Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Protocol
  • Published:

Deriving genotypes from RAD-seq short-read data using Stacks

Nature Protocols volume 12, pages 2640–2659 (2017)Cite this article

Subjects

Abstract

Restriction site-associated DNA sequencing (RAD-seq) allows for the genome-wide discovery and genotyping of single-nucleotide polymorphisms in hundreds of individuals at a time in model and nonmodel species alike. However, converting short-read sequencing data into reliable genotype data remains a nontrivial task, especially as RAD-seq is used in systems that have very diverse genomic properties. Here, we present a protocol to analyze RAD-seq data using the Stacks pipeline. This protocol will be of use in areas such as ecology and population genetics. It covers the assessment and demultiplexing of the sequencing data, read mapping, inference of RAD loci, genotype calling, and filtering of the output data, as well as providing two simple examples of downstream biological analyses. We place special emphasis on checking the soundness of the procedure and choosing the main parameters, given the properties of the data. The procedure can be completed in 1 week, but determining definitive methodological choices will typically take up to 1 month.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Genotype calling greatly depends on coverage.
Figure 2: Selection of assembly parameters in a de novo analysis.
Figure 3: Evolution of the catalog as more samples are added.
Figure 4: Transitioning to populations genetics.

Similar content being viewed by others

References

  1. Narum, S.R., Buerkle, C.A., Davey, J.W., Miller, M.R. & Hohenlohe, P.A. Genotyping-by-sequencing in ecological and conservation genomics. Mol. Ecol. 22, 2841–2847 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Andrews, K.R., Good, J.M., Miller, M.R., Luikart, G. & Hohenlohe, P.A. Harnessing the power of RADseq for ecological and evolutionary genomics. Nat. Rev. Genet. 17, 81–92 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Baird, N.A. et al. Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS One 3, e3376 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Elshire, R.J. et al. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One 6, e19379 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Peterson, B.K., Weber, J.N., Kay, E.H., Fisher, H.S. & Hoekstra, H.E. Double digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS One 7, e37135 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Ali, O.A. et al. RAD capture (Rapture): flexible and efficient sequence-based genotyping. Genetics 202, 389–400 (2016).

    Article  CAS  PubMed  Google Scholar 

  7. Toonen, R.J. et al. ezRAD: a simplified method for genomic genotyping in non-model organisms. PeerJ 1, e203 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Franchini, P., Monné Parera, D., Kautt, A.F. & Meyer, A. quaddRAD: a new high-multiplexing and PCR duplicate removal ddRAD protocol produces novel evolutionary insights in a nonradiating cichlid lineage. Mol. Ecol. 26, 2783–2795 (2017).

    Article  CAS  PubMed  Google Scholar 

  9. Suchan, T. et al. Hybridization capture using RAD probes (hyRAD), a new tool for performing genomic analyses on collection specimens. PLoS One 11, e0151651 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Catchen, J.M., Amores, A., Hohenlohe, P., Cresko, W. & Postlethwait, J.H. Stacks: building and genotyping loci de novo from short-read sequences. G3 1, 171–182 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Catchen, J., Hohenlohe, P.A., Bassham, S., Amores, A. & Cresko, W.A. Stacks: an analysis tool set for population genomics. Mol. Ecol. 22, 3124–3140 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Catchen, J. et al. The population structure and recent colonization history of Oregon threespine stickleback determined using restriction-site associated DNA-sequencing. Mol. Ecol. 22, 2864–2883 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Lescak, E.A. et al. Evolution of stickleback in 50 years on earthquake-uplifted islands. Proc. Natl. Acad. Sci. USA 112, E7204–E7212 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Kautt, A.F., Machado-Schiaffino, G. & Meyer, A. Multispecies outcomes of sympatric speciation after admixture with the source population in two radiations of Nicaraguan Crater Lake cichlids. PLoS Genet. 12, e1006157 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Malinsky, M. et al. Genomic islands of speciation separate cichlid ecomorphs in an East African crater lake. Science 350, 1493–1498 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2015).

