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| Web URL(s): | https://scisoc.confex.com/scisoc/2018am/meetingapp.cgi/Paper/111655
Last checked: 11/14/2018
Requires: JavaScript |
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| Publication Type:
| Report |
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| Content Type: | Abstract or Summary only |
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| Author(s): | Doherty, Joseph |
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| Author Affiliation: | Plant Science and Landscape Architecture, University of Maryland, College Park, MD |
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| Title: | Emerging microbiome analyses: Amplicon sequence variants or operational taxonomic units |
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| Section: | C05 turfgrass science
Other records with the "C05 turfgrass science" Section
Turf physiology, molecular biology, and genetics poster (Includes student competiton)
Other records with the "Turf physiology, molecular biology, and genetics poster (Includes student competiton)" Section
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| Meeting Info.: | Baltimore, Maryland: November 4-7, 2018 |
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| Source: | ASA, CSSA and SSSA International Annual Meetings. 2018, p. 111655. |
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| Publishing Information: | [Madison, Wisconsin]: [American Society of Agronomy, Crop Science Society of America, and Canadian Society of Agronomy] |
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| # of Pages: | 1 |
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| Keywords: | TIC Keywords: Genetic analysis; Microbiomes
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| Abstract/Contents: | "Microbiome research is growing at a rapid rate, and there is significant interest in developing diagnostic and management tools from the diverse microbial communities present in a host's microbiome. To capture microbiome constituents that are not culturable in the lab, next-generation sequencing (NGS) technologies have become the preferred method for microbiome community analysis. These NGS technologies generate millions of sequences per run, which leads to concerns about how to analyze these massive data sets in a biologically meaningful manner. Traditionally, NGS data has been analyzed through operational taxonomic units (OTUs), which are generated by grouping sequences into clusters based on an arbitrarily set 97% similarity threshold. However, recent advances in bioinformatic processing of NGS data has allowed for a finer resolution analysis of these sequences using amplicon sequence variants (ASVs). Unlike OTUs, ASVs are inferred down to single nucleotide differences between sequences and are not data set dependent like OTU clustering methods are. The same data set of bacterial amplicon sequences was run through both OTU clustering methods, in QIIME 1, and through amplicon sequence variant inference, in DADA2. QIIME analysis resulted in 12,448 OTUs, while sequence variant inference resulted in 8,811 ASVs. The quality of these units also differed, with 4,262 OTUs not identifiable at the kingdom level and only 72 ASVs not identifiable at the same level. Significantly less contaminant sequences (e.g., chloroplast and mitochondria sequences) were present after DADA2 processing compared to QIIME as well. Despite obtaining more OTUs than ASVs, statistical conclusions were similar between the two methods. With the increased quality and cross study comparability of ASVs, they should become preferred method for analyzing next-generation sequencing amplicon studies." |
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| Language: | English |
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| References: | 0 |
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| Note: | This item is an abstract only!
"233"
"Poster Number: 1291" |
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| ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Doherty, J. 2018. Emerging microbiome analyses: Amplicon sequence variants or operational taxonomic units. Agron. Abr. p. 111655. |
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Last checked: 11/14/2018
Requires: JavaScript |
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