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AdapterRemoval v2: rapid adapter trimming, identification, and read merging
Mikkel Schubert1, Stinus Lindgreen2,3, Ludovic Orlando4,5
1Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, 1350, Copenhagen, Denmark. MikkelSch@gmail.com.
BMC Research Notes
|February 13, 2016
Summary
AdapterRemoval v2 significantly enhances throughput for adapter trimming in high-throughput sequencing data. This improved tool efficiently processes large datasets, handling diverse adapter types and enabling simultaneous demultiplexing.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- High-throughput sequencing generates vast amounts of data, often containing adapter sequences, especially from degraded samples.
- Efficient adapter trimming is crucial for accurate analysis of sequencing data.
Purpose of the Study:
- To introduce AdapterRemoval v2, a substantially revised and improved version of the AdapterRemoval software.
- To enhance the efficiency and capabilities of adapter trimming and demultiplexing in next-generation sequencing data analysis.
Main Methods:
- AdapterRemoval v2 utilizes single instruction, multiple data (SIMD) instructions (SSE1 and SSE2) and multi-threading for increased throughput.
- The software supports datasets with varying adapter configurations and enables simultaneous demultiplexing and adapter trimming.
- It includes functionality to reconstruct adapter sequences from paired-end reads and offers native support for gzip and bzip2 compressed files.
Main Results:
- AdapterRemoval v2 demonstrates striking improvements in processing throughput compared to its predecessor.
- The tool efficiently handles datasets with diverse adapter types and facilitates simultaneous demultiplexing.
- It offers superior throughput in both single-threaded and multi-threaded operations when compared to most existing tools.
Conclusions:
- AdapterRemoval v2 provides a significant advancement in adapter trimming software for next-generation sequencing.
- The tool offers competitive performance and superior throughput, making it a valuable resource for processing large sequencing datasets.
- Its enhanced features, including improved speed and flexibility, address the growing demands of modern genomics research.
