An operon-based data science approach for the inference of tRNA and rRNA gene evolution
Loading...
Date
2019-12-10
Authors
Pawliszak, Tomasz
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
With advancements in technology, big data can be easily generated and collected. Big data mining and analytics is in demand for discovery of important information and useful knowledge from these big data. An example of big data includes ribonucleic acid (RNA) genes in bacterial genomes in the area of bioinformatics and biological data mining. Specifically, in bacterial genomes, ribosomal ribonucleic acid (rRNA) and transfer ribonucleic acid (tRNA) genes are often organized into operons, i.e., segments of closely located genes that share a single promoter and are transcribed as a single unit. Analyzing how these genes and operons evolve can help us understand what the most common evolutionary events are affecting them and give us a better picture of ancestral codon usage and protein synthesis. We introduce a new approach for the inference of evolutionary histories of rRNA and tRNA genes in bacteria called BOPAL for Bacterial Operon Aligner, which is based on the identification of orthologous operons. This approach allows for a better inference of orthologous genes in genomes that have been affected by many rearrangements, which in turn helps with the inference of more realistic evolutionary scenarios and ancestors. From our comparisons of BOPAL with other gene order alignment programs using simulated data, we have found that BOPAL infers evolutionary events and ancestral gene orders more accurately than other methods based on alignments. An analysis of 12 Bacillus genomes also showed that BOPAL performs well in building ancestral histories in a minimal amount of events.
Description
Keywords
Data mining, Data science, Bioinformatics, Biological data mining
Citation
Tomasz Pawliszak, Meghan Chua, Carson K. Leung, and Olivier Tremblay-Savard. Operon-based approach for the inference of rRNA and tRNA evolutionary histories in bacteria. BMC Genomics (in press), 2019.