swga: a primer design toolkit for selective whole genome amplification

Population genomic analyses are often hindered by difficulties in obtaining sufficient numbers of genomes for analysis by DNA sequencing. Selective whole-genome amplification (SWGA) provides an efficient approach to amplify microbial genomes from complex backgrounds for sequence acquisition. However...

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Veröffentlicht in:Bioinformatics (Oxford, England) Jg. 33; H. 14; S. 2071 - 2077
Hauptverfasser: Clarke, Erik L, Sundararaman, Sesh A, Seifert, Stephanie N, Bushman, Frederic D, Hahn, Beatrice H, Brisson, Dustin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Oxford University Press 15.07.2017
Schlagworte:
Animals
DNA Primers
Drosophila melanogaster - microbiology
Genetics, Population - methods
Genome, Bacterial
Genomics - methods
Humans
Mycobacterium tuberculosis - genetics
Original Papers
Sequence Analysis, DNA - methods
Software
Wolbachia - genetics
ISSN:1367-4803, 1367-4811, 1367-4811
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Zusammenfassung:Population genomic analyses are often hindered by difficulties in obtaining sufficient numbers of genomes for analysis by DNA sequencing. Selective whole-genome amplification (SWGA) provides an efficient approach to amplify microbial genomes from complex backgrounds for sequence acquisition. However, the process of designing sets of primers for this method has many degrees of freedom and would benefit from an automated process to evaluate the vast number of potential primer sets. Here, we present swga , a program that identifies primer sets for SWGA and evaluates them for efficiency and selectivity. We used swga to design and test primer sets for the selective amplification of Wolbachia pipientis genomic DNA from infected Drosophila melanogaster and Mycobacterium tuberculosis from human blood. We identify primer sets that successfully amplify each against their backgrounds and describe a general method for using swga for arbitrary targets. In addition, we describe characteristics of primer sets that correlate with successful amplification, and present guidelines for implementation of SWGA to detect new targets. Source code and documentation are freely available on https://www.github.com/eclarke/swga . The program is implemented in Python and C and licensed under the GNU Public License. ecl@mail.med.upenn.edu. Supplementary data are available at Bioinformatics online.
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Erik L. Clarke and Sesh A. Sundararaman authors contributed equally.
ISSN:1367-4803
1367-4811
1367-4811
DOI:10.1093/bioinformatics/btx118