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Data analysis for multiple time-point whole-genome NGS

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README

What is this repository for?

Virus Analysis

For mutiple time-point whole-genome NGS experimental evolution data
* Time-serial mutation rate analysis
* Time-serial effective population size analysis
* Time-serial selection analysis

Contribution guidelines

references

H. Shim. Feature learning of virus genome evolution with the nucleotide skip-gram neural networks (accepted in NIPS 2017 Workshop).	
A. Carratalà, H. Shim, Q. Zhong, V. Bachmann, J.D. Jensen, T. Kohn. Experimental adaptation of human echovirus 11 to ultraviolet radiation leads to tolerance to disinfection and resistance to ribavirin. Virus Evolution 3, 2, vex035 (2017).
Q. Zhong, A. Carratalà, H. Shim, V. Bachmann, J.D. Jensen, T. Kohn. Resistance of echovirus 11 to CIO2 is associated with enhanced host receptor use, altered entry routes and high fitness. Environ. Sci. Technol. 51, 10746-10755 (2017).

keyword

population genetics; time-sampled data; approximate Bayesian computation; Wright-Fisher model; time-serial mutation rate analysis; time-serial effective population size analysis; time-serial selection analysis

examples

see Example_diploid_model.R
see Example_haploid_model.R

Who do I talk to?

Author and Maintainer

Hyunjin Shim [[email protected]]

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Data analysis for multiple time-point whole-genome NGS

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