Abstract
One of the major goals in metagenomics is to identify organisms present in the microbial community from a huge set of unknown DNA sequences. This profiling has valuable applications in multiple important areas of medical research such as disease diagnostics. Nevertheless, it is not a simple task, and many approaches that have been developed are slow and depend on the read length of the DNA sequences. Here we introduce an innovative and agile approach which k-mer and Monte Carlo simulation to profile and report abundant organisms present in metagenomic samples and their relative abundance without sequence length dependencies. The program was tested with a simulated metagenomes, and the results show that our approach predicts the organisms in microbial communities and their relative abundance.
