Reconstructing microbial communities

Mihai Pop

doi:10.1186/gb-2011-12-s1-i13

Abstract

Metagenomic studies have allowed an unprecedented view of the microbial communities that inhabit our world and our bodies. Deep sequencing data have already been generated from several environments, as well as from various human body sites. As more data are generated, we are beginning to understand the structure of our commensal microbial communities and how microbes affect our health. Analyzing the metagenomic data, however, poses significant computational challenges, because few software tools are available that can handle the volume and characteristics of the data being generated. In my talk, I will primarily focus on the challenges posed by metagenomic assembly and will outline recent research in my laboratory aimed at meeting these challenges. I will also describe some of the analyses that can be performed on the assembled data but would not be possible in read-based analyses.

Highlights

Despite a decrease in the rate of mortality due to diarrhea in the past few decades, diarrhea remains one of the leading causes of childhood deaths worldwide, especially in developing countries
Our simulation shows the following: first, a single-end 454 Jr Titanium run combined with a paired-end 454 Jr Titanium run may assemble about 90% of 100 genomes into
We evaluated the performance of ScaffViz on seven datasets of varying size and complexity

Summary

Introduction

Despite a decrease in the rate of mortality due to diarrhea in the past few decades, diarrhea remains one of the leading causes of childhood deaths worldwide, especially in developing countries. Recent genome-wide association studies (GWAS) have identified allele T of a single nucleotide polymorphism (SNP), rs2294008, in the prostate stem cell antigen (PSCA) gene as a risk factor for bladder cancer [1,2]. A recent genome-wide association study (GWAS) of bladder cancer identified a single nucleotide polymorphism (SNP), rs11892031, within the UGT1A gene cluster on chromosome 2q37.1, as a novel risk factor. Genome-wide association studies (GWAS) of human complex disease have identified a large number of disease-associated genetic loci, which are distinguished by distinctive frequencies of specific single nucleotide polymorphisms (SNPs) in individuals with a particular disease These data do not provide direct information on the biological basis http://genomebiology.com/supplements/12/S1 of a disease or on the underlying mechanisms. There may be multiple paths in the de Bruijn graph that can yield sequences with optical maps that match the genome’s optical map, these paths all yield very similar sequences in most cases

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