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Population Genomics Analysis with RAD, Reprised: Stacks 2

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Marine Genomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2498))

Abstract

Restriction enzymes have been one of the primary tools in the population genetics toolkit for 50 years, being coupled with each new generation of technology to provide a more detailed view into the genetics of natural populations. Restriction site-Associated DNA protocols, which joined enzymes with short-read sequencing technology, have democratized the field of population genomics, providing a means to assay the underlying alleles in scores of populations. More than 10 years on, the technique has been widely applied across the tree of life and served as the basis for many different analysis techniques. Here, we provide a detailed protocol to conduct a RAD analysis from experimental design to de novo analysis—including parameter optimization—as well as reference-based analysis, all in Stacks version 2, which is designed to work with paired-end reads to assemble RAD loci up to 1000 nucleotides in length. The protocol focuses on major points of friction in the molecular approaches and downstream analysis, with special attention given to validating experimental analyses. Finally, the protocol provides several points of departure for further analysis.

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Author information

Authors and Affiliations

  1. Department of Evolution, Ecology, and Behavior, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Angel G. Rivera-Colón & Julian Catchen

Authors
  1. Angel G. Rivera-Colón
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  2. Julian Catchen
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Corresponding author

Correspondence to Julian Catchen .

Editor information

Editors and Affiliations

  1. Institute of Biosciences and BioResources (IBBR), National Research Council, Naples, Italy

    Cinzia Verde

  2. Institute of Biosciences and BioResources (IBBR), National Research Council, Naples, Italy

    Daniela Giordano

Appendices

Appendix

Working example scripts referenced in this protocol can be found online at BitBucket in a Git repository: https://bitbucket.org/CatchenLab/mimb-stacks2-protocol. We list the available scripts below.

Plot Library Demultiplexing and Processing

The file 01_process_radtags/process_radtags_stats.R uses the log reported by process_radtags and calculates several summary statistics on the proportion of samples in the library, number of total reads, and percentage of total reads kept per-sample. It also generates several plots showing the generated distributions. The input file can be obtained using the following.

stacks-dist-extract process_radtags.raw.log per_barcode_raw_read_counts > per_sample_counts.tsv

De Novo Parameter Optimization Shell Loop

The file 02_param_opt/param_opt_loop.sh loops over the values of 1 through 12 and uses the number in each iteration as the corresponding value for ustacks -M and cstacks -n for the Stacks de novo pipeline.

Gstacks Coverage R Script

The file 03_denovo/gstacks_stats.R uses the log reported by gstacks and calculates several summary statistics on sample coverage and PCR duplicates. It also generates several plots showing the generated distributions. The input file can be obtained using the following.

stacks-dist-extract gstacks.log.distribs effective_coverages_per_sample | grep -v '^#' > effective_coverages_per_sample.tsv

Bwa Alignment Shell Loop

The file 04_refmap/bwa_samples_loop.sh loops over each sample in the provided population map file, and for each sample aligns the paired reads with bwa mem and processed alignments using samtools view/sort. For each iteration of the loop, the sample name present in the popmap is used as the prefix from which the path the input reads and output BAM files are constructed.

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Rivera-Colón, A.G., Catchen, J. (2022). Population Genomics Analysis with RAD, Reprised: Stacks 2. In: Verde, C., Giordano, D. (eds) Marine Genomics. Methods in Molecular Biology, vol 2498. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2313-8_7

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  • DOI: https://doi.org/10.1007/978-1-0716-2313-8_7

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2312-1

  • Online ISBN: 978-1-0716-2313-8

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