SEK: Sparsity exploiting k-mer-based estimation of bacterial community composition

Saikat Chatterjee (Corresponding Author), David Koslicki, Siyuan Dong, Nicolas Innocenti, Lu Cheng, Yueheng Lan, Mikko Vehkaperä, Mikael Skoglund, Lars K. Rasmussen, Erik Aurell, Jukka Corander

Research output: Contribution to journalArticleScientificpeer-review

7 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)2423-2431
Number of pages9
Issue number17
Publication statusPublished - 1 Sept 2014
MoE publication typeA1 Journal article-refereed


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