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Increasing protein stability by inferring substitution effects from high-throughput experiments

Shell 0.02% R 6.91% Python 3.23% Jupyter Notebook 89.84%

_2022_norrild_gmma_trx's Introduction

_2022_Norrild_GMMA_TRX

Scripts and output from "Increasing protein stability by inferring substitution effects from high-throughput experiments" by Rasmus K. Norrild, Kristoffer E. Johansson, Charlotte O’Shea, Jens Preben Morth, Kresten Lindorff-Larsen and Jakob R. Winther

Directories

  • variant_call_scripts: Code mentioned in the article used for processing FASTQ files
  • data: Raw data from the CPOP screen and computational methods including plots
  • GMMA: Software, input and output files. The analysis is implemented in R and split into 5 scripts (gmma01-gmma05), see run.sh. The excel file trx_gmma.xlsx contains all 3 output files residue.txt, subst.csv and mutant.csv.
  • nano_dsf: Data and notebook for stability measurements

Sequencing reads are available at https://erda.ku.dk/archives/66b4989dbfdc6374ba673594df195465/published-archive.html

DOI

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