Shubham Verma
Shubham verma Bioinformatics data science portfolio
Shubham Verma | Bioinformatics Data Scientist
🚀 Welcome to my portfolio showcasing bioinformatics projects, pipelines, and tools I’ve developed across clinical and research settings.
🔗 Topmate: topmate.io/shubham_verma96
🔗 1:1 Mentorship: topmate.io/shubham_verma96/page/NQ0wQCWFoh
👨💻 About Me
With 5+ years of experience in computational genomics, I specialize in developing scalable bioinformatics pipelines for large-scale NGS data analysis, translational research, and public health genomics. My work integrates high-throughput sequencing, cloud computing, and machine learning to advance infectious disease surveillance, antimicrobial resistance (AMR) detection, and microbiome analytics.
🔬 Current Focus Areas
- Infectious Disease Genomics – Whole genome sequencing (WGS) analysis for outbreak tracking and pathogen surveillance
- Pathogen Genomics: Bacterial whole-genome sequencing (WGS) for outbreak tracking, phylogenomics, and AMR gene prediction (CARD, ResFinder, AMRFinder).
- Metagenomics: Taxonomic and functional profiling of microbiomes (16S rRNA, shotgun sequencing) using Kraken2, MetaPhlAn, HUMAnN3, and MetaWRAP.
- Microbiome Analytics: Machine learning-driven insights into host–microbiome interactions.
- Genome Assembly & Annotation: Hybrid (short + long-read) assembly with Unicycler, Flye, SPAdes, and annotation via Prokka, Bakta.
Currently based in Dubai, I work at the intersection of bioinformatics, precision medicine, and public health, leveraging computational tools to enhance pathogen surveillance and genomic medicine
🛠️ Technical Skills
- Languages: Python, R, Bash, SQL
- NGS & Omics Tools:
- Bacterial Genomics: Unicycler, Flye, Canu, Shovill, QUAST, ABRicate, Roary, Prokka, Snippy.
- Metagenomics: Kraken2, MetaPhlAn, MEGAHIT, MetaWRAP, DESeq2, LEfSe, Qiime2.
- Variant Analysis: GATK, BCFtools, Snippy, bcftools.
- Pipeline Development: Nextflow, Docker, Snakemake
- Cloud & Compute: AWS (Batch, S3, EC2), HPC, Git
- ML/AI for Genomics: XGBoost, scikit-learn, Random Forest (applied to 16S and genomic data)
- Visualization: Seaborn, Plotly, ggplot2, RMarkdown
🔗 Connect With Me
- LinkedIn: shubham-verma96
- Google Scholar: scholar.google.ae/citations?user=JvWXI4wAAAAJ