PT  - JOURNAL ARTICLE
AU  - Alexandre Yahi
AU  - Paul Hoffman
AU  - Margot Brandt
AU  - Pejman Mohammadi
AU  - Nicholas P. Tatonetti
AU  - Tuuli Lappalainen
TI  - EdiTyper: a high-throughput tool for analysis of targeted sequencing data from genome editing experiments
AID  - 10.1101/2020.07.30.229088
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.07.30.229088
4099  - http://biorxiv.org/content/early/2020/07/30/2020.07.30.229088.short
4100  - http://biorxiv.org/content/early/2020/07/30/2020.07.30.229088.full
AB  - Genome editing experiments are generating an increasing amount of targeted sequencing data with specific mutational patterns indicating the success of the experiments and genotypes of clonal cell lines. We present EdiTyper, a high-throughput command line tool specifically designed for analysis of sequencing data from polyclonal and monoclonal cell populations from CRISPR gene editing. It requires simple inputs of sequencing data and reference sequences, and provides comprehensive outputs including summary statistics, plots, and SAM/BAM alignments. Analysis of simulated data showed that EdiTyper is highly accurate for detection of both single nucleotide mutations and indels, robust to sequencing errors, as well as fast and scalable to large experimental batches. EdiTyper is available in github (https://github.com/LappalainenLab/edityper) under the MIT license.Competing Interest StatementT.L. is a member of the scientific advisory board of Goldfinch Bio and Variant Bio, and has equity in Variant Bio.