Estimating time of HIV infection from NGS data

by Vadim Puller, Richard Neher, and Jan Albert, biorxiv, doi:10.1101/129387

Code associated with our manuscript on estimating the time of HIV infection from next generation sequencing data. This project uses whole genome deep sequencing data from Zanini et al 2015 to establish a method to estimate time of infection from viral diversity.

Data directories

Frequency_data

The directory Frequency_data contains the training data on which the analysis is based. These files are derived from the NGS data by mapping.

• patient_tt.npy : time points for patient samples
• patient_data.npy and patient_mask.npy : data and mask for the array of nucleotide frequencies for all time points
• patient_viral_load.npy : viral load data
• patient_dilutions.npy and patient_dilutions_mask.npy : dilutions data (see Zanini et al 2015)
• annotations.txt : auxhiliary gene annotations info

K31_data

The directory K31_data contains the validation data consisting of two time points from additional 31 patients. The directory content is structured as above in Frequency_data.

Scripts

The scripts EDI_functions.py, EDI_plotting.py, and EDI_median_regression.py contain the source code to load the data, determine the regression coefficients, and generate the figures in the manuscript.

• EDI_functions.py : functions for loading data, calculating sequence diversity, and linear fitting
• EDI_plotting.py : functions for data analysis and generating plots
• EDI_median_regression.py : script generating plots presented in the manuscript
• K31_prediction.py: Script generating figure 6 (validation of ETI inference on additional 31 patients)