Students will identify gene regulatory sequences in insect genomes using the "SCRMshaw" software pipeline developed in my laboratory, and/or work on improvements to this pipeline.
This project is not being offered for the current term. Please check back next semester for updates.
Identifying and studying the genomic sequences responsible for gene regulation—commonly referred to as "enhancers"—is an essential adjunct to sequencing the genomes themselves. Enhancers play a major role in determining how genotype influences phenotype and strongly impact disease, homeostasis, evolution, and development. SCRMshaw (“Supervised Cis-Regulatory Module prediction”) is a machine-learning approach with proven effectiveness for discovering enhancers in both insect and mammalian genomes. We have applied it to over 33 insect genomes to date, and an important ongoing project in our laboratory is to continue with additional species. This activity is important both to the larger insect genomics community and to our own studies of regulatory evolution. We also have ongoing projects to improve the SCRMshaw pipeline, including by improving pre-processing of input sequences, constructing better training data for the machine-learning algorithm, and improving the code. Depending on the skills and interest of the student, they will participate in one or more of these activities. Students will be expected to present their research at lab meetings as well as at departmental research symposia. Students will also be expected to assist in writing up their successful research for future publication.
Students participating in this project should have a basic background in both biology and computers. Although extensive coding skills are not required, a general familiarity with computing is necessary. Students are expected to be able to work independently and to put in the time necessary to achieve their project goals.
Students will participate in weekly lab meetings. These meetings alternate between presentation and discussion of research results, and journal clubs. The students will be expected to participate in both activities. For the former, students will need to present a coherent overview of the background and rationale for their research, their findings, and any problems they have encountered along the way. Students will also be guided in writing up their results for use in eventual publication. Several previous undergraduates from our laboratory have been authors on peer-reviewed publications, including first author. If progress is sufficient, the students may be invited to submit their work for presentation at an international-scale meeting during the summer months.
Length of commitment | Longer than a semester; 6-9 months |
Start time | Spring (January/February 2025) Summer (May/June 2025) |
In-person, remote, or hybrid? | Hybrid Project (can be remote and/or in-person; to be determined by mentor and student) |
Level of collaboration | Individual Student Project |
Benefits | Stipend |
Who is eligible | All undergraduate students with a basic background in both biology and computers, general familiarity with computing, extensive coding skills preferred. Students are expected to be able to work independently and to put in the time necessary to achieve their project goals. |
Once you begin the digital badge series, you will have access to all the necessary activities and instructions. Your mentor has indicated they would like you to also complete the specific preparation activities below. Please reference this when you get to Step 2 of the Preparation Phase.
Students should demonstrate comfort with the Unix/Linux command line and familiarity in simple coding skills, preferably using Python or R. If students have not completed CSE115/116, proof of necessary skills should be provided.
Interested students should review the attached paper. If the methods described therein appear too difficult, this will not be an appropriate project. It is OK if not all of the scientific details make sense, as long as the methods fall within the student's skill set.
biochemistry, bioinformatics, genomics, computational biology, evolution, gene regulation, Jacobs School of Medicine and Biomedical Sciences