  17. Nielsen, R., Paul, J.S., Albrechtsen, A. & Song, Y.S. Genotype and SNP calling from next-generation sequencing data. Nat. Rev. Genet. 12, 443–451 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Browning, S.R. & Browning, B.L. Haplotype phasing: existing methods and new developments. Nat. Rev. Genet. 12, 703–714 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Korneliussen, T.S., Albrechtsen, A. & Nielsen, R. ANGSD: analysis of next generation sequencing data. BMC Bioinformatics 15, 356 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Eaton, D.A.R. PyRAD: assembly of de novo RADseq loci for phylogenetic analyses. Bioinforma. Oxf. Engl. 30, 1844–1849 (2014).

    Article  CAS  Google Scholar 

  21. Edgar, R.C. Search and clustering orders of magnitude faster than BLAST. Bioinforma. Oxf. Engl. 26, 2460–2461 (2010).

    Article  CAS  Google Scholar 

  22. Sovic, M.G., Fries, A.C. & Gibbs, H.L. AftrRAD: a pipeline for accurate and efficient de novo assembly of RADseq data. Mol. Ecol. Resour. 15, 1163–1171 (2015).

    Article  CAS  PubMed  Google Scholar 

  23. Huang, W., Umbach, D.M. & Li, L. Accurate anchoring alignment of divergent sequences. Bioinforma. Oxf. Engl. 22, 29–34 (2006).

    Article  CAS  Google Scholar 

  24. Glaubitz, J.C. et al. TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline. PLoS One 9, e90346 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Lu, F. et al. Switchgrass genomic diversity, ploidy, and evolution: novel insights from a network-based SNP discovery protocol. PLoS Genet. 9, e1003215 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Puritz, J.B., Hollenbeck, C.M. & Gold, J.R. dDocent: a RADseq, variant-calling pipeline designed for population genomics of non-model organisms. PeerJ 2, e431 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Chong, Z., Ruan, J. & Wu, C.-I. Rainbow: an integrated tool for efficient clustering and assembling RAD-seq reads. Bioinforma. Oxf. Engl. 28, 2732–2737 (2012).

    Article  CAS  Google Scholar 

  28. Shafer, A.B.A. et al. Bioinformatic processing of RAD-seq data dramatically impacts downstream population genetic inference. Methods Ecol. Evol. http://dx.doi.org/10.1111/2041-210X.12700 (2016).

  29. Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinforma. Oxf. Engl. 27, 2987–2993 (2011).

    Article  CAS  Google Scholar 

  30. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Garrison, E. & Marth, G. Haplotype-based variant detection from short-read sequencing. Preprint at https://arxiv.org/abs/1207.3907 (2012).

  32. Hohenlohe, P.A. et al. Population genomics of parallel adaptation in threespine stickleback using sequenced RAD tags. PLoS Genet. 6, e1000862 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Jombart, T. & Ahmed, I. adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinforma. Oxf. Engl. 27, 3070–3071 (2011).

    Article  CAS  Google Scholar 

  34. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinforma. Oxf. Engl. 25, 1754–1760 (2009).

    Article  CAS  Google Scholar 

  35. Hoffberg, S.L. et al. RADcap: sequence capture of dual-digest RADseq libraries with identifiable duplicates and reduced missing data. Mol. Ecol. Resour. http://dx.doi.org/10.1111/1755-0998.12566 (2016).

  36. Herrera, S., Reyes-Herrera, P.H. & Shank, T.M. Predicting RAD-seq marker numbers across the eukaryotic tree of life. Genome Biol. Evol. http://dx.doi.org/10.1093/gbe/evv210 (2015).

  37. DePristo, M.A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Romiguier, J. et al. Comparative population genomics in animals uncovers the determinants of genetic diversity. Nature 515, 261–263 (2014).

    Article  CAS  PubMed  Google Scholar 

  39. Braasch, I. et al. A new model army: emerging fish models to study the genomics of vertebrate Evo-Devo. J. Exp. Zool. B Mol. Dev. Evol. 324, 316–341 (2015).

    Article  PubMed  Google Scholar 

  40. Lien, S. et al. The Atlantic salmon genome provides insights into rediploidization. Nature 533, 200–205 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Ilut, D.C., Nydam, M.L. & Hare, M.P. Defining loci in restriction-based reduced representation genomic data from nonmodel species: sources of bias and diagnostics for optimal clustering. Biomed. Res. Int. 2014, 675158 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Harvey, M.G. et al. Similarity thresholds used in DNA sequence assembly from short reads can reduce the comparability of population histories across species. PeerJ 3, e895 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Rodríguez-Ezpeleta, N. et al. Population structure of Atlantic mackerel inferred from RAD-seq-derived SNP markers: effects of sequence clustering parameters and hierarchical SNP selection. Mol. Ecol. Resour. 16, 991–1001 (2016).

