TagDigger: user-friendly extraction of read counts from GBS and RAD-seq data

Area of Science:

• Genomics
• Bioinformatics
• Population Genetics

Background:

• Read depth is crucial for genotype quality and allele dosage estimation in genotyping-by-sequencing (GBS) and restriction site-associated DNA sequencing (RAD-seq).
• Existing GBS and RAD-seq pipelines lack accurate and accessible read count formats.
• Current pipelines offer limited flexibility in selecting specific loci for analysis and struggle with standardized SNP naming for cross-project meta-analysis.

Purpose of the Study:

• To develop user-friendly software for accurate read count extraction and genotype calling in GBS and RAD-seq data.
• To enable flexible analysis by allowing users to specify subsets of loci for examination.
• To facilitate cross-project meta-analysis by consolidating marker names and sequences.

Main Methods:

• Developed TagDigger, a suite of three Python 3 scripts: tagdigger_interactive.py, tag_manager.py, and barcode_splitter.py.
• tagdigger_interactive.py extracts read counts and genotypes from FASTQ files, supporting user-defined barcodes and tags, with CSV input/output.
• tag_manager.py consolidates marker information across projects, while barcode_splitter.py prepares FASTQ files for data archival.

Main Results:

• TagDigger provides accurate read counts and genotypes in an accessible CSV format.
• The software supports importing tag sequences from popular pipelines (Stacks, TASSEL-GBSv2, TASSEL-UNEAK, pyRAD) and allows manual selection of loci.
• TagDigger processes over 100 million FASTQ reads per hour using a scalable, rapid search algorithm.

Conclusions:

• TagDigger is an open-source, freely available software package that enhances GBS and RAD-seq data analysis.
• It offers a user-friendly interface requiring no programming skills and runs on standard operating systems.
• The software efficiently processes large datasets without generating excessive intermediate files, making it suitable for laptops.