel sequencing technology has become one of the most important issues in life sciences. Therefore, it was crucial to develop bioinformatics tools for next-generation sequencing (NGS) data processing. Currently, two of the most significant tasks include alignment to a reference genome and detection of single nucleotide polymorphisms (SNPs). In many types of genomic analyses, great numbers of reads need to be mapped to the reference genome; therefore, selection of the aligner is an essential step in NGS pipelines. Two main algorithms—suffix tries and hash tables—have been introduced for this purpose. Suffix array-based aligners are memory-efficient and work faster than hash-based aligners, but they are less accurate. In contrast, hash table algorithms tend to be slower, but more sensitive. SNP and genotype callers may also be divided into two main different approaches: heuristic and probabilistic methods. A variety of software has been subsequently developed over the past several years. In this paper, we briefly review the current development of NGS data processing algorithms and present the available software.


Alignment Genotype calling NGS SNP calling Review Software 


Compliance with Ethical Standards

This study was conducted without external funding sources and did not involve research in animals.

Conflict of interest

All authors claim no potential conflicts of interest.


  1. Abouelhoda MI, Kurtz S, Ohlebusch E (2004) Replacing suffix trees with enhanced suffix arrays. J Discrete Algorithms 2:53–86CrossRefGoogle Scholar
  2. Alkan C, Kidd JM, Marques-Bonet T, Aksay G, Antonacci F, Hormozdiari F, Kitzman JO, Baker C, Malig M, Mutlu O, Sahinalp SC, Gibbs RA, Eichler EE (2009) Personalized copy number and segmental duplication maps using next-generation sequencing. Nat Genet 41:1061–1067PubMedPubMedCentralCrossRefGoogle Scholar
  3. Altmann A1, Weber P, Bader D, Preuss M, Binder EB, Müller-Myhsok B (2012) A beginners guide to SNP calling from high-throughput DNA-sequencing data. Hum Genet 131(10):1541–54Google Scholar
  4. Ansorge WJ (2009) Next-generation DNA sequencing techniques. N Biotechnol 25:195–203PubMedCrossRefGoogle Scholar
  5. Auffray C, Chen Z, Hood L (2009) Systems medicine: the future of medical genomics and healthcare. Genome Med 1:2PubMedPubMedCentralCrossRefGoogle Scholar
  6. Blanca JM, Pascual L, Ziarsolo P, Nuez F, Cañizares J (2011) ngs_backbone: a pipeline for read cleaning, mapping and SNP calling using next generation sequence. BMC Genomics 12:285PubMedPubMedCentralCrossRefGoogle Scholar
  7. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST, McVean G, Durbin R; 1000 Genomes Project Analysis Group (2011) The variant call format and VCFtools. Bioinformatics 27(15):2156–2158PubMedPubMedCentralCrossRefGoogle Scholar
  8. David M, Dzamba M, Lister D, Ilie L, Brudno M (2011) SHRiMP2: sensitive yet practical SHort Read Mapping. Bioinformatics 27(7):1011–1012PubMedCrossRefGoogle Scholar
  9. Guffanti A, Iacono M, Pelucchi P, Kim N, Soldà G, Croft LJ, Taft RJ, Rizzi E, Askarian-Amiri M, Bonnal RJ, Callari M, Mignone F, Pesole G, Bertalot G, Bernardi LR, Albertini A, Lee C, Mattick JS, Zucchi I, De Bellis G (2009) A transcriptional sketch of a primary human breast cancer by 454 deep sequencing. BMC Genomics 10:163–179PubMedPubMedCentralCrossRefGoogle Scholar
  10. Handel AE, Disanto G, Ramagopalan SV (2013) Next-generation sequencing in understanding complex neurological disease. Expert Rev Neurother 13(2):215–227PubMedCrossRefGoogle Scholar
  11. Hoffmann S, Otto C, Kurtz S, Sharma CM, Khaitovich P, Vogel J, Stadler PF, Hackermüller J (2009) Fast mapping of short sequences with mismatches, insertions and deletions using index structures. PLoS Comput Biol 5(9):e1000502PubMedPubMedCentralCrossRefGoogle Scholar
  12. Homer N, Merriman B, Nelson SF (2009) BFAST: an alignment tool for large scale genome resequencing. PLoS One 4(11):e7767PubMedPubMedCentralCrossRefGoogle Scholar
