Information-theoretic approaches to SVM feature selection for metagenome read classification.

Analysis of DNA sequences isolated directly from the environment, known as metagenomics, produces a large quantity of genome fragments that need to be classified into specific taxa. Most composition-based classification methods use all features instead of a subset of features that may maximize classifier accuracy. We show that feature selection methods can boost performance of taxonomic classifiers.

Signal processing for metagenomics: extracting information from the soup.

Traditionally, studies in microbial genomics have focused on single-genomes from cultured species, thereby limiting their focus to the small percentage of species that can be cultured outside their natural environment. Fortunately, recent advances in high-throughput sequencing and computational analyses have ushered in the new field of metagenomics, which aims to decode the genomes of microbes from natural communities without the need for cultivation. Although metagenomic studies have shed a great deal of insight into bacterial diversity and coding capacity, several computational challenges remain due to the massive size and complexity of metagenomic sequence data.

An information theoretic method of microarray probe design for genome classification.

In recent years, oligo microarrays, or more commonly-known DNA chips, have had a major impact in disease diagnosis, drug discovery, and gene identification. Microarrays contain Nmer DNA fragments, or oligos, in a series of 'wells' placed across the chip, where each well contains thousands of the same fragments and acts as a probe that detects the amount of a specific fragment. A recent use for microarrays is for identification of genomes, such as pathogens.

Metagenome fragment classification using N-mer frequency profiles.

A vast amount of microbial sequencing data is being generated through large-scale projects in ecology, agriculture, and human health. Efficient high-throughput methods are needed to analyze the mass amounts of metagenomic data, all DNA present in an environmental sample. A major obstacle in metagenomics is the inability to obtain accuracy using technology that yields short reads.