Reading themicrobial record.
I connect population-scale genome variation to the deep history of life, and build open tools that make evolutionary analyses easier to inspect, test, and reuse.
Research perspective
I am Muhammad Bilal, a PhD researcher in the Battistuzzi Lab at Oakland University. I investigate how microbial genomes change, how those changes alter phylogenetic inference, and what they can reveal about evolutionary history across deep time.
My route into evolutionary genomics began in biotechnology and microbial genome analysis. Today, I work across population-scale pangenomics, phylogenomics, molecular clocks, high-performance computing, and astrobiology.
The connecting idea is simple: analytical choices should remain visible. I therefore pair biological questions with reproducible workflows, sensitivity analysis, and software that other researchers can inspect and reuse.
What's new
A concise record of the work currently moving from analysis to software, publication, and scientific conversation.
An Oakland Engineer research feature on the Battistuzzi Lab highlights microbial evolution, pangenome analysis, and Muhammad Bilal among the students advancing this work.
Read the OU featurePresenting population-scale pangenome phylogenomics to the astrobiology community in Tucson, Arizona.
A broad synthesis of how pangenomes reveal variation across populations, species, and evolutionary scales.
Developing linked tools for genome curation, species resolution, pangenomics, phylogenomics, genotype-phenotype association, scientific visualization, ordination, and evolutionary-time inference.
Research architecture
Four connected lenses, one continuous investigation of how microbial genomes diversify, persist, and record evolutionary history.
Testing how sampling, thresholds, and genome quality reshape the core and accessory genome.
Reconstructing microbial relationships with reproducible marker selection and model-aware inference.
Studying how calibration and data choices influence evolutionary timelines across microbial lineages.
Connecting ancient microbial evolution and extremophile biology to questions about life beyond Earth.
Two supporting layers turn evolutionary questions into testable, reproducible analyses.
Tracing horizontal gene transfer, functional adaptation, and metabolic pathway evolution across microbial lineages.
OrthoFinder · KEGG · eggNOGDesigning scalable, reproducible HPC pipelines and exploring machine learning for trait prediction in multi-omics research.
Python · Snakemake · scikit-learnResearch record
Peer-reviewed work, public scientific communication, and conference presentations connect the software to the biological questions it was built to examine.
Complete record on Google ScholarBilal, M. & Battistuzzi, F. U.
Encyclopedia of Evolutionary Biology, 2nd edition · ElsevierKanwal, M., Basheer, A., Bilal, M., et al.
International Immunopharmacology · 143, 113241$1,500 · Oakland University · Winter 2026
Population-scale pangenome phylogenomics · Tucson, Arizona
Support for presenting at AbSciCon 2026
Reconstructing the microbial Tree of Life
AbSciCon 2026 · Abramowitz, Bilal, Battistuzzi & Hedges
View citationExperience, education & leadership
Education, research, teaching, and service are not separate lists. Together they show how my scientific questions, technical skills, and responsibilities have developed over time.
Population-scale pangenomics, phylogenomics, molecular clocks, and microbial Tree of Life reconstruction.
BIO 1201 Biology Laboratory and BIO 3401 Genetics Laboratory, with emphasis on experimental reasoning and data interpretation.
Representing and supporting the graduate and professional student community at Oakland University.
Graduate training at the intersection of data analytics, multi-omics science, and interdisciplinary collaboration.
Teaching and academic support across biotechnology, molecular biology, and bioinformatics.
The academic foundation continues the same trajectory, from biotechnology training to doctoral research and data science.
Oakland University · Michigan, USA
Oakland University · Michigan, USA
University of Sargodha · Pakistan
University of Sargodha · Pakistan
Selected software
Flagship projects spanning statistical genomics, phylogenomics, and evidence-aware microbial genome analysis.
View all work on GitHubPopulation-structure-aware microbial GWAS with reproducible QC, likelihood-ratio association testing, Mash-MDS correction, and published-data validation.
