Genome Mining Tools for Secondary Metabolites in Bacteria

DSpace Repository


Dateien:

URI: http://hdl.handle.net/10900/134243
http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1342433
http://dx.doi.org/10.15496/publikation-75594
Dokumentart: Dissertation
Date: 2022-12-15
Language: English
Faculty: 7 Mathematisch-Naturwissenschaftliche Fakultät
Department: Informatik
Advisor: Ziemert, Nadine (Prof. Dr.)
Day of Oral Examination: 2022-11-21
DDC Classifikation: 004 - Data processing and computer science
Other Keywords:
Natural products,
genome mining
bioinformatics
License: Publishing license including print on demand
Order a printed copy: Print-on-Demand
Show full item record

Abstract:

As products of billions of years of evolution, secondary metabolites perform a wide range of activities ensuring the survival of organisms in competitive envi- ronments. These natural products synthesized by diverse living beings through- out the tree of life have been a valuable resource for many industrial applica- tions. Specifically, in pharmaceutical ventures, natural products are used pro- foundly against cancer, pests and microorganisms. Peaked in the golden era of antibiotics, drug discovery against infectious diseases was mainly centered around natural products from fungi and bacteria. Consequently however, mi- crobes have made impressive and frightening progress in gaining resistance against antimicrobials fueled by their improper usage. Coupled with the stag- nation in discovery rates of novel natural products, antimicrobial resistance has become a destructive phenomenon damaging humanity financially and health- wise. To fight off such resistant microbes, it is of paramount importance that we find and produce novel secondary metabolites with antimicrobial features. With the vast improvements in sequencing technologies and analysis algorithms, we possess repositories swarming with “multiomics”-based data, ready to be mined. Now, a crucial thing to do is to enable the prioritization of such data for the sub- sequent processes in wet-lab applications. In this thesis, I have built command line tools as well as web-based databases and pipelines to I) detect genes conferring antibiotic resistance in order to find promising biosynthetic gene clusters that might encode for novel antibiotics and II) prioritize target genes for genetic manipulation that could be used to increase the production of secondary metabolites.

This item appears in the following Collection(s)