Python-based automation of INDIGO webserver using Selenium: A high throughput analysis of Sanger sequence data to detect allelic variations created by CRISPR/Cas9-mediated genome editing of crop plants

Abstract

CRISPR-Cas9 has revolutionized plant genome editing by enabling precise introduction of insertion/deletion (indel) mutations, critical for functional genomics and crop improvement studies. Sanger sequencing, combined with bioinformatics tools like the INDIGO webserver from Gear Genomics, is essential for validating these mutations. However, manual analysis of large numbers of Sanger sequencing (.ab1) files is labor-intensive, particularly when analyzing multiple guide RNA (gRNA) target regions. We developed a Python-based automation pipeline using Selenium with integrated highlighting of protospacer adjacent motif (PAM) regions in the resulting HTML reports. This pipeline enhances scalability of Sanger sequence data analysis and improves result interpretability by automating PAM region identification and supporting multiple gRNA regions. This tool significantly accelerates CRISPR-Cas9-mediated mutation analysis, offering a high throughput, reproducible solution for genome editing research.