currently: computational biology phd student in richard morris' group, john innes centre, norwich, uk (sep. 2021 - present) last seen working as: - graphic design and web dev intern at iceland ocean cluster, reykjavík, iceland (mar. 2023 - may 2023) - bioinformatician in steve penfield's lab, john innes centre, norwich, uk (jul. 2021 - sep. 2021)
i am a computational biologist passionate about using mathematical and computational frameworks to solve biological problems. i can write decent quality code, can train a machine learning model or two and wrangle large datasets. i am always eager learn more about cool techniques and computers in general. i can also work in the lab, without destroying everything, if required. when i am not doing science, i like to pretend i am a graphic designer that can hike tall masses of land. if you are interested in collaborating or have any questions, please feel free to reach out to me. for my full cv, please click here or for a quick overview, continue reading. takk!
email: gurpinder-singh.sidhu@jic.ac.uk github: github.com/Gurpinder98 blueSky: @grpndr.bsky.social linkedin: linkedin.com/in/gurpinder-s-s-a75569268/
technical skills: bioinformatics (experience with sequencing data, transcriptomic data, time-series data), data analysis, machine learning (experience with tensorflow, gpflow, keras), web development (experience with flask, html, css, javascript, wordpress), git, hpc (experience with slurm, snakemake) programming and scripting: python (★★★★★), r (★★★★★), bash (★★★★☆), c (★★★☆☆), rust (★★☆☆☆) lab skills: plant biology (tissue sampling), molecular biology (rna isolation), microscopy, data collection
- regulation of flowering time within brassica napus. (phd project). i am currently working on inferring flowering time gene regulatory networks in brassica napus using time-series gene expression data. as part of this project, i have implemented gene regulatory network inference methods (see github), a reciprocal blast utility for gene mappings (see github) and helped in testing of a new curve registration method (see greatR). - a bayesian framework for differential gene expression. i collaborated with a team of researchers to develop a bayesian framework for ranking genes based on their statistical evidence for differential expression. i contributed to the development of the framework, the testing of method with available r packages (see github) and the writing of the manuscript. for full list, see my cv.
- Hoerbst F, Sidhu GS, Tomkins M, Morris RJ. What is a differentially expressed gene? bioRxiv. 2025. doi: 10.1101/2025.01.31.635902 - Hoerbst F, Sidhu GS, Omori T, Tomkins M, Morris RJ. A Bayesian framework for ranking genes based on their statistical evidence for differential expression. bioRxiv. 2025. doi: 10.1101/2025.01.20.633909 for the full list, see my orcid or my cv.