RabbitQCPlus
RabbitQCPlus accelerates quality control of sequencing data to improve processing speed and accuracy for downstream genomic analyses.
Key Features:
- Performance Optimization: Engineered for ultra-efficient multi-core processing using vectorization, memory copy reduction, parallel (de)compression, and optimized data structures, delivering 1.1–5.4× speedups in basic QC operations versus state-of-the-art tools.
- Compressed FASTQ Handling: Processes gzip-compressed FASTQ files at least four times faster than other available applications.
- Error Correction Efficiency: The error correction module yields a 1.3-fold increase in processing speed compared with competing tools.
- Over-Representation Analysis: Performs per-read over-representation analysis with high throughput, processing 280 GB of plain FASTQ data in under four minutes on a multi-core server versus at least 22 minutes for other applications.
Scientific Applications:
- Genomic quality control for high-throughput sequencing: Accelerates QC of large sequencing datasets to support reliable downstream analyses in genomics research.
- Sequencing data preprocessing: Streamlines QC steps in sequencing pipelines to reduce computational bottlenecks prior to downstream analyses such as variant calling or expression quantification.
Methodology:
RabbitQCPlus applies vectorization, memory copy reduction, parallel (de)compression on multi-core architectures, and optimized data structures to accelerate sequencing quality-control computations.
Topics
Details
- License:
- MIT
- Cost:
- Free of charge
- Tool Type:
- command-line tool
- Operating Systems:
- Linux
- Programming Languages:
- C++
- Added:
- 1/29/2024
- Last Updated:
- 1/29/2024
Operations
Data Inputs & Outputs
Publications
Yan L, Yin Z, Zhang H, Zhao Z, Wang M, Müller A, Kallenborn F, Wichmann A, Wei Y, Niu B, Schmidt B, Liu W. RabbitQCPlus 2.0: More efficient and versatile quality control for sequencing data. Methods. 2023;216:39-50. doi:10.1016/j.ymeth.2023.06.007. PMID:37330158.
PMID: 37330158
Funding: - National Natural Science Foundation of China: 61972231, 62102231
- Natural Science Foundation of Shandong Province: ZR2021QF089
- Deutsche Forschungsgemeinschaft: 439669440 TRR319 RMaP TP C01