👋 Hi, I am Jing Qi - a bioinformatician-in-training currently pursuing MRes Biomedical Research, specialising in data science at Imperial College London. I am currently working on my second MRes project, focusing on Cancer and TCR sequencing. I am actively learning everything related to it.

🔬 Research background

During my recent MRes project, I developed computational pipelines to analyse 10x scMultiome data (snRNAseq + snATACseq). This involved implementing rigorous quality control and integration architectures, including doublet removals via SOLO, ambient RNA correction using ScAR, automated cell annotation with Azimuth and muti-omics joint embedding using MULTIVI. Through this work, I have developed a deep familiarity with the Scanpy, scverse, and Seurat ecosystems.

Previously, I was a research assistant in the Tenesa lab at the Roslin Institute, working on livestock epigenomics. There, I worked with Dr Pau Navarro, Prof James Prendergast and Prof Albert Tenesa to develop the first high-density DNA methylation array for cattle using a self-designed bioinformatics workflow. Before this, I studied for my BSc (Hons) Biological Sciences (Genetics) at the University of Edinburgh. In my final (honours) year, I undertook a bioinformatics-orientated project in The Wallace Lab, under the supervision of Dr Edward Wallace and Dr Sam Haynes. I characterised the binding activity of RNA-binding proteins (with a focus on Khd1) in budding yeast, Saccharomyces cerevisiae. In the summer of 2021, I also completed a two-month placement with Prof James Prendergast,examining a Boran genome to identify a potential causal variant conferring tolerance to East Coast Fever.

Across these projects, I developed a strong background in bioinformatics, particularly in next-generation sequencing data analysis and bioinformatics pipeline development. I have also dabbled in applying machine learning models to multi-omics.

🔎 Scientific interest

I have a profound interest in understanding the gene regulatory mechanisms that drive phenotypic heterogeneity among individuals, particularly within disease contexts. I am interested in uncovering the functional impacts of genetic and epigenetic variants, thereby contributing to genomic medicine and driving progress in precision healthcare.