MCSS: microbial community simulator based on structure

Xingqi Hui; Jinbao Yang; Jinhuan Sun; Fang Liu; Weihua Pan

doi:10.3389/fmicb.2024.1358257

MCSS: microbial community simulator based on structure

Front Microbiol. 2024 Mar 7:15:1358257. doi: 10.3389/fmicb.2024.1358257. eCollection 2024.

Authors

Xingqi Hui^#^{1

2}, Jinbao Yang^#^{2

3}, Jinhuan Sun⁴, Fang Liu^{1

5}, Weihua Pan²

Affiliations

¹ Zhengzhou Research Base, State Key Laboratory of Cotton Biology, School of Agricultural Sciences, Zhengzhou University, Zhengzhou, China.
² Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences (ICR, CAAS), Shenzhen, China.
³ College of Informatics, Huazhong Agricultural University, Wuhan, China.
⁴ Key Laboratory of Plant Molecular Physiology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Botany, Chinese Academy of Sciences, Beijing, China.
⁵ National Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences (ICR, CAAS), Anyang, China.

^# Contributed equally.

Abstract

De novo assembly plays a pivotal role in metagenomic analysis, and the incorporation of third-generation sequencing technology can significantly improve the integrity and accuracy of assembly results. Recently, with advancements in sequencing technology (Hi-Fi, ultra-long), several long-read-based bioinformatic tools have been developed. However, the validation of the performance and reliability of these tools is a crucial concern. To address this gap, we present MCSS (microbial community simulator based on structure), which has the capability to generate simulated microbial community and sequencing datasets based on the structure attributes of real microbiome communities. The evaluation results indicate that it can generate simulated communities that exhibit both diversity and similarity to actual community structures. Additionally, MCSS generates synthetic PacBio Hi-Fi and Oxford Nanopore Technologies (ONT) long reads for the species within the simulated community. This innovative tool provides a valuable resource for benchmarking and refining metagenomic analysis methods. Code available at: https://github.com/panlab-bio/mcss.

Keywords: assembly; long reads; metagenome; microbiome communities; simulator.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work has been supported by the Innovation Program of Chinese Academy of Agricultural Sciences, Shenzhen Science and Technology Program (grant no. RCBS20210609103819020) and the National Natural Science Foundation of China (grant no. 32100501).