BACKGROUND: The bacterial genus Borrelia (phylum Spirochaetes) consists of two groups of pathogens represented respectively by B. burgdorferi, the agent of Lyme borreliosis, and B. hermsii, the agent of tick-borne relapsing fever. The number of publicly available Borrelia genomic sequences is growing rapidly with the discovery and sequencing of Borrelia strains worldwide. There is however a lack of dedicated online databases to facilitate comparative analyses of Borrelia genomes. DESCRIPTION: We have developed BorreliaBase, an online database for comparative browsing of Borrelia genomes. The database is currently populated with sequences from 35 genomes of eight Lyme-borreliosis (LB) group Borrelia species and 7 Relapsing-fever (RF) group Borrelia species. Distinct from genome repositories and aggregator databases, BorreliaBase serves manually curated comparative-genomic data including genome-based phylogeny, genome synteny, and sequence alignments of orthologous genes and intergenic spacers. CONCLUSIONS: With a genome phylogeny at its center, BorreliaBase allows online identification of hypervariable lipoprotein genes, potential regulatory elements, and recombination footprints by providing evolution-based expectations of sequence variability at each genomic locus. The phylo-centric design of BorreliaBase (http://borreliabase.org) is a novel model for interactive browsing and comparative analysis of bacterial genomes online.

MOTIVATION: Direct sequencing of microbes in human ecosystems (the human microbiome) has complemented single genome cultivation and sequencing to understand and explore the impact of commensal microbes on human health. As sequencing technologies improve and costs decline, the sophistication of data has outgrown available computational methods. While several existing machine learning methods have been adapted for analyzing microbiome data recently, there is not yet an efficient and dedicated algorithm available for multiclass classification of human microbiota. RESULTS: By combining instance-based and model-based learning, we propose a novel sparse distance-based learning method for simultaneous class prediction and feature (variable or taxa, which is used interchangeably) selection from multiple treatment populations on the basis of 16S rRNA sequence count data. Our proposed method simultaneously minimizes the intraclass distance and maximizes the interclass distance with many fewer estimated parameters than other methods. It is very efficient for problems with small sample sizes and unbalanced classes, which are common in metagenomic studies. We implemented this method in a MATLAB toolbox called MetaDistance. We also propose several approaches for data normalization and variance stabilization transformation in MetaDistance. We validate this method on several real and simulated 16S rRNA datasets to show that it outperforms existing methods for classifying metagenomic data. This article is the first to address simultaneous multifeature selection and class prediction with metagenomic count data. AVAILABILITY: The MATLAB toolbox is freely available online at http://metadistance.igs.umaryland.edu/. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Background The human gastrointestinal tract is a complex ecosystem containing a delicate balance of human and microbial cells involved in an intricate symbiotic relationship. In general, the microbial constituency helps maintain a healthy environment and aids in the efficient digestion. However, environmental and/or genetic factors may result in an altered bacterial composition that manifests in a diseased condition, such as Crohn’ sd isease. The recent availability of genomic-based molecular technologies such as whole community genome sequencing and whole community proteomics have provided unique capabilities of profiling the compositions and activities of this microbiome without having to cultivate its membership.