Tuesday, October 27, 2015

Fast genome and metagenome distance estimation using MinHash / Machine learning for metagenomics: methods and tools

Awesome!  If you've read these blog entries [1,2,3] you know that genomics is now heading toward the analysis of large population of genomes aka metagenomics.

The first preprint was promised back in August and uses a Locality Sensitive Hashing to perform a comparison on a large population of genomes. The second preprint reviews several ML techniques for metagenomics and points to the earlier third preprint where another hashing technique is used.  That last approach used Vowpal Wabbit that is not a Locality Sensitive Hashing technique while MinHash used in the first preprint is. Without further ado:

Fast genome and metagenome distance estimation using MinHash by Brian D. Ondov, Todd J. Treangen, Adam B. Mallonee, Nicholas H. Bergman, Sergey Koren, Adam M. Phillippy

Machine learning for metagenomics: methods and tools by Hayssam Soueidan, Macha Nikolski

While genomics is the research field relative to the study of the genome of any organism, metagenomics is the term for the research that focuses on many genomes at the same time, as typical in some sections of environmental study. Metagenomics recognizes the need to develop computational methods that enable understanding the genetic composition and activities of communities of species so complex that they can only be sampled, never completely characterized.
Machine learning currently offers some of the most computationally efficient tools for building predictive models for classification of biological data. Various biological applications cover the entire spectrum of machine learning problems including supervised learning, unsupervised learning (or clustering), and model construction. Moreover, most of biological data -- and this is the case for metagenomics -- are both unbalanced and heterogeneous, thus meeting the current challenges of machine learning in the era of Big Data.
The goal of this revue is to examine the contribution of machine learning techniques for metagenomics, that is answer the question "to what extent does machine learning contribute to the study of microbial communities and environmental samples?" We will first briefly introduce the scientific fundamentals of machine learning. In the following sections we will illustrate how these techniques are helpful in answering questions of metagenomic data analysis. We will describe a certain number of methods and tools to this end, though we will not cover them exhaustively. Finally, we will speculate on the possible future directions of this research.

Large-scale Machine Learning for Metagenomics Sequence Classification by Kévin Vervier, Pierre Mahé, Maud Tournoud, Jean-Baptiste Veyrieras, Jean-Philippe Vert
Metagenomics characterizes the taxonomic diversity of microbial communities by sequencing DNA directly from an environmental sample. One of the main challenges in metagenomics data analysis is the binning step, where each sequenced read is assigned to a taxonomic clade. Due to the large volume of metagenomics datasets, binning methods need fast and accurate algorithms that can operate with reasonable computing requirements. While standard alignment-based methods provide state-of-the-art performance, compositional approaches that assign a taxonomic class to a DNA read based on the k-mers it contains have the potential to provide faster solutions. In this work, we investigate the potential of modern, large-scale machine learning implementations for taxonomic affectation of next-generation sequencing reads based on their k-mers profile. We show that machine learning-based compositional approaches benefit from increasing the number of fragments sampled from reference genome to tune their parameters, up to a coverage of about 10, and from increasing the k-mer size to about 12. Tuning these models involves training a machine learning model on about 10 8 samples in 10 7 dimensions, which is out of reach of standard soft-wares but can be done efficiently with modern implementations for large-scale machine learning. The resulting models are competitive in terms of accuracy with well-established alignment tools for problems involving a small to moderate number of candidate species, and for reasonable amounts of sequencing errors. We show, however, that compositional approaches are still limited in their ability to deal with problems involving a greater number of species, and more sensitive to sequencing errors. We finally confirm that compositional approach achieve faster prediction times, with a gain of 3 to 15 times with respect to the BWA-MEM short read mapper, depending on the number of candidate species and the level of sequencing noise.
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