MSc Bioinformatics (Teesside University, 2025) with 3+ years of clinical laboratory experience spanning molecular diagnostics, antimicrobial resistance research, and microbiome analysis. I build automated pipelines for bacterial genomics and NGS data processing, and I understand the wet-lab context behind the data — having generated a significant portion of it myself.

My work sits at the intersection of computational genomics and applied microbiology: building reproducible workflows, interrogating large-scale genomic datasets, and translating findings into outputs that mean something for AMR surveillance and clinical diagnostics.

Open to opportunities in bioinformatics analysis, NGS pipeline development, and genomics research roles across the UK. Get in touch.

Production-ready Snakemake workflow for large-scale bacterial defence system analysis

Acinetobacter baumannii is one of the WHO's priority pathogens — notorious for acquiring resistance and evading clinical interventions — but the relationship between its phage defence systems and antibiotic resistance gene carriage was poorly characterised. I built an end-to-end automated pipeline to investigate this at scale.

What it does: Retrieves genomes from NCBI, runs DefenseFinder and PADLOC for defence system prediction, ResFinder for resistance gene identification, and integrative mobile element (IME) prediction — then outputs structured result tables ready for statistical analysis. Fully reproducible across fresh environments.

Stack: Snakemake · Python · Bash · DefenseFinder · PADLOC · ResFinder · CRISPRCasFinder

Scale: Validated across 500+ Acinetobacter genomes

Published: Findings from this pipeline contributed to a first-author publication in Journal of Applied Microbiology (2026)

R-based statistical analysis of defence system architecture and AMR correlations

Companion repository to the pipeline above. Takes the structured output and performs species-level comparative analysis, co-occurrence testing, and correlation mapping between defence system presence, resistance gene load, and mobile genetic element distribution.

Key finding: Specific defence systems — particularly Gao_Qat — co-occur with multiple resistance determinants at rates significantly above background, suggesting shared genomic neighbourhoods that may facilitate simultaneous acquisition of defence and resistance. SspBCDE was consistently enriched in A. baumannii clinical isolates, implicating it as a factor in this pathogen's clinical persistence.

Stack: R · DESeq2 · ggplot2 · Statistical correlation and enrichment analysis

Reproducible HiFi and HiFi+Hi-C assembly pipelines across three organisms of increasing genomic complexity

Assembly strategy is not universal — ploidy, heterozygosity, and available data types all determine the right approach. This series of three end-to-end pipelines works through that decision space systematically: a haploid bacterial genome as a clean baseline, a diploid fungal pathogen (C. albicans) assembled with HiFi reads only, and a diploid yeast (S. cerevisiae) assembled to chromosome level using HiFi + Hi-C phasing data.

The key technical finding: hifiasm's --primary mode produces a functional assembly from a diploid genome without phasing data, but the 209-contig output and complex heterozygous bubble graph for C. albicans make the limitation tangible. Adding Hi-C chromatin contact maps for S. cerevisiae resolves both haplotypes to chromosome-scale — 17 and 16 contigs with 0-edge contig graphs and N50 of ~923 kb — demonstrating where HiFi alone is sufficient and where it is not.

Stack: hifiasm · seqkit · NanoPlot · FastQC · QUAST · BUSCO (fungi_odb10 · saccharomycetes_odb10) · Bandage

Repos: E. coli HiFi · C. albicans diploid · S. cerevisiae Hi-C phased

Comparative 16S rRNA pipeline applied to 122 clinical diabetic wound samples

VSEARCH and QIIME2 are the two dominant tool choices for amplicon-based microbiome analysis, but their performance characteristics on the same clinical dataset are rarely documented directly. This pipeline applies both to 122 diabetic foot ulcer samples from the Jnana et al. (2020) dataset (Applied and Environmental Microbiology), quantifying where the methods agree and where they diverge.

Overall community composition shows strong concordance (Pearson r = 0.787 for dominant taxa), but the methods separate substantially on rare organisms — VSEARCH detected 820 low-abundance genera against QIIME2's 454, with 322 shared. For clinical microbiome work where rare opportunistic pathogens are relevant, that difference affects what gets reported.

Stack: Python · VSEARCH · QIIME2 · DADA2 · BLAST+ (SILVA) · FastQC · Trimmomatic · matplotlib · seaborn

Four peer-reviewed papers spanning genome defence systems, COVID-19 diagnostics, antimicrobial resistance, and microbiome analysis:

• Muthuraman V, Roy P, Dean P, Lopes BS, Shehreen S. (2026). The balance between defence systems and horizontal gene transfer shapes adaptation in clinical strains of Acinetobacter spp. Journal of Applied Microbiology, lxag069. DOI

• Takke A, Zarekar M, Muthuraman V, et al. (2022). Comparative study of clinical features and vaccination status in Omicron and non-Omicron infected patients during the 3rd wave in Mumbai. Journal of Family Medicine and Primary Care, 11(10), 6135–6142. DOI

• Daswani P, Muthuraman V, et al. (2020). Effect of Psidium guajava (guava) leaf decoction on antibiotic-resistant clinical diarrhoeagenic isolates of Shigella spp. International Journal of Enteric Pathogens, 8(4), 122–129. DOI

• Jnana A, Muthuraman V, et al. (2020). Microbial community distribution and core microbiome in successive wound grades of individuals with diabetic foot ulcers. Applied and Environmental Microbiology, 86(6), e02608-19. DOI

Before moving into computational work full-time, I spent three years in active research and clinical laboratory environments:

• ICMR – National Institute of Immunohaematology — scaled COVID-19 RT-qPCR testing from 100 to 300+ samples/day through workflow automation; built the QC monitoring infrastructure used across the lab's 24/7 operations
• The Foundation for Medical Research — AMR research on MDR Shigella; designed and validated the 96-well screening assay that underpins the published findings on guava leaf extract
• Manipal School of Life Sciences — characterised wound microbiome dynamics in diabetic foot ulcers using 16S rRNA sequencing and QIIME2; the dataset and pipeline from this work were published in Applied and Environmental Microbiology

This background shapes how I approach computational problems — I know what the data represents before it enters the pipeline, which changes the questions you ask of it.

MSc Bioinformatics — Teesside University (2025) MSc Molecular Biology & Human Genetics — Manipal University (2017)