Optimizing illumina next-generation sequencing library preparation for extremely at-biased genomes

Oyola, S. O. et al. (2012) Optimizing illumina next-generation sequencing library preparation for extremely at-biased genomes. BMC Genomics, 13(1), (doi: 10.1186/1471-2164-13-1) (PMID:22214261) (PMCID:PMC3312816)

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Massively parallel sequencing technology is revolutionizing approaches to genomic and genetic research. Since its advent, the scale and efficiency of Next-Generation Sequencing (NGS) has rapidly improved. In spite of this success, sequencing genomes or genomic regions with extremely biased base composition is still a great challenge to the currently available NGS platforms. The genomes of some important pathogenic organisms like Plasmodium falciparum (high AT content) and Mycobacterium tuberculosis (high GC content) display extremes of base composition. The standard library preparation procedures that employ PCR amplification have been shown to cause uneven read coverage particularly across AT and GC rich regions, leading to problems in genome assembly and variation analyses. Alternative library-preparation approaches that omit PCR amplification require large quantities of starting material and hence are not suitable for small amounts of DNA/RNA such as those from clinical isolates. We have developed and optimized library-preparation procedures suitable for low quantity starting material and tolerant to extremely high AT content sequences.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Otto, Dr Thomas
Authors: Oyola, S. O., Otto, T. D., Gu, Y., Maslen, G., Manske, M., Campino, S., Turner, D. J., MacInnis, B., Kwiatkowski, D. P., Swerdlow, H. P., and Quail, M. A.
College/School:College of Medical Veterinary and Life Sciences > Institute of Infection Immunity and Inflammation
Journal Name:BMC Genomics
Publisher:BioMed Central
ISSN (Online):1471-2164
Copyright Holders:Copyright ©2012 The Authors
First Published:First published in BMC Genomics 13(1)
Publisher Policy:Reproduced under a Creative Commons License

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