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Geno-informatics for Prediction of Virulence and Drug Resistance in Bacterial Pathogens

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Integrated Omics Approaches to Infectious Diseases

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Abstract

In recent years, there has been a rapid surge in the number of resistant strains of bacterial pathogens, mainly due to misuse of antibiotics in our day-to-day environment for treating common ailments. Hence new drug discovery is a priority. The discoveries of new antimicrobials increasingly rely on genotypic data resulting from whole genome sequencing. In recent years, there has been advancement in the whole genome sequence facilities and a number of completed genomes, but the wealth of information is not being fully utilized. Therefore, there is a need of microbial informatics algorithms to exploit this genomic data and provide an opportunity for the development of newer remedies. The combined advent of both genomics and bioinformatics can help in the identification, screening, and refinement of drug targets and predict drug resistance leading towards fast and efficient antimicrobial therapeutics. Therefore, this chapter is focused on the in silico approaches which can facilitate understanding, identifying, and controlling the virulence and bacterial antibiotic resistance.

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Authors and Affiliations

  1. Molecular Medicine Laboratory, Department of Microbiology, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, India

    Umay Kulsum, S. Rashmi Mudliar & Sarman Singh

  2. Department of Surgery, Millers School of Medicine, University of Miami, Coral Gables, FL, USA

    Praveen Kumar Singh

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  1. Umay Kulsum
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  1. Amity Institute of Biotechnology, Amity University Haryana, Gurugram, Haryana, India

    Saif Hameed

  2. Amity Institute of Biotechnology, Amity University Haryana, Gurugram, Haryana, India

    Zeeshan Fatima

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Kulsum, U., Singh, P.K., Mudliar, S.R., Singh, S. (2021). Geno-informatics for Prediction of Virulence and Drug Resistance in Bacterial Pathogens. In: Hameed, S., Fatima, Z. (eds) Integrated Omics Approaches to Infectious Diseases. Springer, Singapore. https://doi.org/10.1007/978-981-16-0691-5_1

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