PT  - JOURNAL ARTICLE
AU  - Philipp N. Spahn
AU  - Tyler Bath
AU  - Ryan J. Weiss
AU  - Jihoon Kim
AU  - Jeffrey D. Esko
AU  - Nathan E. Lewis
AU  - Olivier Harismendy
TI  - PinAPL-Py: a web-service for the analysis of CRISPR-Cas9 Screens
AID  - 10.1101/147462
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 147462
4099  - http://biorxiv.org/content/early/2017/06/08/147462.short
4100  - http://biorxiv.org/content/early/2017/06/08/147462.full
AB  - Summary Large-scale genetic screens using CRISPR/Cas9 technology have emerged as a major tool for functional genomics. With its increased popularity, experimental biologists frequently acquire large sequencing datasets for which they often do not have an easy analysis option. While a few bioinformatic tools are available, their installation and use typically require bioinformatic expertise. To make sequencing data analysis more accessible to a wide range of scientists, we developed a Platform-independent Analysis of Pooled Screens using Python (PinAPL-Py), which we present as an intuitive web-service. PinAPL-Py implements state-of-the-art tools and statistical models, assembled in a comprehensive workflow covering alignment, quality control, enrichment/depletion analysis and gene ranking. The workflow supports multiple libraries, and offers different analysis options for read count normalization or gene-ranking methods. Other technical parameters can be easily adjusted to allow greater flexibility and customization. Thus PinAPL-Py provides high-quality data analysis in an easily accessible service.Availability and implementation PinAPL-Py is freely accessible at pinapl-py.ucsd.edu with instructions, documentation and test datasets. Experienced users can run PinAPL-Py on their local machine using the Docker image (oncogx/pinaplpy_docker). Documentation can be found on GitHub at https://github.com/LewisLabUCSD/PinAPL-Py.Supplementary information Supplementary information for this article is available online.Contact oharismendy{at}ucsd.edu, nlewisres{at}ucsd.edu