Defining Loci in Restriction-Based Reduced Representation Genomic Data from Nonmodel Species : Sources of Bias and Diagnostics for Optimal Clustering
Joint Authors
Nydam, Marie L.
Hare, Matthew P.
Ilut, Daniel C.
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-06-25
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Ilut, Daniel C.& Nydam, Marie L.& Hare, Matthew P.. 2014. Defining Loci in Restriction-Based Reduced Representation Genomic Data from Nonmodel Species : Sources of Bias and Diagnostics for Optimal Clustering. BioMed Research International،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-489567
Modern Language Association (MLA)
Ilut, Daniel C.…[et al.]. Defining Loci in Restriction-Based Reduced Representation Genomic Data from Nonmodel Species : Sources of Bias and Diagnostics for Optimal Clustering. BioMed Research International No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-489567
American Medical Association (AMA)
Ilut, Daniel C.& Nydam, Marie L.& Hare, Matthew P.. Defining Loci in Restriction-Based Reduced Representation Genomic Data from Nonmodel Species : Sources of Bias and Diagnostics for Optimal Clustering. BioMed Research International. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-489567
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-489567