Metagenomics is an inherently complex field in which one of
the primary goals is to determine the compositional organisms present
in an environmental sample. Thereby, diverse tools have been developed
that are based on the similarity search results obtained from comparing
a set of sequences against a database. However, to achieve this goal
there still are affairs to solve such as dealing with genomic variants and
detecting repeated sequences that could belong to different species in a
mixture of uneven and unknown representation of organisms in a sample.
Hence, the question of whether analyzing a sample with reads provides
further understanding of the metagenome than with contigs arises. The
assembly yields larger genomic fragments but bears the risk of producing
chimeric contigs. On the other hand, reads are shorter and therefore
their statistical significance is harder to asses, but there is a larger number
of them. Consequently, we have developed a workflow to assess and
compare the quality of each of these alternatives. Synthetic read datasets
beloging to previously identified organisms are generated in order to validate
the results. Afterwards, we assemble these into a set of contigs and
perform a taxonomic analysis on both datasets. The tools we have developed
demonstrate that analyzing with reads provide a more trustworthy
representation of the species in a sample than contigs especially in cases
that present a high genomic variability.
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