  13. Horner DS, Pavesi G, Castrignanò T, De Meo PD, Liuni S, Sammeth M, Picardi E, Pesole G (2010) Bioinformatics approaches for genomics and post genomics applications of next-generation sequencing. Brief Bioinform 11:181–197PubMedCrossRefGoogle Scholar
  14. Koboldt DC, Zhang Q, Larson DE, Shen D, McLellan MD, Lin L, Miller CA, Mardis ER, Ding L, Wilson RK (2012) VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res 22(3):568–576PubMedPubMedCentralCrossRefGoogle Scholar
  15. Langmead B, Salzberg S (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357–359PubMedPubMedCentralCrossRefGoogle Scholar
  16. Langmead B, Trapnell C, Pop M, Salzberg SL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10:R25PubMedPubMedCentralCrossRefGoogle Scholar
  17. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25:1754–1760PubMedPubMedCentralCrossRefGoogle Scholar
  18. Li H, Durbin R (2010) Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26(5):589–595PubMedPubMedCentralCrossRefGoogle Scholar
  19. Li H, Homer N (2010) A survey of sequence alignment algorithms for next-generation sequencing. Brief Bioinform 11:473–483PubMedPubMedCentralCrossRefGoogle Scholar
  20. Li H, Ruan J, Durbin R (2008a) Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res 18:1851–1858PubMedPubMedCentralCrossRefGoogle Scholar
  21. Li R, Li Y, Kristiansen K, Wang J (2008b) SOAP: short oligonucleotide alignment program. Bioinformatics 24:713–714PubMedCrossRefGoogle Scholar
  22. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R; 1000 Genome Project Data Processing Subgroup (2009a) The Sequence Alignment/Map format and SAMtools. Bioinformatics 25:2078–2079PubMedPubMedCentralCrossRefGoogle Scholar
  23. Li R, Li Y, Fang X, Yang H, Wang J, Kristiansen K, Wang J (2009b) SNP detection for massively parallel whole-genome resequencing. Genome Res 19(6):1124–1132PubMedPubMedCentralCrossRefGoogle Scholar
  24. Li R, Yu C, Li Y, Lam TW, Yiu SM, Kristiansen K, Wang J (2009c) SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics 25:1966–1967PubMedCrossRefGoogle Scholar
  25. Liu C-M, Wong T, Wu E, Luo R, Yiu S-M, Li Y, Wang B, Yu C, Chu X, Zhao K, Li R, Lam T-W (2012) SOAP3: ultra-fast GPU-based parallel alignment tool for short reads. Bioinformatics 28(6):878–879PubMedCrossRefGoogle Scholar
  26. Luo R, Liu B, Xie Y, Li Z, Huang W, Yuan J, He G, Chen Y, Pan Q, Liu Y, Tang J, Wu G, Zhang H, Shi Y, Liu Y, Yu C, Wang B, Lu Y, Han C, Cheung DW, Yiu SM, Peng S, Xiaoqian Z, Liu G, Liao X, Li Y, Yang H, Wang J, Lam TW, Wang J (2012) SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. Gigascience 1:18PubMedPubMedCentralCrossRefGoogle Scholar
  27. Luo R, Wong T, Zhu J, Liu C-M, Zhu X, Wu E, Lee L-K, Lin H, Zhu W, Cheung DW, Ting H-F, Yiu S-M, Peng S, Yu C, Li Y, Li R, Lam T-W (2013) SOAP3-dp: fast, accurate and sensitive GPU-based short read aligner. PLoS One 8(5):e65632PubMedPubMedCentralCrossRefGoogle Scholar
  28. Ma B, Tromp J, Li M (2002) PatternHunter: faster and more sensitive homology search. Bioinformatics 18:440–445PubMedCrossRefGoogle Scholar
  29. Malhis N, Butterfield YSN, Ester M, Jones SJM (2009) Slider—maximum use of probability information for alignment of short sequence reads and SNP detection. Bioinformatics 25:6–13PubMedPubMedCentralCrossRefGoogle Scholar
  30. Mardis ER (2008) The impact of next-generation sequencing technology on genetics. Trends Genet 24:133–141PubMedCrossRefGoogle Scholar
  31. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA (2010) The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20:1297–1303PubMedPubMedCentralCrossRefGoogle Scholar
  32. Medvedev P, Stanciu M, Brudno M (2009) Computational methods for discovering structural variation with next-generation sequencing. Nat Methods 6:S13–S20PubMedCrossRefGoogle Scholar
  33. Metzker ML (2010) Sequencing technologies—the next generation. Nat Rev Genet 11:31–46PubMedCrossRefGoogle Scholar
  34. Meuwissen T, Goddard M (2010) Accurate prediction of genetic values for complex traits by whole-genome resequencing. Genetics 185:623–631PubMedPubMedCentralCrossRefGoogle Scholar
