Meta-analysis of Sublethal Pesticide Exposure Effects on Gene Expression of Honey Bees

2021-2022 Fall
Faculty Department of Project Supervisor: 
Faculty of Engineering and Natural Sciences
Number of Students: 


With the rise of industrial agriculture to meet the demand of the growing world population,

the reliance on pesticides to control for pests is ever-increasing. Pesticides target pests, such

as insects, that are considered harmful for crops. However, pesticides also have lethal and

sublethal effects on non-target groups such as honey bees. Therefore, pesticide exposure

contributes to the decline in honey bee health and negatively impacts the global economy.

There are a variety of pesticides that are proven to have a detrimental effect on honey bee

health. It has been established that pesticide exposure affects negatively olfactory memory

and learning, orientation, foraging activity, flying behavior, and navigation capacity. Recent

studies pinpointed genes and pathways that are affected due to sublethal exposure of various

types of pesticides individually in honey bees using transcriptomics. However, there are

different types and classes of pesticides, each having a different mode of action. Furthermore,

some genes identified to be differentially regulated due to sublethal pesticide exposure

showed variability in expression across different studies. The aim of this project is to identify

subtle changes in gene expression and highlight commonalities in honey bee response to

sublethal pesticide exposure by analyzing multiple RNA-seq datasets of honey bees exposed

to various types and classes of pesticides.



• Proficient at using computers

• Basic knowledge in statistics

• Basic knowledge in R programming language

• Familiarity with next-generation sequencing (NGS)



• Basic knowledge in Unix command line

• Basic knowledge in RNA-seq



• Retrieval of NGS data from NCBI GEO, NCBI SRA, and ENA

• RNA-seq data analysis tools and pipelines

• Working in HPC environment and Unix command line

• R and some of its famous packages

• Exploratory data analysis

• Differential gene expression analysis

• Gene set and pathway enrichment analysis



• Searching for appropriate datasets and cataloging them

• Processing of raw RNA-seq data

• Performing exploratory data and differential gene expression analysis

• Performing pathway enrichment analysis


*Note: this project will be conducted online.

Related Areas of Project: 
Computer Science and Engineering
Molecular Biology, Genetics and Bioengineering

About Project Supervisors



Molecular Biology, Genetics and Bioengineering Program
Faculty of Engineering and Natural Sciences

Office: FENS 2061



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