An Efficient Greedy Incremental Sequence Clustering Algorithm

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.