Experiments by Caroline Kikawa, Jackson Barr Stuart and Leslie Goo. Analysis by Jesse Bloom and Caroline Kikawa.
For a summary of the results, see selections_analysis.ipynb.
Other results are placed in ./results/, although not all files are tracked in the GitHub repo.
- Barcoded subamplicon sequencing reads are processed into codon counts, and stored in ./results/codoncounts/
- For individual and median antibody escape values, see differential selection measurements, placed in ./results/diffsel/.
- PDB files with reassigned B-factors with antibody escape are placed in ./results/reassignedpdb/.
For the data and analysis used in the neutralization assays and visualizations for the paper, see the notebooks and results in ./paper_figures/, and for documentation of the SRA sequencing submission, see ./sra_submission/
First activate the conda environment for the analysis. If you have prebuilt the relevant environments, you can do this just with:
conda activate zikv_dmstools2
or:
conda activate neutcurve
Otherwise, first build the conda environments from the environments/environment_dmstools2.yml or environments/environment_neutcurve.ymlfile, then activate it as above.
After you have activated the either conda environment, simply run the Python Jupyter notebooks: use environment_dmstools2 to run selections_analysis.ipynb or polyclonal_analysis.ipynb. On the Hutch cluster, you will first want to grab a node with 16 cores before doing this. For the notebooks within ./paper_figures/, use environment_neutcurve.
The input data are in ./data/:
-
./data/E.fasta: coding sequence of E protein from ZIKV MR766 strain used as parent for mutagenesis.
-
./data/subamplicon_alignspecs.txt: the alignment specs for the barcoded subamplicon sequencing.
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./data/samplelist.csv: all the samples that we sequenced and the locations of the associated deep-sequencing data.
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./data/concat_fastq: a folder containing a script, input, output and documentation for a program that will accept multiple FASTQ files for a single sample and concatenate them together.
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./data/functonal_data: a folder containing amino acid preferences and site-wise mutational tolerance data published in Sourisseau et al. 2019