Contamination Survey of Insect Genomic and Transcriptomic Data.

Area of Science:

• Genomics
• Bioinformatics
• Zoology

Background:

• High-throughput sequencing generates vast amounts of data, increasing the risk of contamination from non-target species.
• Insecta, a highly diverse arthropod group, requires thorough evaluation of contamination in public genomic and transcriptomic databases.
• Understanding contamination sources is crucial for accurate biodiversity and evolutionary studies.

Purpose of the Study:

• To evaluate contamination prevalence in insect whole-genome sequencing (WGS) and transcriptomic (TSA) data from GenBank.
• To analyze potential causes of insect and mammal contamination in genomic datasets.
• To propose a workflow for detecting and mitigating contamination in sequencing data.

Main Methods:

• Utilized Cytochrome c Oxidase subunit I (COI) barcodes for contamination detection.
• Analyzed 2796 WGS and 1382 TSA assemblies across four major insect orders.
• Investigated contamination from insects and mammals within the datasets.

Main Results:

• Contamination was detected in 1.14% of WGS and 11.0% of TSA assemblies.
• TSA data showed significantly higher contamination rates compared to WGS data.
• Contamination varied by insect order: Hemiptera (9.22%), Hymenoptera (7.66%), Coleoptera (3.48%), and Diptera (1.89%).
• Potential contamination sources including food, parasitism, and cross-contamination were identified.

Conclusions:

• Transcriptomic data present a higher contamination risk than whole-genome data in insects.
• Contamination levels are order-specific, necessitating tailored detection methods.
• A standardized workflow and mitigation strategies are proposed to improve data quality in public repositories.