Bioinformatician - Genomics & Pipelines
MSc Bioinformatics & Systems Biology | University of Manchester 🇬🇧
Open to global opportunities
I am a Bioinformatician specialising in the analysis of high-dimensional genomic data, including NGS (WES/WGS) and Single-cell transcriptomics. I focus on developing reproducible, scalable pipelines using Python, R, and Nextflow to interpret complex biological datasets and prioritise variants for precision medicine. Currently, I am leading the design of high-throughput analytics pipelines that bridge the gap between large-scale data engineering and biological discovery.
- Scalable WES Pipelines: Deploying reproducible variant calling workflows on HPC environments using SLURM to identify pathogenic hits in rare disease cohorts.
- Cancer Transcriptomics: Utilising TCGAbiolinks and DESeq2 to identify differentially expressed genes (DEGs) and validating them via OncoKB.
- Multi-Omics Annotation: Designing frameworks for GWAS SNP prioritization using FUMA, eQTL mapping, and BioMart.
- Single-Cell Atlases: Analysing scRNA-seq datasets to identify cell-type-specific regulatory factors and transcription factors across developmental stages.
- Reproducible Research: Building research-grade pipelines that follow Ensembl best-practice guidelines.
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Omics Expertise: WES/WGS, bulk/single-cell RNA-seq, Spatial Transcriptomics, GWAS, and Multi-omics Integration.
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Programming: Python (Pandas, Scikit-learn, TensorFlow), R (Bioconductor, Tidyverse), Bash, SQL, and Git/GitHub.
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Pipeline Orchestration: Nextflow, Snakemake, and Linux/Unix environments.
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NGS Tool Stack:
- Alignment & Processing: STAR, BWA, Bowtie, SAMtools, Picard, and BEDTools.
- Variant Calling & Annotation: GATK, bcftools, VEP, GTEx, and GWAS Catalog.
- Differential Expression & GWAS: DESeq2 and PLINK.
- Computation & Cloud: SLURM, HPC environments, and AWS (EC2, S3, Lambda).
- Machine Learning: Supervised/Unsupervised Learning, Deep Learning for omics, and Statistical Validation of ML Models.
- Product Analytics: Amplitude tracking, Looker Studio, and Google Analytics.
- Visualisation: R (ggplot2, Shiny), Python (Matplotlib, Plotly), Tableau, and Power BI.
I write about trends and deep-dive workflows and tutorials in bioinformatics, translating biological complexity into analytical clarity.
I actively contribute curated datasets and code with real-world relevance:
- Drugs, Conditions & Side Effects: (5.5K+ views | 1.2K+ downloads).
- Life Expectancy Analysis: (6K+ views | 1.5K+ downloads).
I am open to collaborations and research roles in Bioinformatics, Genomics, and Precision Medicine.
"In a world full of noise, I turn complex biological data into meaningful signals."