    Article  PubMed  Google Scholar 

  44. Paris, J.R., Stevens, J.R. & Catchen, J.M. Lost in parameter space: a road map for stacks. Methods Ecol. Evol. 8, 1360–1373 (2017).

    Article  Google Scholar 

  45. Weir, B.S. Genetic Data Analysis II: Methods for Discrete Population Genetic Data (Sinauer Associates, 1996).

  46. Excoffier, L., Smouse, P.E. & Quattro, J.M. Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131, 479–491 (1992).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Meirmans, P.G. Using the AMOVA framework to estimate a standardized genetic differentiation measure. Evol. Int. J. Org. Evol. 60, 2399–2402 (2006).

    Article  Google Scholar 

  48. Bird, C.E., Karl, S.A., Mouse, P.E. & Toonen, R.J. in Phylogeography and Population Genetics in Crustacea 31–55 (CRC Press, 2011).

Download references

Acknowledgements

We thank J. Paris and N. Rayamajhi for their help in testing the procedure and for discussion of the manuscript.

Author information

Authors and Affiliations

  1. Department of Animal Biology, University of Illinois at Urbana–Champaign, Urbana, Illinois, USA

    Nicolas C Rochette & Julian M Catchen

Authors
  1. Nicolas C Rochette
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Julian M Catchen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

N.C.R. and J.M.C. designed the protocol, performed experiments, and wrote the manuscript.

Corresponding author

Correspondence to Julian M Catchen.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Observed per-sample coverages.

The median number of reads for the 78 samples of the demonstration dataset is 1,209,782. The last two samples, ‘sj_1483.05’ and ‘sj_181931’, have almost no reads, likely because of a mistake at the bench at the multiplexing step that caused them not to be represented in the DNA library.

Supplementary Figure 2 Comparison between the reference-based and de novo approaches.

The two approaches yield very similar overall results, yet differ in their treatment of specific subsets of the data.

(A) Number of loci (solid lines) and polymorphic loci (dashed lines) shared by 80% of samples in the reference-based (blue) and de novo (black, same numbers as in Figure 2) approaches, for 12 representative samples. The reference-based analysis yields 35,792 loci shared by 80% samples while the de novo one yields (with M=n=4) 35,277 such loci. Furthermore, mapping the consensus sequences of these de novo loci to the reference genome using BWA (not shown) shows that just 32,843 of them (93.1%) have a one-to-one relationship with loci in the reference-based analysis, while 96 (0.3%) appear under-merged (pairs of loci map to the same genomic location) and 1,748 (5.0%) can’t be mapped to the reference. The remaining 590 de novo loci (1.7%) correspond to in-between cases in which the de novo loci partly exist in the reference-based analysis but are not part of the filtered set of loci present in 80% of samples. Conversely, 2,903 of the filtered loci of the reference-based analysis are missing from the filtered de novo set.

(B) Distribution of the number of SNPs per locus for the reference-based (blue) and de novo (yellow-red, same numbers as in Figure 2) approaches. We note that the total number of SNPs is higher in the reference-based analysis than in the de novo analysis with M=n=4 (57,872 vs. 53,051), but that the rate of SNPs with implausibly high heterozygosities (>60%) is also slightly higher (2.0% vs. 1.8%).

(C) PCA of 76 individuals, computed in the same way as figure 4B, but using the genotypes resulting from the reference-based analysis. For comparability with Figure 4B, the Y-axis is PC3, not PC2. Insert: Percentage of the variance explained by the first ten components.

Supplementary information

Supplementary Figures

Supplementary Figures 1 and 2. (PDF 376 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rochette, N., Catchen, J. Deriving genotypes from RAD-seq short-read data using Stacks. Nat Protoc 12, 2640–2659 (2017). https://doi.org/10.1038/nprot.2017.123

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nprot.2017.123

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics