Evaluation and comparison of multiple aligners for next-generation sequencing data analysis.
Jing Shang1, Fei Zhu2, Wanwipa Vongsangnak3
1Center for Systems Biology, Soochow University, 1st Shizi Street, Suzhou, Jiangsu 215006, China ; Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, China.
Biomed Research International
|April 30, 2014
Summary
Selecting the right next-generation sequencing (NGS) aligner is crucial for accurate data analysis. This study systematically compares NGS aligner performance, offering guidance for biologists to choose optimal tools for their specific research needs.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Next-generation sequencing (NGS) generates vast amounts of data.
- Biologists often select NGS aligners without considering performance or accuracy.
- Reference genomes may contain sequence variations and polymorphisms affecting alignment.
Purpose of the Study:
- To systematically evaluate and compare the capabilities of multiple NGS data analysis aligners.
- To classify alignment algorithms for better understanding.
- To provide a guiding resource for selecting appropriate aligners.
Main Methods:
- Comparative analysis of multiple NGS aligners.
- Classification of alignment algorithms.
- Evaluation using long-read and short-read datasets (real-life and in silico).
- Assessment based on alignment features, computational performance, and accuracy.
Main Results:
- Demonstrated a comprehensive evaluation and comparison of various NGS aligners.
- Provided insights into the performance differences of aligners.
- Highlighted the importance of aligner selection based on specific criteria.
Conclusions:
- The study offers a valuable resource for biologists needing to select NGS aligners.
- Informed selection of aligners can improve the accuracy and efficiency of NGS data analysis.
- Understanding aligner capabilities is key for diverse genomic applications.
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