  35. Needleman SB, Wunsch CD (1970) A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol 48(3):443–453PubMedCrossRefGoogle Scholar
  36. Nielsen R, Paul JS, Albrechtsen A, Song YS (2011) Genotype and SNP calling from next-generation sequencing data. Nat Rev Genet 12(6):443–451PubMedPubMedCentralCrossRefGoogle Scholar
  37. Ning Z, Cox AJ, Mullikin JC (2001) SSAHA: a fast search method for large DNA databases. Genome Res 11(10):1725–1729PubMedPubMedCentralCrossRefGoogle Scholar
  38. Pabinger S, Dander A, Fischer M, Snajder R, Sperk M, Efremova M, Krabichler B, Speicher MR, Zschocke J, Trajanoski Z (2014) A survey of tools for variant analysis of next-generation genome sequencing data. Brief Bioinform 15(2):256–278PubMedPubMedCentralCrossRefGoogle Scholar
  39. Pérez-Enciso M, Ferretti L (2010) Massive parallel sequencing in animal genetics: wherefroms and wheretos. Anim Genet 41(6):561–569PubMedCrossRefGoogle Scholar
  40. Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, Nielsen T, Pons N, Levenez F, Yamada T, Mende DR, Li J, Xu J, Li S, Li D, Cao J, Wang B, Liang H, Zheng H, Xie Y, Tap J, Lepage P, Bertalan M, Batto JM, Hansen T, Le Paslier D, Linneberg A, Nielsen HB, Pelletier E, Renault P, Sicheritz-Ponten T, Turner K, Zhu H, Yu C, Li S, Jian M, Zhou Y, Li Y, Zhang X, Li S, Qin N, Yang H, Wang J, Brunak S, Doré J, Guarner F, Kristiansen K, Pedersen O, Parkhill J, Weissenbach J; MetaHIT Consortium, Bork P, Ehrlich SD, Wang J (2010) A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464:59–65PubMedPubMedCentralCrossRefGoogle Scholar
  41. Ruffalo M, LaFramboise T, Koyutürk M (2011) Comparative analysis of algorithms for next-generation sequencing read alignment. Bioinformatics 27(20):2790–2796PubMedCrossRefGoogle Scholar
  42. Rumble SM, Lacroute P, Dalca AV, Fiume M, Sidow A, Brudno M (2009) SHRiMP: accurate mapping of short color-space reads. PLoS Comput Biol 5:e1000386PubMedPubMedCentralCrossRefGoogle Scholar
  43. Shen Y, Wan Z, Coarfa C, Drabek R, Chen L, Ostrowski EA, Liu Y, Weinstock GM, Wheeler DA, Gibbs RA, Yu F (2010) A SNP discovery method to assess variant allele probability from next-generation resequencing data. Genome Res 20(2):273–280PubMedPubMedCentralCrossRefGoogle Scholar
  44. Smith TF, Waterman MS (1981) Identification of common molecular subsequences. J Mol Biol 147:195–197PubMedCrossRefGoogle Scholar
  45. Smith AD, Xuan Z, Zhang MQ (2008) Using quality scores and longer reads improves accuracy of Solexa read mapping. BMC Bioinformatics 9:128PubMedPubMedCentralCrossRefGoogle Scholar
  46. Smith AD, Chung WY, Hodges E, Kendall J, Hannon G, Hicks J, Xuan Z, Zhang MQ (2009) Updates to the RMAP short-read mapping software. Bioinformatics 25:2841–2842PubMedPubMedCentralCrossRefGoogle Scholar
  47. Sultan M, Schulz MH, Richard H, Magen A, Klingenhoff A, Scherf M, Seifert M, Borodina T, Soldatov A, Parkhomchuk D, Schmidt D, O’Keeffe S, Haas S, Vingron M, Lehrach H, Yaspo ML (2008) A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 321:956–960PubMedCrossRefGoogle Scholar
  48. Taylor KH, Kramer RS, Davis JW, Guo J, Duff DJ, Xu D, Caldwell CW, Shi H (2007) Ultradeep bisulfite sequencing analysis of DNA methylation patterns in multiple gene promoters by 454 sequencing. Cancer Res 67:8511–8518PubMedCrossRefGoogle Scholar
  49. Van Tassell CP, Smith TP, Matukumalli LK, Taylor JF, Schnabel RD, Lawley CT, Haudenschild CD, Moore SS, Warren WC, Sonstegard TS (2008) SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nat Methods 5(3):247–252PubMedCrossRefGoogle Scholar
  50. Wei Z, Wang W, Hu P, Lyon GJ, Hakonarson H (2011) SNVer: a statistical tool for variant calling in analysis of pooled or individual next-generation sequencing data. Nucleic Acids Res 39(19):e132PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Institute of Plant Genetics, Polish Academy of Sciences, Poznan 2015

Authors and Affiliations

  1. 1.Biostatistics Group, Department of GeneticsWroclaw University of Environmental and Life SciencesWroclawPoland

Personalised recommendations