Reproducible pangenome-to-phylogeny workflow with explicit thresholds, inspectable intermediate results, and publication-ready reporting.
Evidence-aware microbial species delineation across genome quality, ANI, taxonomy, dereplication, and phylogeny.
Open research ecosystem
The software follows the biological reasoning: assemble a defensible cohort, resolve taxonomic uncertainty, infer evolutionary relationships, test genotype-phenotype associations, examine gene-content structure, and place change in evolutionary time.
Genome retrieval, GCA/GCF reconciliation, metadata capture, explicit quality filters, and optional close-genome relatedness checks.
Combines genome quality, ANI, alignment fraction, GTDB context, dereplication, and phylogenomics while preserving discordant and unresolved cases.
A teaching-oriented workflow connecting annotation, Roary or Panaroo, threshold-aware summaries, IQ-TREE, and inspectable reports.
A tested Snakemake workflow for reference assessment, core SNPs, recombination filtering, accessory-genome comparison, temporal screening, and reporting.
Microbial GWAS across binary genomic features with PCA or Mash-MDS covariates, LRT significance, FDR control, diagnostics, and real published-data validation.
An interpretable ordination workbench using PCA, Jaccard PCoA, metadata-aware visualization, and candidate gene-cluster loadings.
Permutation-based accumulation curves and Heaps' law fitting with explicit gamma interpretation and publication-ready figures.
A learning-first laboratory for reproducible deep-time phylogenomics, beginning with manual understanding and sensitivity analysis before automation.
Scientific data visualization
A six-repository visualization series spanning Python and R. Each module is tutorial-first, reproducible, scientifically guarded, and built around biological and omics examples rather than decorative plotting.
Custom geometry, GridSpec, shared coordinate systems, animation, phylogenies, genome tracks, dashboards, and vector export for biological data.
Tidy-data workflows, semantic mappings, distributions, uncertainty, omics exploration, reusable functions, tests, and publication exports.
Interactive biological graphics with custom hover, coordinated subplots, dashboards, animation, 3D projections, hierarchy, and reproducible HTML export.
Layered marks, transformations, scales, facets, composition, omics case studies, reusable R functions, tests, and regenerated galleries.
Phylogenomic tree annotation, tree-aligned pangenomes, annotation-rich heatmaps, oncoprints, multi-omics panels, and strict identifier checks.
Modular reactive analysis, interactive graphics, responsive layout, downloads, validation, server-side tests, smoke testing, and container-ready deployment.
Working toolkit
I work across Linux and high-performance computing environments, translating biological questions into reproducible analyses that remain inspectable from raw genomes through statistical inference to final figures.
Roary · Panaroo · PIRATE · PPanGGOLiN
IQ-TREE · MAFFT · MUSCLE · iTOL · ggtree
GWAS · population structure · PCA/MDS · logistic regression · FDR
Matplotlib · Seaborn · Plotly · ggplot2 · ComplexHeatmap · Shiny
MCMCTree · PAML · RelTime · calibration design
FastANI · GTDB-Tk · dRep · CheckM2
Python · R · Bash · Linux · Git
Nextflow · Snakemake · Conda · Slurm · GitHub Actions
Evolutionary time
TimeTree provides published divergence-time estimates for pairs of species or higher taxa. It is a useful starting point for exploring evolutionary timescales and tracing estimates back to their sources.
Explore TimeTreeUse scientific names or select an example comparison.
The search launches directly on the TimeTree website.
Review published divergence estimates and their sources.
Curated resources
Papers, databases, and learning materials I return to when moving from biological questions to reproducible analysis.
Open work
Open to collaboration
I welcome conversations with researchers, collaborators, scientific software teams, and recruiters working across computational biology, microbial genomics, astrobiology, and reproducible research.
“Nothing in biology makes sense except in the light of evolution.”
Theodosius Dobzhansky, 1973