Abstract
Gene sequence clustering is very basic and important in computational biology and bioinformatics for the study of phylogenetic relationships and gene function prediction, etc. With the rapid growth of the amount of biological data (gene/protein sequences), clustering faces more challenges in low efficiency and precision. For example, there are many redundant sequences in gene databases that do not provide valid information but consume computing resources. Widely used greedy incremental clustering tools improve the efficiency at the cost of precision. To design a balanced gene clustering algorithm, which is both fast and precise, we propose a modified greedy incremental sequence clustering tool, via introducing a pre-filter, a modified short word filter, a new data packing strategy, and GPU accelerates. The experimental evaluations on four independent datasets show that the proposed tool can cluster datasets with precisions of 99.99%. Compared with the results of CD-HIT, Uclust, and Vsearch, the number of redundant sequences by the proposed method is four orders of magnitude less. In addition, on the same hardware platform, our tool is 40% faster than the second-place. The software is available at https://github.com/SIAT-HPCC/gene-sequence-clustering.
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Acknowledgment
This work was partly supported by the National Key Research and Development Program of China under Grant No. 2018YFB0204403, Strategic Priority CAS Project XDB38050100, National Science Foundation of China under grant no. U1813203, the Shenzhen Basic Research Fund under grant no. RCYX2020071411473419, KQTD20200820113106007 and JCYJ20180507182818013, CAS Key Lab under grant no. 2011DP173015. We would like to thank Intel for the tech support and resources such as oneAPI DevCloud in this study.
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Ju, Z. et al. (2021). An Efficient Greedy Incremental Sequence Clustering Algorithm. In: Wei, Y., Li, M., Skums, P., Cai, Z. (eds) Bioinformatics Research and Applications. ISBRA 2021. Lecture Notes in Computer Science(), vol 13064. Springer, Cham. https://doi.org/10.1007/978-3-030-91415-8_50
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DOI: https://doi.org/10.1007/978-3-030-91415-8_50
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