An interactive workflow for the analysis of contigs from the metagenomic shotgun assembly of SMRT Sequencing data.
The data throughput of next-generation sequencing allows whole microbial communities to be analyzed using a shotgun sequencing approach. Because a key task in taking advantage of these data is the ability to cluster reads that belong to the same member in a community, single-molecule long reads of up to 30 kb from SMRT Sequencing provide a unique capability in identifying those relationships and pave the way towards finished assemblies of community members. Long reads become even more valuable as samples get more complex with lower intra-species variation, a larger number of closely related species, or high intra-species variation. Here we present a collection of tools tailored for PacBio data for the analysis of these fragmented metagenomic assembles, allowing improvements in the assembly results, and greater insight into the communities themselves. Supervised classification is applied to a large set of sequence characteristics, e.g., GC content, raw-read coverage, k-mer frequency, and gene prediction information, allowing the clustering of contigs from single or highly related species. A unique feature of SMRT Sequencing data is the availability of base modification / methylation information, which can be used to further analyze clustered contigs expected to be comprised of single or very closely related species. Here we show base modification information can be used to further study variation, based on differences in the methylated DNA motifs involved in the restriction modification system. Application of these techniques is demonstrated on a monkey intestinal microbiome sample and an in silico mix of real sequencing data from distinct bacterial samples.