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EARRINGS: an efficient and accurate adapter trimmer entails no a priori adapter sequences
Ting-Hsuan Wang1, Cheng-Ching Huang1, Jui-Hung Hung1
1Department of Computer Science, College of Computer Science, National Chiao Tung University, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan.
Bioinformatics (Oxford, England)
|January 18, 2021
Summary
This study introduces EARRINGS, a novel adapter trimming tool for next-generation sequencing (NGS) data. EARRINGS accurately removes adapter sequences without prior knowledge, improving large-scale meta-analyses.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Next-generation sequencing (NGS) data preprocessing requires adapter trimming for accurate analysis.
- Current adapter trimming methods often rely on user-provided sequences, which can be inaccurate or unavailable.
- This limitation hinders large-scale meta-analyses and cross-sample comparisons.
Purpose of the Study:
- To develop a fast and accurate adapter detection and trimming algorithm that does not require prior adapter sequences.
- To provide a robust solution for adapter trimming in large-scale bioinformatics analyses.
- To enhance the reliability of meta-analyses using NGS data.
Main Methods:
- Developed novel adapter detection and trimming algorithms.
- Implemented algorithms in C++ utilizing SIMD and multithreading for speed.
- Benchmarked performance against existing adapter trimming tools.
Main Results:
- The EARRINGS software achieves comparable accuracy to existing methods without needing adapter sequences.
- EARRINGS demonstrates higher throughput, making it efficient for large datasets.
- The tool is particularly effective for meta-analyses involving numerous samples.
Conclusions:
- EARRINGS offers a significant advancement in adapter trimming for NGS data.
- The software facilitates more reliable and efficient large-scale genomic data analysis.
- EARRINGS is a valuable addition to sequence analysis pipelines.
