Transform Genomic Data Into FDA-Ready Insights
Professional Bioinformatics Services for Biotech & Pharma
Pharma-grade NGS analysis, reproducible pipelines, and AI-powered workflows from a team with 9+ years of experience at Roche/Genentech.
9+ Years Pharma Experience β’ 800+ Samples Analyzed β’ 6 Publications
Why Choose ACTN3 Bioinformatics?
π₯ Pharma Experience
9+ years at Roche/Genentech analyzing clinical trial data. We understand regulatory requirements, GxP awareness, and audit-ready documentation.
π Publication Quality
6 peer-reviewed papers in top-tier journals. We donβt just analyzeβwe deliver publication-ready insights and figures.
π Reproducible Workflows
Git, Docker, Quarto reports. Every analysis is version-controlled, containerized, and fully reproducible for regulatory compliance.
β‘ Faster Than In-House
Established pipelines and 9 years of expertise mean faster delivery than building capabilities internally.
π¦ R Package Expertise
CRAN/Bioconductor standards, comprehensive documentation, unit testing. Production-ready code, not just scripts.
π€ Flexible Partnership
Small team = responsive communication, no corporate bureaucracy. We adapt to your timeline and requirements.
Our Core Services
NGS Analysis
End-to-end sequencing analysis from FASTQ to insights:
RNA-seq (bulk & single-cell), ChIP-seq, ATAC-seq, WGS/WES, CRISPR screens, scTCR/scBCR-seq (immune repertoire), spatial transcriptomics, long-read sequencing, methylation analysis
Timeline: 2-8 weeks
Deliverables: Quarto reports, publication figures, processed datasets
R Package Development
Custom Bioconductor packages for your workflows:
Package architecture, comprehensive documentation (pkgdown), unit testing (testthat), CRAN/Bioconductor submission support
Timeline: 4-12 weeks
Deliverables: Documented package, vignettes, GitHub repository
Pipeline Development
Production-ready Snakemake/Nextflow workflows:
Automated processing, Docker containers, HPC optimization, CI/CD integration, comprehensive documentation
Timeline: 4-12 weeks
Deliverables: Reproducible pipeline, documentation, deployment guide
AI System Deployment
ML-ready datasets and predictive models:
Feature engineering, model training, REST API deployment, automated QC systems, production monitoring
Timeline: 6-16 weeks
Deliverables: Trained models, deployment scripts, API documentation
Shiny Dashboards
Interactive data exploration tools:
Real-time QC monitoring, exploratory analysis interfaces, client-facing visualizations, GitHub Pages deployment
Timeline: 2-6 weeks
Deliverables: Hosted application, source code, user documentation
Multi-Omics Integration
Comprehensive data integration:
Transcriptomics + genomics + epigenomics, TCGA/GEO mining, cross-species analysis, pathway enrichment
Timeline: 4-12 weeks
Deliverables: Integrated datasets, insight reports, visualizations
Questions About Your Project?
Not sure which service you need? Get a free 30-minute consultation to discuss your requirements, timeline, and deliverables.
Technology Stack
Core Analysis
R/Bioconductor β’ Seurat β’ limma/edgeR/DESeq2 β’ Python/Scanpy β’ fgsea β’ ComplexHeatmap
Pipeline Engineering
Snakemake β’ Nextflow β’ Docker β’ Git/GitHub β’ HPC clusters β’ CI/CD
Machine Learning
caret β’ PAM β’ NMF β’ scikit-learn β’ TensorFlow β’ Feature engineering
Reporting & Visualization
Quarto β’ Shiny β’ ggplot2 β’ plotly β’ RMarkdown β’ pkgdown
Proven Track Record
9+ Years
Roche/Genentech Experience
800+
Patient Samples Analyzed
160K
sgRNAs in CRISPR Screens
6
Publications in Top Journals
Featured Work
113-Gene Classifier for Lung Cancer β Developed predictive signature with 87-91% accuracy across >800 patients. Published in Clinical Cancer Research (2021).
CRISPR Screen Platform β Analyzed 160,000 sgRNAs identifying immune evasion mechanisms. Published in iScience (2025).
scRNA-seq Pipeline β Reproducible Snakemake workflow producing ML-ready perturbation datasets with automated QC and normalization.
Ready to Accelerate Your Discovery?
From FASTQ to FDA-ready insights in weeks, not months. Letβs discuss how we can support your research goals with pharma-grade bioinformatics.