01

Discovery & Scoping

We understand your scientific objectives, data landscape, infrastructure constraints, and compliance requirements. We define clear deliverables and success criteria. This phase is critical—we invest time upfront to ensure we're solving the right problem with the right approach, avoiding costly rework later.

  • Initial consultation and requirements gathering — Understand your research questions, hypotheses, and expected outcomes
  • Data assessment and quality evaluation — Review your sequencing data, assess quality metrics, and identify potential issues
  • Infrastructure and compliance review — Evaluate your compute environment, data storage, and regulatory requirements
  • Project timeline and milestone definition — Establish clear timelines, deliverables, and success metrics
02

Design & Planning

We design analysis workflows and infrastructure architecture. For regulated environments, we establish SOPs, validation plans, and traceability frameworks upfront. This phase transforms requirements into actionable technical specifications, ensuring the solution is both scientifically sound and technically feasible.

  • Workflow architecture design — Design Nextflow pipelines, containerization strategy, and compute architecture
  • SOP development for regulated environments — Create standard operating procedures, validation protocols, and documentation frameworks
  • Validation and testing strategy — Define test datasets, acceptance criteria, and validation approaches
  • Resource and infrastructure planning — Estimate compute requirements, storage needs, and cost projections
03

Development & Analysis

We build pipelines, execute analyses, and iterate based on scientific insights. All work is version-controlled, documented, and reproducible. This is where the science happens—we combine rigorous statistical methods with biological interpretation to deliver insights that drive discovery.

  • Pipeline development and containerization — Build and test Nextflow workflows with Docker/Singularity containers
  • Data processing and quality control — Execute preprocessing, QC, and filtering with comprehensive quality reporting
  • Statistical analysis and interpretation — Apply appropriate statistical methods, perform differential analysis, and interpret biological significance
  • Iterative refinement based on results — Refine analyses based on initial findings, incorporate feedback, and optimize workflows
04

Delivery & Handoff

We deliver interpretation-ready results, production pipelines, comprehensive documentation, and knowledge transfer to your team. Our goal is to ensure you can use, maintain, and extend the work we've done—not just receive a black box.

  • Final reports and visualizations — Publication-ready figures, interactive dashboards, and interpretation-ready summaries
  • Production-ready pipeline deployment — Deploy workflows to your infrastructure with monitoring, logging, and error handling
  • Comprehensive documentation — Technical documentation, user guides, SOPs, and validation reports
  • Training and knowledge transfer — Hands-on training sessions, code walkthroughs, and ongoing support

From insight to impact — all in one workflow

Unlike other bioinformatics services built for one-off analyses, Omicsify connects every step of your genomics workflow. Every answer naturally informs the next step, helping your teams move from raw data to actionable insights without ever breaking context.

1

Data Processing & Quality Control

Start with comprehensive data processing and QC. We handle raw sequencing data from any platform—Illumina, PacBio, Oxford Nanopore—with automated quality assessment and preprocessing pipelines. Quality control isn't just a checkbox—it's the foundation of reliable analysis. We identify issues early, document quality metrics, and ensure data meets standards for downstream analysis.

  • Automated QC metrics and reporting — Per-sample and cohort-level quality metrics with automated flagging of problematic samples
  • Multi-platform data harmonization — Standardize data from different sequencing platforms and protocols for integrated analysis
  • Quality thresholds and filtering — Apply appropriate quality filters based on your research question and data characteristics
2

Analysis & Interpretation

Execute domain-specific analyses with best-practice workflows. From variant calling to differential expression, we apply the right methods for your research question. We don't just run tools—we understand the assumptions, limitations, and biological context to ensure results are both statistically sound and scientifically meaningful.

  • Domain-appropriate statistical methods — Choose and apply methods suited to your data type, sample size, and research question
  • Biological interpretation and annotation — Annotate variants, genes, and pathways with functional information and biological context
  • Comparative and integrative analyses — Combine multiple data types, compare across conditions, and integrate with public datasets
3

Visualization & Reporting

Transform results into publication-ready visualizations and interpretation-ready reports. Your teams get actionable insights formatted for decision-making, manuscripts, or regulatory submissions. We create visualizations that tell a story—highlighting key findings, supporting conclusions, and enabling exploration of the data.

  • Publication-quality figures and tables — High-resolution figures, formatted tables, and figure legends ready for manuscripts
  • Interactive dashboards and notebooks — R Shiny apps, Jupyter notebooks, and other interactive tools for data exploration
  • Regulatory-ready documentation — Reports formatted for regulatory submissions with traceability and validation documentation
4

Production Deployment

Deploy scalable, reproducible pipelines that work in production. Whether on-prem HPC or cloud infrastructure, we build systems that handle scale, cost, and compliance requirements. Production deployment means more than just running a pipeline—it means building systems that are reliable, maintainable, and cost-effective over the long term.

  • Containerized, version-controlled pipelines — Deploy Nextflow workflows with Docker/Singularity containers, managed with Git version control
  • Scalable compute architectures — Design systems that scale from single samples to population-scale studies with cost optimization
  • Automated monitoring and alerting — Implement logging, monitoring, and alerting to ensure pipeline reliability and catch issues early

Ready to start your project?

Let's discuss how our process can work for you.