Abstract
In 1980s, Carl Woese made a ground breaking contribution to microbiology using rRNA-genes for phylogenetic classifications. He used it not only to explore microbial diversity but also as a method for bacterial annotation. Today, rRNA-based analysis remains a central method in microbiology. Many researchers followed this track, using several new generations of Artificial Neural Networks obtained high accuracies using available datasets of their time. By the time, the number of bacteria increased enormously. In this article we used Longest Common Subsequence similarity measure to classify bacterial 16S rRNA gene sequences of 1.820.414 bacteria in SILVA, 3.196.038 bacteria in RDP, and 198.509 bacteria in Greengenes. The last two taxonomy have six taxonomical levels, phylum, class, order, family, genus, and species, while SILVA has two more levels subclass and suborder, but lacks species level. The majority of classifications (98%) were of high accuracy (98%).
Highlights
Bacteria are often identified as the causes of human and animal diseases
RRNA-based analysis remains a central method in microbiology, used to explore microbial diversity and as a method for bacterial annotation. ribosomal RNA (rRNA)-based identification methods are conceptually easier to interpret than molecular phylogenetic analyses and are often preferred when the groups are well defined
In our study we have found that Greengenes is more inconsistent compared to the first two
Summary
Some bacteria, produce antibiotics; others live symbiotically in the guts of animals including humans, or elsewhere in their bodies, or on the roots of certain plants. They help to break down dead organic matter; make up the base of the food web in many environments. Bacteria are of such immense importance because of their extreme flexibility, capacity for rapid growth and reproduction, and contribution to the processes in the body of humans. Gursoy/ Southeast Europe Journal of Soft Computing Vol. No.2 September 2018 (69-78)
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