NDLI: Neural network-based taxonomic clustering for metagenomics

Content Provider	IEEE Xplore Digital Library
Author	Essinger, S.D. Polikar, R. Rosen, G.L.
Copyright Year	2010
Description	Author affiliation: Department of Electrical and Computer Engineering, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104, USA (Rosen, G.L.) \|\| Department of Electrical and Computer Engineering, Rowan University, 201 Mullica Hill Road, Glassboro NJ 08028, USA (Polikar, R.) \|\| Department of Electrical and Computer Engineering, Drexel University, 3141 Chestnut Street, Philadelphia PA 19104, USA (Essinger, S.D.)
Abstract	Metagenomic studies inherently involve sampling genetic information from an environment potentially containing thousands of distinctly different microbial organisms. This genetic information is sequenced producing many short fragments (<500 base pair (bp)); each is tentatively a small representative of the DNA coding structure. Any of the fragments may belong to any of the organisms in the sample, but the relationship is unknown a priori. Furthermore, most of these organisms have not been identified and correspondingly are not represented in any of the publicly available search databases. Our goal is to be able to predict the taxonomic classification of an organism based on the fragments obtained from an environmental sample that may include many (some previously unidentified) organisms. To elucidate the diversity and composition of the sample, we first use a supervised naïve Bayes classifier to score the fragments of known genomes, followed by an unsupervised clustering to group fragments from similar organisms together. We are then free to analyze each cluster separately. This is challenging since we are not interested in similar sequences, but sequences that come from similar genomes, which are known to vary widely intra-genomically. Our dataset comprises of an extremely challenging scenario involving clustering fragments at the phyla level, where none of the phyla have been previously seen or identified. We present two variations of our proposed approach, one based on ART and K-means. We show that ART can cluster 500bp fragments from 17 novel phyla at an overall isolation/grouping that is 10% better than K-means and nearly 7 times over chance.
Starting Page	1
Ending Page	7
File Size	844408
Page Count	7
File Format	PDF
ISBN	9781424469161
ISSN	10987576
e-ISBN	9781424469185
DOI	10.1109/IJCNN.2010.5596644
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-18
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genomics Strain Training Clustering algorithms Subspace constraints Classification algorithms DNA
Content Type	Text
Resource Type	Article

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