1. Predicting Insect Invasiveness with Whole-Genome Sequencing Data
- Author
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Cong Pian, Mingxing Jiang, Shu-ping Wang, Cong Huang, Jiapeng Luo, Wanqiang Qian, Fanghao Wan, Fei Li, Kun Lang, Daniel Simberloff, Nianwan Yang, Xi Li, Longsheng Xing, Xiaodan Fan, and Wanxue Liu
- Subjects
Whole genome sequencing ,media_common.quotation_subject ,fungi ,Computational biology ,Insect ,Biology ,media_common - Abstract
Background: Invasive alien insects threaten agriculture, biodiversity, and human livelihoods globally. Unfortunately, insect invasiveness still cannot be reliably predicted. Empirical policies of insect pest quarantine and inspection are mainly designed against species that are already problematic.Results: We conducted a comparative genomic analysis of 37 invasive insect species and six non-invasive insect species, showing that the gene families associated with defense, protein and nucleic acid metabolism, chemosensory function, and transcriptional regulation were significantly expanded in invasive insects, suggesting that enhanced abilities in self-protection, nutrition exploitation, and locating food or mates are intrinsic features conferring invasiveness in insects. By using these intrinsic genome features, we proposed an invasiveness index and estimated the invasiveness of 99 other insect species with genome data, classifying them as highly, moderately, or minimally invasive. Insects possessing all these aforementioned enhanced abilities are predicted to be highly invasive, and vice versa. Next, a logistic-regression classifier was trained to predict insect invasiveness, achieving 93.2% accuracy. Conclusions: We present evidence that several traits may confer invasiveness in insects and these features can be used to predict insect invasiveness accurately, and we quantify insect invasiveness with an invasiveness index.
- Published
- 2020
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