Technology & Expertise
Our Technical Arsenal
9+ years of pharma-grade bioinformatics expertise with comprehensive technology stack for NGS analysis, pipeline development, and AI integration.
🧬 Core Bioinformatics
NGS Analysis Platforms
RNA-seq Analysis
limma-voom • edgeR • DESeq2 • HTSeqGenie • GSNAP • STAR • Salmon • featureCounts
Single-Cell RNA-seq
Seurat • Scanpy • UMAP • t-SNE • Cell type annotation • Trajectory inference • RNA velocity • scTCR/scBCR-seq immune repertoire profiling
CRISPR/ORF Screens
crisprVerse • screenCounter • MAGeCK • TMM normalization • limma-voom • Hit prioritization
ChIP-seq & Epigenomics
BWA • Bowtie2 • MACS2 • DiffBind • ChIPseeker • Motif enrichment • Homer
Genomic Variants
GATK • Strelka2 • VarScan • COSMIC • SnpEff • VEP • Variant filtering • Clinical interpretation
Spatial Transcriptomics
10X Visium • Spatial gene expression • Tissue architecture • Cell-cell interactions
Long-Read Sequencing
PacBio • Nanopore • Isoform detection • Structural variants • Repeat region analysis
Methylation Analysis
WGBS • Infinium arrays • Bismark • methylKit • DMR detection • Epigenetic profiling
📊 Statistical Analysis
Differential Expression
Methods: limma-voom, edgeR, DESeq2, moderated t-tests, robust empirical Bayes
Adjustments: FDR correction (Benjamini-Hochberg), q-values (Storey), permutation tests
Visualization: Volcano plots, MA plots, heatmaps with hierarchical clustering
Pathway Analysis
Tools: fgsea, Camera (limma), GSVA, GSEA, Enrichr
Databases: MSigDB (Hallmark, C2, C5, C7), GO, KEGG, Reactome, WikiPathways
Networks: STRING, IPA, Metacore GeneGO, regulatory network reconstruction
Survival Analysis
Models: Cox proportional hazards, Kaplan-Meier curves, log-rank tests
R Packages: survival, survfit, survminer
Features: Time-dependent covariates, interaction terms, stratification
Machine Learning
Unsupervised: Consensus NMF, hierarchical clustering, PCA, t-SNE, UMAP, k-means
Supervised: PAM, Random Forests, SVM, Neural Networks (Keras/TensorFlow)
Validation: Cross-validation, independent cohort testing, performance metrics (ROC, AUC, sensitivity, specificity)
⚙️ Pipeline Engineering
Workflow Management
Snakemake — Production pipelines with DAG visualization, cluster execution, checkpoint/restart
Nextflow — DSL2 workflows, nf-core modules, cloud-native deployment
Bash Scripting — Custom automation, data munging, job submission
Containerization
Docker — Reproducible environments, multi-stage builds, Docker Hub registry
Singularity — HPC-compatible containers, rootless execution
Best Practices: Minimal base images, layer caching, security scanning
Version Control
Git/GitHub — Branching strategies, pull requests, code review
GitHub Actions — CI/CD pipelines, automated testing, deployment
GitLab CI/CD — Alternative platform for enterprise clients
High-Performance Computing
Job Schedulers: SLURM, SGE, PBS/Torque
Resource Management: Memory optimization, parallel processing, array jobs
Data Transfer: rsync, Aspera, AWS S3/Glacier
📦 R Package Development
Package Infrastructure
Development Tools: devtools, usethis, roxygen2, pkgdown
Documentation: Function references, vignettes, NEWS.md, README
Testing: testthat (unit tests), covr (code coverage), R CMD check
Standards Compliance
CRAN Standards: Package structure, documentation requirements, DESCRIPTION fields
Bioconductor: S4 classes, vignettes, dependencies, coding style
Best Practices: Error handling, input validation, version compatibility
Object-Oriented Programming
S3 Classes: Simple object systems, generic methods
S4 Classes: Formal class definitions, slot validation, inheritance
R6 Classes: Reference semantics for mutable objects
🤖 AI & Machine Learning
ML Frameworks
R: caret, mlr3, tidymodels, keras, tensorflow
Python: scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM
AutoML: H2O.ai, auto-sklearn, TPOT
Feature Engineering
Genomics-Specific: Gene signatures, pathway scores, mutation burden
Normalization: z-score, quantile, TPM, CPM, TMM, sctransform
