nist_lacI_landscape_analysis

This repo contains no code but contains pointers to the various software packages used for analysis of the NIST lacI landscape dataset

The custom software used for analysis of the NIST lacI landscape dataset includes the following software packages, available on GitHub as indicated:

1. bartender-1.1, barcode clustering algorithm Described in: Zhao, L., Liu, Z., Levy, S. F. & Wu, S. Bartender: a fast and accurate clustering algorithm to count barcode reads. Bioinformatics 34, 739–747 (2018). GitHub repository: https://github.com/LaoZZZZZ/bartender-1.1

2. NISTBartender, Windows GUI interface for dual barcode parsing and automating calls to bartender-1.1 GitHub repository: https://github.com/djross22/NISTBartender

3. engineering-bio-lacI-landscape, bioinformatic pipeline for processing long-read PacBio sequencing data GitHub repository: https://github.com/nate-d-olson/engineering-bio-lacI-landscape

4. gsf_ims_fitness, Python package and Jupyter notebooks used for analysis of sequencing data after bartender-1.1, NISTBartender, and engineering-bio-lacI-landscape GitHub repository: https://github.com/djross22/gsf_ims_fitness

5. laci-landscape-dnn, Python code to set up and train the deep neural network model GitHub repository: https://github.com/ptonner/laci-landscape-dnn

Detailed instructions for installing and running each software component are included with each components repository.