Operon Finder: A Deep Learning-based Web Server for Accurate Prediction of Prokaryotic Operons.

Operons are groups of consecutive genes that transcribe together under the regulation of a common promoter. They influence protein regulation and various physiological pathways, making their accurate detection desirable. The detection of operons through experimental means is a laborious and financially intensive process. Therefore, human experts predict potential operons utilizing their prior knowledge of the genetic organization and functional correlation of the genes. However, with the rise in the number of completely sequenced genomes, the development of automated algorithms, tools, and web servers is highly preferred over manual detection for operon prediction. Currently available state-of-the-art algorithms use a deep learning-based model to predict if the adjacent genes belong to the same operons in the given genome. However, these require an understanding of programming knowledge and computational skills, making them not-very user friendly. In this study, we developed a user-friendly web service, Operon Finder, for on-the-fly prediction of operons using the deep learning method. The interface provides a facility for genome search, operon live-filtering, viewing operonic DNA sequences, downloading predicted results, and links for data retrieval from the NCBI (National Center for Biotechnology Information) database. The web server is available at https://www.iitg.ac.in/spkanaujia/operonfinder.html. The experimental methods and the implementation details are publicly available at https://github.com/SPKlab/Operon-Finder.

Identification and characterization of ABC transporters for carbohydrate uptake in Thermus thermophilus HB8.

Organisms use a variety of carbohydrates and metabolic pathways in order to capitalize in their specific environments. Depending upon their habitat, organism employs different types of transporters to maintain the cellular nutritional balance via central metabolism. A major contributor in this process in bacteria is a carbohydrate ABC transporter. The focus of this study is to get an insight into the carbohydrate transport and metabolism of a hot-spring-dwelling bacterium Thermus thermophilus HB8. We applied high-throughput data-mining approaches for identification and characterization of carbohydrate ABC transporters in T. thermophilus HB8. This enabled the identification of 11 putative carbohydrate ABC transport systems. To identify the cognate ligands for these transporters, functional annotation was performed. However, scarcity of homologous-protein's function hinders the process of functional annotation. Thus, to overcome this limitation, we integrated the functional annotation of carbohydrate ABC transporters with their metabolic analysis. Our results demonstrate that out of 11 putative carbohydrate ABC transporters, six are involved in the sugar (four for monosaccharides and polysaccharides-degraded products and two for osmotic regulation), four in phospholipid precursor (namely UgpABCE) and the remaining one in purine uptake. Further, analysis suggests the existence of sharing mechanism of transmembrane domains (TMDs) and/or nucleotide-binding domains (NBDs) among the 11 carbohydrate ABC transporters.