Selection: Recursive feature elimination, LASSO, elastic net, random forest importance
Model Deployment
REST APIs: Flask, FastAPI, Plumber (R)
Containerization: Docker for model serving
Monitoring: Model performance tracking, drift detection
Cloud: AWS SageMaker, Azure ML, Google Cloud AI Platform
📈 Visualization & Reporting
Static Graphics
ggplot2 — Publication-quality plots with custom themes
ComplexHeatmap — Multi-layer heatmaps, annotations, clustering
pheatmap — Quick heatmaps for exploratory analysis
Base R — Specialized plots for genomics (MAF plots, circos-style)
Interactive Visualizations
Shiny — Web applications for data exploration
plotly — Interactive plots with zoom, hover tooltips
DT — Interactive tables with search/filter
htmlwidgets — JavaScript visualizations in R
Report Generation
Quarto — Modern literate programming, multi-format output
RMarkdown — Dynamic documents, parameterized reports
pkgdown — Package documentation websites
Bookdown — Long-form documentation, thesis, books
🗄️ Data Management
Data Formats
Sequence Data: FASTQ, BAM, SAM, CRAM, BED, VCF
Expression Data: h5ad, Seurat objects, ExpressionSet, SingleCellExperiment
Tabular: CSV, TSV, Excel, Parquet, Arrow
Databases: SQLite, PostgreSQL, MySQL, MongoDB
Public Databases
TCGA — The Cancer Genome Atlas (>11,000 patients)
GEO — Gene Expression Omnibus (>200,000 datasets)
EGA — European Genome-phenome Archive (controlled access)
SRA — Sequence Read Archive (raw sequencing data)
cBioPortal — Cancer genomics data portal
COSMIC — Catalog of Somatic Mutations in Cancer
FoundationOne — Clinical genomic profiling data
Data Wrangling
R: dplyr, data.table, tidyr, stringr, purrr
Python: pandas, numpy, polars
Command Line: awk, sed, grep, cut, sort, uniq
🔬 Sequencing Platforms
Illumina
NovaSeq 6000 — High-throughput WGS, RNA-seq, panels
HiSeq 2500/4000 — Mid-throughput sequencing
NextSeq 500/550 — Desktop sequencer for targeted panels
MiSeq — Small-scale sequencing, amplicon analysis
10X Genomics
Single-Cell Gene Expression — 3’ and 5’ chemistry
Single-Cell Immune Profiling — scTCR-seq, scBCR-seq
Visium Spatial — Spatial transcriptomics
Multiome — scRNA-seq + scATAC-seq
Long-Read
PacBio Sequel II — HiFi long reads (10-25kb)
Oxford Nanopore — MinION, GridION, PromethION (ultra-long reads)
Microarrays
Affymetrix — Gene expression arrays (Exon, Gene ST)
Illumina BeadArray — Methylation arrays (450K, EPIC)
Agilent — Custom arrays for targeted profiling
🏢 Industry-Standard Tools
Compliance & Quality
ISO Standards Experience — Quality management systems (QMS)
GxP Awareness — Good Clinical/Laboratory/Manufacturing Practices
21 CFR Part 11 — Understanding of electronic records regulations
CRAN/Bioconductor Standards — Package quality guidelines
Collaboration Tools
Slack — Team communication
Zoom/Teams — Virtual meetings
Asana/Jira — Project management
Confluence — Documentation
SharePoint — Document collaboration
Cloud Platforms
AWS — S3, EC2, Batch, SageMaker
Google Cloud — Cloud Storage, Compute Engine, Vertex AI
Azure — Blob Storage, Virtual Machines, Machine Learning
Collaboration: Terra/Firecloud, DNAnexus, Seven Bridges
📚 Continuous Learning
We stay current with the latest bioinformatics developments:
Conferences: AACR, ISMB, R/Pharma, BioC (Bioconductor)
Journals: Nature Biotech, Genome Biology, Bioinformatics, Nature Methods
Communities: Biostars, Bioconductor support, Stack Overflow
Training: Coursera, edX, company workshops, peer learning
🎯 Our Expertise in Action
9+ Years Pharma Experience at Roche/Genentech
800+ Patient Samples analyzed across clinical trials
160,000 sgRNAs processed in CRISPR screens
6 Publications in top-tier journals
Multiple R Packages developed for internal use
Production Pipelines serving multiple teams
💼 Ready to Leverage Our Expertise?
From cutting-edge analysis to production-ready pipelines, we bring the right tools and expertise to your project.
📧 Discuss Your Needs 🛠️ Explore Services
Email: kontakt@actn3.pl
Response Time: Within 24 hours