The cyanobacteria have a high structural variability and phenotypic diversity, because of this
reason they are present in several different habitat. The high diversity of Amazonian biome,
summed to bacteriocin studies (peptides produced by numerous microorganisms, including
cyanobacteria) have a great importance to the pharmaceutical and food industry. Besides that,
other relevant point for bacteriocin related research is their relation with genes of the domain
nif11, present in cyanobacteria from the Amazon region. In the present study, we used
bioinformatics techniques and tools such as multiple sequence alignment, visualization
software, and profiling by inference. For the later we used a developed and trained Hidden
Markov Model (HMM) to identify sequences of nif11 genes in seven genomes.The efficiency
of the developed model was verified by comparing the results obtained with those generated
by the TIGR03798 model, where there was an increase on the sensibility of the target
sequences identification.
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