Defining Loci in Restriction-Based Reduced Representation Genomic Data from Nonmodel Species : Sources of Bias and Diagnostics for Optimal Clustering

Next generation sequencing holds great promise for applications of phylogeography, landscape genetics, and population genomics in wild populations of nonmodel species, but the robustness of inferences hinges on careful experimental design and effective bioinformatic removal of predictable artifacts....

Full description

Saved in:
Bibliographic Details
Published in:BioMed research international 2014-01, Vol.2014 (2014), p.1-9
Main Authors: Ilut, Daniel C., Nydam, Marie L., Hare, Matthew P.
Format: Article
Language:English
Subjects:
Alleles
Animals
Bias
Ciona savignyi
Cluster Analysis
Computational Biology
Computer Simulation
DNA Restriction Enzymes - metabolism
DNA sequencing
Enzymes
Genetic aspects
Genome
Genomes
Genomics
Glycine max - genetics
Heterozygote
Homozygote
Multigene Family
Nucleotide sequencing
Phylogeography
Polymorphism, Single Nucleotide
Polyploidy
Quality control
Sequence Analysis, DNA
Smegmamorpha - genetics
Soybean
Soybeans
Urochordata - genetics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Next generation sequencing holds great promise for applications of phylogeography, landscape genetics, and population genomics in wild populations of nonmodel species, but the robustness of inferences hinges on careful experimental design and effective bioinformatic removal of predictable artifacts. Addressing this issue, we use published genomes from a tunicate, stickleback, and soybean to illustrate the potential for bioinformatic artifacts and introduce a protocol to minimize two sources of error expected from similarity-based de-novo clustering of stacked reads: the splitting of alleles into different clusters, which creates false homozygosity, and the grouping of paralogs into the same cluster, which creates false heterozygosity. We present an empirical application focused on Ciona savignyi, a tunicate with very high SNP heterozygosity (~0.05), because high diversity challenges the computational efficiency of most existing nonmodel pipelines while also potentially exacerbating paralog artifacts. The simulated and empirical data illustrate the advantages of using higher sequence difference clustering thresholds than is typical and demonstrate the utility of our protocol for efficiently identifying an optimum threshold from data without prior knowledge of heterozygosity. The empirical Ciona savignyi data also highlight null alleles as a potentially large source of false homozygosity in restriction-based reduced representation genomic data.
ISSN:2314-6133
2314-6141
DOI:10.1155/2014/675158