phippery: a software suite for PhIP-Seq data analysis

Author:

Galloway Jared G1ORCID,Sung Kevin1ORCID,Minot Samuel S2ORCID,Garrett Meghan E34,Stoddard Caitlin I3,Willcox Alexandra C345,Yaffe Zak A345,Yucha Ryan36,Overbaugh Julie13,Matsen Frederick A17ORCID

Affiliation:

1. Computational Biology, Public Health Sciences Division, Fred Hutchinson Cancer Center , Seattle, WA 98109, USA

2. Data Core, Fred Hutchinson Cancer Center , Seattle, WA 98109, USA

3. Human Biology Division, Fred Hutchinson Cancer Center , Seattle, WA 98109, USA

4. Molecular and Cellular Biology Program, University of Washington , Seattle, WA 98195, USA

5. Medical Scientist Training Program, University of Washington , Seattle, WA 98195, USA

6. Department of Microbiology, University of Washington School of Medicine , Seattle, WA 98195, USA

7. Howard Hughes Medical Institute , Seattle, WA, 98109, USA

Abstract

Abstract Summary We present the phippery software suite for analyzing data from phage display methods that use immunoprecipitation and deep sequencing to capture antibody binding to peptides, often referred to as PhIP-Seq. It has three main components that can be used separately or in conjunction: (i) a Nextflow pipeline, phip-flow, to process raw sequencing data into a compact, multidimensional dataset format and allows for end-to-end automation of reproducible workflows. (ii) a Python API, phippery, which provides interfaces for tasks such as count normalization, enrichment calculation, multidimensional scaling, and more, and (iii) a Streamlit application, phip-viz, as an interactive interface for visualizing the data as a heatmap in a flexible manner. Availability and implementation All software packages are publicly available under the MIT License. The phip-flow pipeline: https://github.com/matsengrp/phip-flow. The phippery library: https://github.com/matsengrp/phippery. The phip-viz Streamlit application: https://github.com/matsengrp/phip-viz.

Funder

National Institutes of Health

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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