McAN: a novel computational algorithm and platform for constructing and visualizing haplotype networks
Lun Li, Bo Xu, Dongmei Tian, Anke Wang, Junwei Zhu, Cuiping Li, Na Li, Wei Zhao, Leisheng Shi, Yongbiao Xue, Zhang Zhang, Yīmíng Bào, Wenming Zhao, Shuhui Song
Abstract
Haplotype networks are graphs used to represent evolutionary relationships between a set of taxa and are characterized by intuitiveness in analyzing genealogical relationships of closely related genomes. We here propose a novel algorithm termed McAN that considers mutation spectrum history (mutations in ancestry haplotype should be contained in descendant haplotype), node size (corresponding to sample count for a given node) and sampling time when constructing haplotype network. We show that McAN is two orders of magnitude faster than state-of-the-art algorithms without losing accuracy, making it suitable for analysis of a large number of sequences. Based on our algorithm, we developed an online web server and offline tool for haplotype network construction, community lineage determination, and interactive network visualization. We demonstrate that McAN is highly suitable for analyzing and visualizing massive genomic data and is helpful to enhance the understanding of genome evolution. Availability: Source code is written in C/C++ and available at https://github.com/Theory-Lun/McAN and https://ngdc.cncb.ac.cn/biocode/tools/BT007301 under the MIT license. Web server is available at https://ngdc.cncb.ac.cn/bit/hapnet/. SARS-CoV-2 dataset are available at https://ngdc.cncb.ac.cn/ncov/. Contact: [email protected] (Song S), [email protected] (Zhao W), [email protected] (Bao Y), [email protected] (Zhang Z), [email protected] (Xue Y).