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

TL;DR

This paper introduces a novel, fast, and robust method for estimating bacterial community composition from high-throughput sequencing data by leveraging sparsity and convex optimization techniques, improving efficiency and robustness over existing methods.

Contribution

The paper presents a sparsity-based estimation approach using convex optimization and greedy algorithms for rapid and robust bacterial community analysis from sequencing data.

Findings

The method is significantly faster than traditional approaches.

It demonstrates higher robustness to data variation.

The approach achieves accurate community composition estimation.

Abstract

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.