Chatterjee, S., Koslicki, D., Dong, S. et al. (8 more authors) (2014) SEK: sparsity exploiting k-mer-based estimation of bacterial community composition. Bioinformatics, 30 (17). pp. 2423-2431. ISSN 1367-4803
Abstract
MOTIVATION: Estimation of bacterial community composition from a high-throughput sequenced sample is an important task in metagenomics applications. As the sample sequence data typically harbors reads of variable lengths and different levels of biological and technical noise, accurate statistical analysis of such data is challenging. Currently popular estimation methods are typically time-consuming in a desktop computing environment.
RESULTS: Using sparsity enforcing methods from the general sparse signal processing field (such as compressed sensing), we derive a solution to the community composition estimation problem by a simultaneous assignment of all sample reads to a pre-processed reference database. A general statistical model based on kernel density estimation techniques is introduced for the assignment task, and the model solution is obtained using convex optimization tools. Further, we design a greedy algorithm solution for a fast solution. Our approach offers a reasonably fast community composition estimation method, which is shown to be more robust to input data variation than a recently introduced related method.
AVAILABILITY AND IMPLEMENTATION: A platform-independent Matlab implementation of the method is freely available at http://www.ee.kth.se/ctsoftware; source code that does not require access to Matlab is currently being tested and will be made available later through the above Web site.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author 2014. Published by Oxford University Press. This is an author produced version of a paper subsequently published in Bioinformatics. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Algorithms; Bacteria; High-Throughput Nucleotide Sequencing; Metagenomics; Models, Statistical; RNA, Ribosomal, 16S; Sequence Analysis, DNA |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 15 Feb 2016 12:41 |
Last Modified: | 29 Mar 2018 02:22 |
Published Version: | http://dx.doi.org/10.1093/bioinformatics/btu320 |
Status: | Published |
Publisher: | Oxford University Press |
Refereed: | Yes |
Identification Number: | 10.1093/bioinformatics/btu320 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:95092 |