Moreover, unassembled metagenomic sequence data are fragmented, noisy, error prone and contain uneven sequencing depths. Thus, raw sequence data alone are typically not sufficient for an in-depth analysis of a communities functional gene repertoire. Even though shotgun metagenomic sequencing provides comprehensive access to microbial genomic material, many of the encoded functional genes are substantially longer (~1000 bp ) than the length of reads provided by the sequencing platforms most commonly used for shotgun metagenomic studies (Illumina HiSeq 3000, 2 × 150 bp ). The ‘science’ of metagenomics has greatly accelerated the study of uncultured microorganisms in their natural environments, providing unparalleled insights into microbial community composition and putative functionality. We provide a concise workflow for the selection of the best assembly tool. We found that assembler choice ultimately depends on the scientific question, the available resources and the bioinformatic competence of the researcher. MEGAHIT emerged as a computationally inexpensive alternative to SPAdes, assembling the most complex dataset using less than 500 GB of RAM and within 10 hours. Overall, we found that SPAdes provided the largest contigs and highest N50 values across 6 of the 9 environmental datasets, followed by MEGAHIT and metaSPAdes. To assist with selection of an appropriate metagenome assembler we evaluated the capabilities of nine prominent assembly tools on nine publicly-available environmental metagenomes, as well as three simulated datasets. However, while several platforms have been developed for this critical step, there is currently no clear framework for the assembly of metagenomic sequence data. The analysis of metagenomic sequences facilitates gene prediction and annotation, and enables the assembly of draft genomes, including uncultured members of a community. Metagenomics allows unprecedented access to uncultured environmental microorganisms.

Tutorial clc genomics workbench