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An ecoinformatics tool for microbial community studies: Supervised classification of amplicon length heterogeneity (ALH) profiles of 16S rRNA

Yang, C. and Mills, D. and Mathee, K. and Wang, Y. and Jayachandran, K. and Sikaroodi, M. and Gillevet, P. and Entry, J. and Narasimhan, N. (2006) An ecoinformatics tool for microbial community studies: Supervised classification of amplicon length heterogeneity (ALH) profiles of 16S rRNA. Journal of Microbiological Methods. 65:49-62.

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Abstract

Support vector machines (SVM) and K-nearest neighbors (KNN) are two computational machine learning tools that
perform supervised classification. This paper presents a novel application of such supervised analytical tools for microbial
community profiling and to distinguish patterning among ecosystems. Amplicon length heterogeneity (ALH) profiles from
several hypervariable regions of 16S rRNA gene of eubacterial communities from Idaho agricultural soil samples and from
Chesapeake Bay marsh sediments were separately analyzed. The profiles from all available hypervariable regions were
concatenated to obtain a combined profile, which was then provided to the SVM and KNN classifiers. Each profile was
labeled with information about the location or time of its sampling. We hypothesized that after a learning phase using
feature vectors from labeled ALH profiles, both these classifiers would have the capacity to predict the labels of previously
unseen samples. The resulting classifiers were able to predict the labels of the Idaho soil samples with high accuracy. The
classifiers were less accurate for the classification of the Chesapeake Bay sediments suggesting greater similarity within the
Bay's microbial community patterns in the sampled sites. The profiles obtained from the VI +V2 region were more
informative than that obtained from any other single region. However, combining them with profiles from the V1 region
(with or without the profiles from the V3 region) resulted in the most accurate classification of the samples. The addition
of profiles from the V9 region appeared to confound the classifiers. Our results show that SVM and KNN classifiers can
be effectively applied to distinguish between eubacterial community patterns from different ecosystems based only on their
ALH profiles.

Item Type: Article
NWISRL Publication Number: 1181
Subjects: Research methodology
Mass Import - autoclassified (may be erroneous)
Depositing User: Dan Stieneke
Date Deposited: 20 Nov 2010 21:49
Last Modified: 01 Nov 2016 15:49
Item ID: 32
URI: https://eprints.nwisrl.ars.usda.gov/id/eprint/32