A Comparative Study of Sequence Clustering Algorithms
Sequence clustering software is essential in bioinformatics. However, selecting the appropriate one can be challenging due to its diverse algorithms and targeted applications. This paper analyzes and evaluates eight representative softwares (algorithms) in terms of precision, sensitivity, speed, scale of running time, and memory consumption. Furthermore, this paper examines the effects of sequence count, sequence length, identity, thread count, and GPU on the above aspects. Sequence length and identity significantly impact clustering efficiency (speed and memory consumption), with fluctuation amplitudes exceeding an order of magnitude and non-monotonic effects observed. The evaluation results are analyzed and summarized in tables for users' reference.
Authors
- Dian Huang (ORCID: https://orcid.org/0000-0002-7853-1747)
- Yanjie Wei (ORCID: https://orcid.org/0000-0002-4791-7540)
- Jintao Meng (ORCID: https://orcid.org/0000-0002-6208-4102)
- Zhen Ju (ORCID: https://orcid.org/0000-0001-7720-1570)
- Wenhui Xi (ORCID: https://orcid.org/0000-0003-4272-7518)
- Huiling Zhang (ORCID: https://orcid.org/0000-0002-7856-2703)
- Shengzhong Feng (ORCID: https://orcid.org/0000-0002-6225-2815)
- Jingjing Zhang (ORCID: https://orcid.org/0000-0002-9884-4230)
Institutions
- South China Agricultural University (CN)
- Shenzhen Institute of Information Technology (CN)
- Chinese Academy of Sciences (CN)
- Guangdong Institute of Intelligent Manufacturing (CN)
- Shenzhen Institutes of Advanced Technology (CN)
Publication Details
- Journal
- Big Data Mining and Analytics
- Published
- 2025-07-14
- DOI
- https://doi.org/10.26599/bdma.2025.9020010
- Citations
- 2
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 1.61
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China