Abstract

Next-generation sequencing techniques allow us to generate reads from a microbial environment in order to analyze the microbial community. However, assembling of a set of mixed reads from thousands of different species to form contigs is a bottleneck of metagenomic research. Although there are many assemblers for assembling reads sampled from a single genome, only a few assemblers work on metagenomic data. Moreover, the performances of these assemblers on metagenomic data are far from satisfactory because of the following reasons: (1) up to 99% of the species in the sample are unknown and have no reference genomes, (2) sequencing depths of genomes from different species are highly uneven because different species in a sample have different abundances (over 100 times), and (3) genomes of subspecies and species in a sample can be very similar and the existence of common regions across different genomes make the assembly problem much more complicated. In this talk, different techniques (IDBA-UD and MetaIDBA), with possible incorporation with binning results (MetaCluster), for solving these three problems will be presented. As the last two problems are more severe for RNA -Seq data from metagenomic sample (metatranscriptomic data) than metagenomic data, no existing assemblers work well on the metatranscriptome data (even though gene sequence information of some microbes might be known). Main features of a metatranscriptome assembler (IDBA-MT) will also be discussed.