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Biology 2017, 6(1), 14; doi:10.3390/biology6010014

MetaGaAP: A Novel Pipeline to Estimate Community Composition and Abundance from Non-Model Sequence Data

School of Earth, Environmental and Biological Sciences, Queensland University of Technology, Brisbane City QLD 4000, Australia
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Author to whom correspondence should be addressed.
Academic Editor: Jukka Finne
Received: 1 December 2016 / Revised: 6 January 2017 / Accepted: 7 February 2017 / Published: 17 February 2017
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Abstract

Next generation sequencing and bioinformatic approaches are increasingly used to quantify microorganisms within populations by analysis of ‘meta-barcode’ data. This approach relies on comparison of amplicon sequences of ‘barcode’ regions from a population with public-domain databases of reference sequences. However, for many organisms relevant ‘barcode’ regions may not have been identified and large databases of reference sequences may not be available. A workflow and software pipeline, ‘MetaGaAP,’ was developed to identify and quantify genotypes through four steps: shotgun sequencing and identification of polymorphisms in a metapopulation to identify custom ‘barcode’ regions of less than 30 polymorphisms within the span of a single ‘read’, amplification and sequencing of the ‘barcode’, generation of a custom database of polymorphisms, and quantitation of the relative abundance of genotypes. The pipeline and workflow were validated in a ‘wild type’ Alphabaculovirus isolate, Helicoverpa armigera single nucleopolyhedrovirus (HaSNPV-AC53) and a tissue-culture derived strain (HaSNPV-AC53-T2). The approach was validated by comparison of polymorphisms in amplicons and shotgun data, and by comparison of predicted dominant and co-dominant genotypes with Sanger sequences. The computational power required to generate and search the database effectively limits the number of polymorphisms that can be included in a barcode to 30 or less. The approach can be used in quantitative analysis of the ecology and pathology of non-model organisms. View Full-Text
Keywords: bioinformatics; baculoviruses; metapopulation; meta-barcoding; MetaGaAP; HaSNPV-AC53; community analysis bioinformatics; baculoviruses; metapopulation; meta-barcoding; MetaGaAP; HaSNPV-AC53; community analysis
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MDPI and ACS Style

Noune, C.; Hauxwell, C. MetaGaAP: A Novel Pipeline to Estimate Community Composition and Abundance from Non-Model Sequence Data. Biology 2017, 6, 14.

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