Journal Article

Population Genomic Inferences from Sparse High-Throughput Sequencing of Two Populations of <i>Drosophila melanogaster</i>

Timothy B. Sackton, Rob J. Kulathinal, Casey M. Bergman, Aaron R. Quinlan, Erik B. Dopman, Mauricio Carneiro, Gabor T. Marth, Daniel L. Hartl and Andrew G. Clark

in Genome Biology and Evolution

Published on behalf of Society for Molecular Biology and Evolution

Volume 1, issue , pages 449-465
Published in print January 2009 |
Published online November 2009 | e-ISSN: 1759-6653 | DOI: http://dx.doi.org/10.1093/gbe/evp048

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Short-read sequencing techniques provide the opportunity to capture genome-wide sequence data in a single experiment. A current challenge is to identify questions that shallow-depth genomic data can address successfully and to develop corresponding analytical methods that are statistically sound. Here, we apply the Roche/454 platform to survey natural variation in strains of Drosophila melanogaster from an African (n = 3) and a North American (n = 6) population. Reads were aligned to the reference D. melanogaster genomic assembly, single nucleotide polymorphisms were identified, and nucleotide variation was quantified genome wide. Simulations and empirical results suggest that nucleotide diversity can be accurately estimated from sparse data with as little as 0.2× coverage per line. The unbiased genomic sampling provided by random short-read sequencing also allows insight into distributions of transposable elements and copy number polymorphisms found within populations and demonstrates that short-read sequencing methods provide an efficient means to quantify variation in genome organization and content. Continued development of methods for statistical inference of shallow-depth genome-wide sequencing data will allow such sparse, partial data sets to become the norm in the emerging field of population genomics.

Keywords: Drosophila; population genomics; next-gen sequencing; transposable elements; copy number polymorphism; nucleotide diversity

Journal Article.  12481 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology ; Evolutionary Biology ; Genetics and Genomics

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