BIOINFORMATICS & COMPUTATIONAL BIOLOGY

Biological data.
Reproducible evidence.

HyLab is an independent research project exploring transparent bioinformatics workflows—from a clearly defined question to validated analysis and an understandable scientific report.

Research prototype · No clinical or diagnostic use

Reproducible by defaultCode, parameters and data provenance remain traceable.
Evidence before claimsResults are separated from interpretation and uncertainty.
Small, focused studiesEach project begins with a narrow and testable question.

RESEARCH DIRECTIONS

Start narrow.
Build reliable foundations.

The first version is intentionally focused. HyLab begins as a public research notebook and project portfolio. It can later grow into reusable workflows, interactive tools or collaborative studies.

01 · EPIGENOMICS

Chromatin accessibility workflows

Reproducible processing and interpretation of chromatin-accessibility data, with explicit quality control, differential analysis and biological context.

  • Pipeline and parameter transparency
  • Quality-control decision points
  • Clear separation of signal and interpretation
Initial research theme
02 · TRANSLATIONAL ANALYSIS

Public biomedical datasets

Focused analyses of public and appropriately de-identified datasets to explore biologically relevant questions without overstating clinical significance.

  • Documented data provenance
  • Reproducible statistical analysis
  • Limitations kept visible
Planned project format
03 · METHODS

Workflow benchmarking

Transparent comparisons of tools, parameters and analysis choices, using defined benchmarks and sensitivity analyses rather than a single preferred result.

  • Versioned environments
  • Benchmark datasets and metrics
  • Independent scientific review
Future research track

REPRODUCIBLE WORKFLOW

Methods that can be
followed, tested and reused.

Every project should leave a readable trail: why the analysis was performed, which data and tools were used, what changed during the work and where uncertainty remains.

PROJECT STANDARD

One traceable path from question to conclusion. Designed for public research outputs, not black-box claims.
01

Research question

Define the biological question, expected output and boundaries before analysing the data.

Question.md
02

Data provenance

Record source, accession, inclusion criteria, transformations and relevant usage constraints.

Data manifest
03

Analysis environment

Version code, dependencies, parameters and execution steps so the work can be repeated.

Workflow + config
04

Quality and sensitivity

Inspect technical quality, alternative assumptions and the robustness of key findings.

QC report
05

Scientific report

Present results, interpretation and limitations without hiding negative or inconclusive findings.

Research note

WORKING PRINCIPLES

Useful research requires
more than working code.

HyLab combines computational work with scientific restraint. A technically correct pipeline can still support a weak conclusion if the question, data or assumptions are poorly defined.

01

Transparent

Important analysis choices and limitations are visible rather than buried in implementation details.

02

Reproducible

Projects use versioned code, environments and parameters wherever practical.

03

Carefully interpreted

Statistical associations are not presented as biological mechanisms or clinical conclusions without evidence.

04

Privacy-conscious

The initial scope is public or appropriately de-identified data; no identifiable patient data is collected by this website.

PROTOTYPE STATUS

A small public starting point.

This first release establishes HyLab's identity, research scope and quality principles. It deliberately contains no accounts, uploads, tracking scripts or clinical functionality.

Static websiteNo cookiesNo user dataAWS-ready

ABOUT HYLAB

Independent research,
built in Switzerland.

HyLab is an independent bioinformatics and computational-biology research project. Its purpose is to develop focused studies and transparent analytical workflows that can be inspected, challenged and improved.

The project draws on experience across bioinformatics, biochemistry and regulated life-science environments. Public releases will distinguish clearly between exploratory research, validated methods and any later product concepts.

Research and privacy information