From raw data to results, in days not weeks.

Build bioinformatics pipelines on a visual canvas, run them on your own data, and see every step. No code, no waiting on a bioinformatician.

Early access for research labs. No spam, just one note when your seat opens.

The experiment takes a week. The analysis shouldn’t take a month.

Most labs generate data faster than anyone in the building can analyse it. The tools exist. What is missing is a way to use them without learning to program first.

  • About 3,000 steps

    The tools your field already trusts, from read trimming to statistics, ready to connect.

  • No code to write

    Build, run and change a pipeline from the canvas. No scripts, no environments.

  • Every run recorded

    Versions, inputs and parameters kept with the result, so it runs again the same way.

R1.fastq.gzR2.fastq.gz.gtfsamples.csv.bam$ bash run_v3_final.shSTAR --genomeDir ref/error: index not found

Data in. Result out.

Four things happen between the sequencer and lab meeting. Scroll through them.

  1. 1

    Drag in the steps you need

    Pick from the library, or describe the experiment and let the assistant draft it. Steps only connect where the files fit, so a wiring mistake is not possible.

    rnaseq-treated-vs-control
    FASTQ
    fastp
    Salmon
    DESeq2
    Plot

    Step library

    fastpMultiQCSTARSalmonfeatureCountsDESeq2edgeRVolcano plotHeatmap
    Run logrnaseq-treated-vs-control4 of 4 steps done
    1. fastp12/12 samples trimmed, 96.8% reads kept0:41
    2. salmon12/12 quantified, mapping rate 91.2%2:16
    3. deseq218,402 genes tested, 214 at padj < 0.053:02
    4. plotvolcano.svg written3:05

    DESeq2 settings

    Design
    ~ batch + condition
    Contrast
    treated vs control
    Adjusted p
    < 0.05
    |log2 fold change|
    > 1

    Treated vs control, 214 genes changed

  2. 2

    Change a setting, not a script

    Click a step and its settings open beside it: the threshold, the reference, the comparison you want to make. Each one says what it does.

    rnaseq-treated-vs-control
    FASTQ
    fastp
    Salmon
    DESeq2
    Plot

    Step library

    fastpMultiQCSTARSalmonfeatureCountsDESeq2edgeRVolcano plotHeatmap
    Run logrnaseq-treated-vs-control4 of 4 steps done
    1. fastp12/12 samples trimmed, 96.8% reads kept0:41
    2. salmon12/12 quantified, mapping rate 91.2%2:16
    3. deseq218,402 genes tested, 214 at padj < 0.053:02
    4. plotvolcano.svg written3:05

    DESeq2 settings

    Design
    ~ batch + condition
    Contrast
    treated vs control
    Adjusted p
    < 0.05
    |log2 fold change|
    > 1

    Treated vs control, 214 genes changed

  3. 3

    Press Run and watch it go

    Every step runs on your own machine in its own environment, in order. Each one tells you when it is done, or exactly why it stopped.

    rnaseq-treated-vs-control
    FASTQ
    fastp
    Salmon
    DESeq2
    Plot

    Step library

    fastpMultiQCSTARSalmonfeatureCountsDESeq2edgeRVolcano plotHeatmap
    Run logrnaseq-treated-vs-control4 of 4 steps done
    1. fastp12/12 samples trimmed, 96.8% reads kept0:41
    2. salmon12/12 quantified, mapping rate 91.2%2:16
    3. deseq218,402 genes tested, 214 at padj < 0.053:02
    4. plotvolcano.svg written3:05

    DESeq2 settings

    Design
    ~ batch + condition
    Contrast
    treated vs control
    Adjusted p
    < 0.05
    |log2 fold change|
    > 1

    Treated vs control, 214 genes changed

  4. 4

    Read the result the same day

    The figure is drawn from the last step’s output. The table behind every point is a click away, and it exports as SVG for the figure panel.

    rnaseq-treated-vs-control
    FASTQ
    fastp
    Salmon
    DESeq2
    Plot

    Step library

    fastpMultiQCSTARSalmonfeatureCountsDESeq2edgeRVolcano plotHeatmap
    Run logrnaseq-treated-vs-control4 of 4 steps done
    1. fastp12/12 samples trimmed, 96.8% reads kept0:41
    2. salmon12/12 quantified, mapping rate 91.2%2:16
    3. deseq218,402 genes tested, 214 at padj < 0.053:02
    4. plotvolcano.svg written3:05

    DESeq2 settings

    Design
    ~ batch + condition
    Contrast
    treated vs control
    Adjusted p
    < 0.05
    |log2 fold change|
    > 1

    Treated vs control, 214 genes changed

The OpenBionode canvas: the differential expression steps of a yeast RNA-seq pipeline, from DESeq2 to a volcano plot and a heatmap, grouped in labelled sections.
A yeast RNA-seq pipeline, from counts to figures.

Change a setting. Not a script.

Parameters live in a panel. The code lives behind the node. Drop in the steps your field already trusts and connect them, rather than wiring them together in Bash.

  • Around 3,000 steps, the same tools a core facility would run
  • Set a threshold in a panel instead of editing a config file
  • No conda environments, no cluster account, no job scripts
The figures view: a clustered heatmap of counts per gene, the table it was drawn from, and the list of figure templates.
The same run, two figures later.

Read it yourself. Hand it to a colleague.

A run ends in plots and tables a colleague can rerun exactly, drawn from its own outputs, with the numbers behind each one a click away.

  • Figure templates bind straight to a step’s output
  • See the table a plot was drawn from, without leaving the page
  • Export SVG for the figure panel, CSV for everything else
Get early access

Describe it. Review it. Then run it.

Tell the assistant what the experiment was. It drafts the pipeline as steps on the canvas, not a thousand lines of code, so you can read every one before anything runs.

It never presses Run. You do.

Press Enter to draft

Pick an example above, or describe your own. The draft appears here.

A preview on four starter pipelines. In the app the assistant drafts from your own description and data.

Built for the people waiting on the analysis.

Wet-lab scientists

Analyse your own data the day it comes off the sequencer, without waiting on anyone else’s week.

See pipelines

Bioinformaticians

Build a pipeline once and hand it to the lab. Stop rerunning the same analysis for other people.

See pipelines

Biotech R&D

Shorten the loop from instrument to decision, with a record of every result behind it.

See pipelines

From the journal

All of the journal

The person who ran the experiment should run the analysis

Thinking, 21 October 2026

Your first differential expression analysis, step by step

Explainers, 14 October 2026

What a pipeline actually is, for people who have never written one

Explainers, 7 October 2026

Bring the analysis you’ve been waiting on.

OpenBionode is in early access. Tell us what you analyse and we’ll make sure the steps you need are ready when your seat opens.

Early access for research labs. No spam, just one note when your seat opens.