Capabilities
A technical overview of the methods, tools, standards, and infrastructure we use to deliver bioinformatics, AI, and scientific software solutions.
Request Technical BriefWhat We Can Analyze and Build
Methods, workflows, and software categories we work with — mapped to what they produce in practice.
Genomic Data Analysis
- Whole-genome and whole-exome sequencing (WGS/WES)
- Targeted panel sequencing analysis
- Genome-wide association studies (GWAS) and statistical genetics
- Population-scale cohort analysis and association testing
- Somatic variant calling and tumour profiling
- Germline variant classification and interpretation
- Copy number variant (CNV) and structural variant (SV) analysis
- Pharmacogenomics profiling
Transcriptomics & Epigenomics
- Bulk RNA-seq differential expression analysis
- Single-cell RNA-seq (scRNA-seq) clustering and annotation
- Spatial transcriptomics data processing
- Alternative splicing and isoform analysis
- ChIP-seq and ATAC-seq peak calling
- DNA methylation analysis (WGBS, RRBS)
- Long-read RNA-seq (PacBio, Oxford Nanopore)
Microbiome & Metagenomics
- 16S/18S/ITS amplicon sequencing analysis
- Shotgun metagenomics profiling
- Functional annotation of microbial communities
- Longitudinal microbiome study analysis
- Antibiotic resistance gene identification
- Metatranscriptomics and metabolomics integration
Machine Learning & AI
- Biomarker discovery and feature selection
- Disease classification and risk prediction models
- Survival analysis and time-to-event modeling
- Deep learning for genomic sequence analysis
- NLP for biomedical text and clinical records
- Image analysis for histopathology and microscopy
- Federated learning for privacy-preserving analysis
Software Development
- Web-based research data management platforms
- Interactive bioinformatics dashboards and visualizations
- REST and GraphQL APIs for biological databases
- LIMS integration and laboratory automation
- Clinical variant reporting systems
- Secure, cloud-native deployment (AWS, GCP, Cloudflare)
- Data pipeline orchestration and scheduling
Data Governance & Security
- GDPR-aware data handling architecture
- Role-based access control and audit logging
- Encryption in transit and at rest
- Pseudonymization and de-identification workflows
- Support for DTA and secure collaboration requirements
- EU-based processing options for clinical research data
Our Technical Stack
We use established, maintained, and widely-adopted tools — choosing the right technology for each problem, not chasing trends.
Bioinformatics & Genomics
The core tooling for NGS analysis — from raw read alignment and variant calling to pipeline orchestration and annotation.
- GATK · BWA · STAR · HISAT2
- DESeq2 · edgeR · limma
- Nextflow · Snakemake · WDL
- ANNOVAR · VEP · SnpEff
- QIIME2 · MetaPhlAn · HUMAnN
- Seurat · Scanpy (single-cell)
AI & Machine Learning
Production-grade ML frameworks used to build, validate, and explain predictive models on biological and clinical data.
- PyTorch · TensorFlow · JAX
- scikit-learn · XGBoost · LightGBM
- Hugging Face Transformers
- SHAP · LIME (interpretability)
- MLflow · Weights & Biases
- CellTypist · scVI (bio-specific ML)
Software & Infrastructure
Full-stack development and cloud infrastructure to build, deploy, and scale research tools and diagnostic software.
- Python · R · Bash · TypeScript
- Next.js · React · FastAPI
- PostgreSQL · MongoDB · DuckDB
- Docker · Kubernetes · Terraform
- AWS · GCP · Cloudflare
- REST API · GraphQL · HL7 FHIR
Data Standards & Formats
Native support for the file formats and data standards used across genomics, clinical informatics, and life science research.
- VCF · BAM/CRAM · FASTQ
- DICOM · NIfTI (medical imaging)
- HL7 FHIR (clinical data)
- HDF5 · Zarr · Parquet
- JSON-LD · RDF (ontologies)
- MIAME · MINSEQE (reporting)
How We Apply These Capabilities
Technical depth only matters if it translates to outcomes. Here is how our capabilities map to the real problems clients bring us.
Problem
Sequencing data sitting unused
Outcome
Reproducible, publication-ready analysis with annotated variants and a documented pipeline you can re-run.
Problem
A clinical dataset with no predictive model
Outcome
A validated ML classifier with interpretability output (SHAP) and a clear performance summary your team can trust.
Problem
Manual reporting that takes hours each week
Outcome
An automated reporting pipeline that generates structured, reviewed outputs from instrument data — no manual steps.
Problem
A research tool that only runs on one person's laptop
Outcome
A containerised, cloud-deployed application accessible to your whole team — with documentation and a maintenance handover.
Handled with Rigour and Confidentiality
Every project is treated with the care appropriate for sensitive biological and clinical data.
EU-Based Processing
Nalam Biosciences is registered in Estonia, EU. Data processed within EU jurisdiction under GDPR by default.
Confidentiality by Default
All client data and research materials are treated as strictly confidential. NDA execution available on request.
Secure Infrastructure
Cloud deployments use encrypted storage, private networking, and role-based access controls from day one.
No Vendor Lock-in
All deliverables — code, pipelines, data, documentation — are owned by the client. No proprietary platform dependencies unless required.