Hormones, particularly androgens (like testosterone) and estrogens, play a pivotal role in the progression of diseases such as prostate and breast cancer. Our laboratory aims to elucidate the molecular mechanisms of hormone action that affect carcinogenesis. To that end, we routinely employ knockdown or knockout experiments in conjunction with omics techniques, including RNA-sequencing and proteomics, to discern molecular interplays. Individual gene perturbations can influence the expression of numerous genes, making the analysis of subsequent omics-derived gene lists challenging. To address this, our laboratory developed GeneSetR (https://www.biorxiv.org/content/10.1101/2023.09.18.558211v1), a web server equipped to analyze user-defined gene lists utilizing data from a recently published genome-wide Perturb-Seq study (1). This MSc project will center on further development and refinement of GeneSetR with an important bioinformatics component. Thus, only those highly motivated students with some informatics background should consider this project.
Background
Perturb-Seq is a powerful new method for studying gene-phenotype relationships (1,2). It allows researchers to investigate the consequences of large numbers of genetic perturbations on gene expression profiles at the single-cell level. This has led to new insights into a wide range of biological processes, including ER stress signaling, innate immunity, and cell cycle regulation (2-4).
GeneSetR is a pioneering new tool for analyzing gene lists based on Perturb-Seq data. It overcomes many of the limitations of traditional gene list analysis tools by focusing on specific Perturb-Seq experiments. This approach mitigates reliance on broad, lowly curated databases that are likely contaminated and thus decrease reliability of analysis results. By focusing on the specific and highly controlled dataset of genome wide Perturb-Seq (GWPS), GeneSetR offers a novel tool for gene list analysis.
Given the rapid advancement and the decreasing costs associated with next-generation sequencing, there is a projected surge in GWPS across diverse cellular contexts. To capitalize on this data ‘explosion’ in a timely manner, we developed GeneSetR, a novel web server specifically designed for efficient querying of Perturb-Seq data. We are keen to augment the capabilities of GeneSetR. The envisaged next phase of its development involves integration of sophisticated visualization libraries, annotations from other prominent datasets, and collaboration with a wider developer community. This will allow new modes of analysis and can result in novel hypotheses that can then be tested in the laboratory.
We are seeking a highly-motivated MSc candidate to contribute to this project.
As an MSc student in this project, you will have the opportunity to:
- Gain hands-on experience to work with Perturb-Seq data and gene set analysis to develop hypotheses.
- Develop your programming skills and learn to use a variety of bioinformatics tools
- Get familiar with pivotal molecular biology databases
- Be co-author on a publication(s) in a high impact journal
We are looking for a student with the following qualifications:
- Familiarity with web application development (e.g. React, Javascript and Python)
- Experience with data analysis and visualization tools
- Interest in molecular biology/bioinformatics
- Prior experience in web server or application development will be considered a plus
The chosen candidate will work closely with a postdoctoral fellow. For a more detailed exposition on GeneSetR and the scope of the project, potential candidates are directed to our dedicated website at https://genesetr.uio.no. For questions and more information, please do not hesitate to contact Omer Faruk Kuzu at o.f.kuzu@ibv.uio.no, Fahri Saatcioglu at fahris@ibv.uio.no,
Figure shows various modules of GeneSetR. For more information, please go to the website (https://genesetr.uio.no), and read our paper on Biorxiv (https://www.biorxiv.org/content/10.1101/2023.09.18.558211v1).
References
- Replogle JM, Saunders RA, Pogson AN, Hussmann JA, Lenail A, Guna A, et al. Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq. Cell 2022;185:2559-75 e28
- Dixit A, Parnas O, Li B, Chen J, Fulco CP, Jerby-Arnon L, et al. Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens. Cell 2016;167:1853-66 e17
- Adamson B, Norman TM, Jost M, Cho MY, Nunez JK, Chen Y, et al. A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response. Cell 2016;167:1867-82 e21
- Jaitin DA, Weiner A, Yofe I, Lara-Astiaso D, Keren-Shaul H, David E, et al. Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq. Cell 2016;167:1883-96 e15