Title: "Statistical and Computational Challenges for Metagenomics Analysis Based on Next-Generation Sequencing Data"
Speaker: Hongmei Jiang, Department of Statistics, Northwestern University
Place: HORT 117; September 20, 2011, Tuesday, 4:30pm

Abstract

Next-generation sequencing technologies greatly promote the field of metagenomics which studies multiple genomes recovered directly from an environment, without the need of culturing them. Based on the short reads sequenced from a metagenomic sample, we would like to identify the multiple species or genomes contained in the sample and to estimate their relative abundance. One widely used approach is to employ sequence homology by aligning sequence reads to known reference sequence databases using a comparison program such as BLAST and assigning the reads to the taxa of the taxonomy tree based on the best match or multiple high-scoring hits. Due to homogeneity of DNA sequences, low coverage sequencing, and large volume of experimental data, estimating the relative abundance of multiple genomes accurately becomes very challenging. Here, we propose a mixture model to estimate the relative abundance of the species and to assign the reads in a global framework. The method is comprehensively tested on simulated metagenomic and is able to accurately estimate the relative abundance of the genomes. We also apply the proposed method on several metagenomic real datasets. The current statistical and computational methods that are being developed to analyze the metagnoimcs data and the challenges will be highlighted in the talk.

Associated Reading:
Daniel H. Huson, Alexander F. Auch, Ji Qi, and Stephan C. Schuster. 2007. MEGAN analysis of metagenomic data.Genome Research.



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