This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Bioinformatics following peer review. The definitive publisher-authenticated version, Chatterjee, S., Koslicki, D., Dong, S., Innocenti, N., Cheng, L., Lan, Y., ... & Corander, J. (2014). SEK: Sparsity exploiting k-mer-based estimation of bacterial community composition. Bioinformatics, 30(17), 2423-2431. doi:10.1093/bioinformatics/btu320, is available online at: http://bioinformatics.oxfordjournals.org/content/30/17/2423.
The published article is copyrighted by the Author(s) and published by Oxford University Press.
MOTIVATION: Estimation of bacterial community composition from
a high-throughput sequenced sample is an important task in
metagenomics applications. Since 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 very 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: 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 website.