Chopper

Chopper processes and filters long-read sequencing FASTQ data from Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio) to prepare high-quality datasets for downstream analyses.

Key Features:

• FASTQ filtering and trimming: Performs filtering and trimming of FASTQ files to remove low-quality reads and prepare data for downstream analysis.
• Scalability for large cohorts: Designed for efficient processing of large volumes of long-read sequencing data to support large-scale sequencing projects.
• Rust implementation: Implemented in Rust to provide fast execution and memory safety, offering performance and stability advantages compared to Python-based tools such as NanoFilt and NanoLyse.
• Integration with NanoPack suite: Integrates with NanoPack tools including cramino, kyber, and phasius for complementary quality assessment, summarization, and phased-alignment visualization.
• Open source and continuous testing: Released under the MIT license and continuously tested using GitHub Actions.

Scientific Applications:

• Quality assessment: Filters out low-quality reads from ONT and PacBio FASTQ datasets to improve the reliability of downstream analyses.
• Data summarization and visualization: Contributes to summarization and visualization of long-read sequencing experiments as part of the NanoPack suite.
• Phased alignments and structural variation/haplotype analysis: Supports workflows for phased alignment visualization and analyses relevant to structural variation and haplotype studies via integration with phasius.

Methodology:

Performs filtering and trimming of FASTQ files from ONT and PacBio long-read sequencing and is implemented in Rust.