Dataset in support of the University of Southampton Doctoral Thesis: Identifying antibiotic precursors by screening genetically encoded cyclic peptide libraries by Leonie Windeln

Abstract

Dataset in support of the University of Southampton Doctoral Thesis: Identifying antibiotic precursors by screening genetically encoded cyclic peptide libraries by Leonie Windeln. The data contains growth curves of E. coli in response to cyclic peptide exposure and cyclic peptide production via arabinose-dependent expression. As well as synthetic peptide testing data. Further, this dataset contains Sanger and next generation sequencing results and their analysis for the purpose of quality control, cyclic peptide identification and target identification. There are 5 power point presentations that hold the original main figures of the thesis. Sanger sequencing contains screenshots and figures of the data generated via sanger sequencing throughout the thesis. Characterisation of naïve libraries holds the information and figures about the naïve libraries in detail. Patterns drop out contains all figures about the drop out and is complemented by the two excel sheets focussed drop out sx5 summary and raw data patterns dropout as well as the prism file SFX4 focussed dropout barchart. Synthetic compound testing contains the files for growth curves and original images of testing the synthetic compounds. Target ID contains all data relevant to target identification. The data was collected via different experimental methods (growth curves of E.coli, NGS, WGS, Sanger sequencing, SPPS, RTqPCR) that are detailed in the thesis, which is also included as a word and pdf document. Licence: CC BY NC ND Related projects/Funders: MSD funded Grant: EP/R513325/1

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