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In silico research tools

In silico research tools, custom-built.

Bioinformatics pipelines and analysis apps designed around your data and your questions. Built by a scientist, tested like software, and handed over so your team can run them.

What we build

Tools for every step of the analysis.

Every tool is built for one team's data, questions and infrastructure, and every one ends in something a bench scientist can use.

Sequencing

Sequencing pipelines

QC, alignment, variant calling and expression analysis, from FASTQ to a report, as versioned and reproducible pipelines.

Microbiology

Microbial genomics

Assembly, annotation, strain typing and phylogenies, and screening for resistance and virulence genes across your isolates.

Structure

Protein and structure workflows

Structure prediction with AlphaFold 2, ESMFold or other models licensed for commercial use, docking and virtual screening, and sequence-to-structure analysis at scale.

Design

Design tools

Primers and probes, CRISPR guides, codon optimization and in silico cloning, with your lab's rules built in.

Analysis

Curves, statistics and models

Dose-response and growth-curve fitting, qPCR quantification, and models trained on your own assay data.

Interfaces

Apps for bench scientists

Web interfaces around all of the above, so nobody needs a terminal, a cluster account or a bioinformatician to run an analysis.

Try it

A tiny in silico tool, running in your browser.

Paste a DNA sequence, or use the EGFP coding sequence below, and get the first things a bench scientist checks. Nothing is uploaded: every calculation runs on your device.

Length
720 bp
GC content
61.5%
Composition
A 174 · C 240 · G 203 · T 103

Open reading frames, all six frames

+1+2+3−1−2−30100200300400500600700

Frame +1, 1..720, 720 nt, 239 aa. 1 ORF of at least 50 aa in total.

Longest ORF, translated

MVSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTLTYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITLGMDELYK

Reverse complement

TTACTTGTACAGCTCGTCCATGCCGAGAGTGATCCCGGCGGCGGTCACGAACTCCAGCAGGACCATGTGATCGCGCTTCTCGTTGGGGTCTTTGCTCAGGGCGGACTGGGTGCTCAGGTAGTGGTTGTCGGGCAGCAGCACGGGGCCGTCGCCGATGGGGGTGTTCTGCTGGTAGTGGTCGGCGAGCTGCACGCTGCCGTCCTCGATGTTGTGGCGGATCTTGAAGTTCACCTTGATGCCGTTCTTCTGCTTGTCGGCCATGATATAGACGTTGTGGCTGTTGTAGTTGTACTCCAGCTTGTGCCCCAGGATGTTGCCGTCCTCCTTGAAGTCGATGCCCTTCAGCTCGATGCGGTTCACCAGGGTGTCGCCCTCGAACTTCACCTCGGCGCGGGTCTTGTAGTTGCCGTCGTCCTTGAAGAAGATGGTGCGCTCCTGGACGTAGCCTTCGGGCATGGCGGACTTGAAGAAGTCGTGCTGCTTCATGTGGTCGGGGTAGCGGCTGAAGCACTGCACGCCGTAGGTCAGGGTGGTCACGAGGGTGGGCCAGGGCACGGGCAGCTTGCCGGTGGTGCAGATGAACTTCAGGGTCAGCTTGCCGTAGGTGGCATCGCCCTCGCCCTCGCCGGACACGCTGAACTTGTGGCCGTTTACGTCGCCGTCCAGCTCGACCAGGATGGGCACCACCCCGGTGAACAGCTCCTCGCCCTTGCTCACCAT

Translation in one frame

MVSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTLTYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITLGMDELYK*

Standard genetic code (NCBI table 1). An ORF starts at ATG and ends at the first in-frame stop codon; positions are 1-based on the sequence as entered, and minus-strand ORFs are shown as complement(start..end). A demo, not a validated tool: the pipelines we build for you are tested against data with known answers.

Microbial genomics

From isolate to answer.

A microbiologist knows which question the genome has to answer: which strains are related, what they carry, and whether the lab should worry.

  • Assembly and annotation from short or long reads
  • Typing (MLST, cgMLST) and core-genome phylogenies
  • Resistance and virulence genes, plasmids and mobile elements
  • Outbreak and batch comparisons your QA team can read
Fig. 1. Illustrative output of a microbial genomics pipeline: a core-genome tree of eight E. coli isolates next to the resistance genes found in each. Here blaCTX-M-15 travels with the ST131 clade.

Results you can defend

Tested like software. Checked like science.

  • Reproducible

    Pipelines are versioned, containerized and pinned, so the same input gives the same result next year.

  • Tested against known answers

    Before you rely on a pipeline, it runs on data where the answer is known, and the comparison is in the handover.

  • Documented for your methods section

    Every release comes with the versions, parameters and references you need for a report, a paper or an audit.

  • Runs where your data lives

    Your cloud account, your cluster or your own servers. Large files stay put; the pipeline goes to them.

Fig. 2. What a handover includes: the pipeline checked against a set of variants with known answers, rerun for reproducibility, with pinned versions and a methods paragraph. Illustrative report.

Next step

Which step in your lab still runs on copy-paste?

Tell us in a 30-minute call. You'll leave with a clear next step, whether or not we end up working together.