APSCALE

APSCALE processes DNA metabarcoding sequencing data for read processing, OTU clustering, and denoising to support biodiversity assessment and ecological monitoring.

Key Features:

• Implementation: Written in Python.
• Paired-End Merging: Combines paired-end reads to produce assembled sequences.
• Primer Trimming: Removes primer sequences from raw reads.
• Quality Filtering: Filters out low-quality reads based on user-defined thresholds.
• OTU Clustering and Denoising: Performs operational taxonomic unit (OTU) clustering and denoising and supports markers including internal transcribed spacer (ITS), 16S rRNA, and cytochrome c oxidase subunit I (COI).
• OTU Filtering: Applies filters to refine OTU datasets according to specified criteria.
• Scalability: Implements multithreading to handle large datasets efficiently.
• Integration with Open-Source Tools: Integrates VSEARCH, cutadapt, and LULU to enhance processing performance and accuracy.
• Designed for High-Throughput Data: Targets processing of high-throughput sequencing metabarcoding datasets.

Scientific Applications:

• Biodiversity Assessment: Processes metabarcoding data for broad biodiversity surveys and inventories.
• Microbial Community Profiling: Enables large-scale analyses of microbial communities using 16S rRNA and ITS markers.
• Plant Species Surveys: Supports metabarcoding studies focused on plant species detection and monitoring.
• Animal Population Studies: Supports animal population and barcoding studies using COI marker data.
• Ecological Monitoring and Conservation: Facilitates ecological monitoring, conservation assessments, and environmental impact assessments using metabarcoding data.

Methodology:

Performs paired-end merging, primer trimming, quality filtering, OTU clustering and denoising, and OTU filtering; integrates cutadapt, VSEARCH and LULU; implements multithreading and is written in Python.