Alpseq: an open-source workflow to turbocharge nanobody discovery with high-throughput sequencing

Area of Science:

• Biotechnology and Bioinformatics
• Immunology and Molecular Biology

Background:

• Nanobodies offer advantages like small size and high specificity for biotechnological uses.
• Analyzing large nanobody next-generation sequencing (NGS) datasets poses significant bioinformatic challenges.
• Existing methods struggle with the scale and diversity of nanobody NGS data.

Purpose of the Study:

• To develop an efficient and accurate open-source software pipeline for processing nanobody NGS data.
• To provide researchers with a user-friendly tool for analyzing nanobody libraries and panning campaigns.
• To facilitate the identification of lead nanobody candidates for experimental validation.

Main Methods:

• Developed alpseq, an optimized, open-source software pipeline using Nextflow for pre-processing and an analysis module for quality control, diversity analysis, enrichment identification, and clustering.
• Paired the pipeline with a PCR-free sequencing library preparation protocol to minimize bias.
• Implemented a user-friendly interactive report for data exploration and support for sophisticated panning campaign designs.

Main Results:

• The alpseq pipeline efficiently processes raw nanobody reads and analyzes NGS data.
• The software provides comprehensive quality control, diversity metrics, and identifies enriched sequences.
• Interactive reports enable easy exploration of results, including identification of cross-binding leads from complex panning experiments.

Conclusions:

• alpseq offers an efficient, accurate, and user-friendly solution for nanobody NGS data analysis.
• The pipeline aids in understanding nanobody selection processes and discovering lead candidates.
• alpseq empowers researchers with diverse bioinformatic backgrounds to analyze their nanobody data effectively.