Coming Early 2026 — Free tier: 100 MB storage, 1 CPU, 2 GB RAM

Cloud Notebooks
for Bioinformatics

Instantly launch a preconfigured Python or R environment in the cloud with all the tools you need for biological data analysis. Perfect when you need a powerful machine for just a few hours without the hassle of setup.

We're building something awesome. Join the waitlist to get early access!
100 MB
Free storage
1 CPU
Per environment
2 GB
RAM included
Python Notebook
Python 3.11.8 • JupyterLab 4.1
Pre-installed packages
numpy pandas matplotlib scipy biopython requests
Markdown
# My Analysis Your Python notebook is ready to use.
Python [1]
import pandas as pd import numpy as np # Load your data here df = pd.read_csv("data.csv")
Launch in Cloud
1 CPU 2 GB 100 MB
R Notebook
R 4.3.2 • IRkernel 1.3.2
Pre-installed packages
tidyverse ggplot2 dplyr readr Biostrings devtools
Markdown
# My R Analysis Your R environment is ready for analysis.
R [1]
library(tidyverse) library(ggplot2) # Load your data here df <- read_csv("data.csv")
Launch in Cloud
1 CPU 2 GB 100 MB

Advanced Configurations

Choose your environment
Python 3.12 minimal lightweight • pip ready
Python 3.10 DataScience pandas • numpy • sklearn
Popular
Python + Deep Learning PyTorch • CUDA • GPU
R 4.3 minimal base R • CRAN ready
R 4.3 + tidyverse dplyr • ggplot2 • tidyr
Popular
R + Single-cell Seurat • scRNA-seq

Ready-to-use Notebooks

Get started with examples
Pro

Single-cell RNA-seq Analysis

Intermediate • 45 min

Complete preprocessing + clustering pipeline with Seurat.

4 CPU 16 GB RAM 5 GB
R [3]
DimPlot(pbmc, reduction = "umap", group.by = "seurat_clusters")
Output
R Seurat
Open
Free

Bulk RNA-seq Analysis

Intermediate • 30 min

Volcano plots and differentially expressed genes with DESeq2.

1 CPU 2 GB RAM 100 MB
R [7]
EnhancedVolcano(res, x = "log2FoldChange", y = "padj")
Output
R DESeq2
Open
Free

Machine Learning

Beginner • 25 min

Classification with scikit-learn on biological data.

1 CPU 2 GB RAM 100 MB
Python [12]
from sklearn.metrics import confusion_matrix plot_confusion_matrix(clf, X_test, y_test)
Output
Python sklearn
Open
Free

Genomic Visualization

Intermediate • 35 min

Interactive plots of genomic regions with Plotly.

1 CPU 2 GB RAM 100 MB
Python [5]
plot_genome_browser( chrom="chr17", start=7571720, end=7590868)
Output
Python Plotly
Open
Free

Bioinfo Tutorial

Beginner • 20 min

Introduction to Python for bioinformatics with pandas.

1 CPU 2 GB RAM 100 MB
Python [8]
df["expression"].plot(kind="bar") plt.title("Gene Expression")
Output
Python pandas
Open
Pro

Proteomics Analysis

Advanced • 50 min

Differential MS analysis, GO enrichment and KEGG pathways.

2 CPU 8 GB RAM 2 GB
R [15]
pheatmap(top_proteins, scale = "row", cluster_cols = TRUE)
Output
R limma
Open

Why BioNotebooks?

Instant Start

Environments ready in under 30 seconds, no installation required.

Pre-installed Packages

All major bioinfo tools already configured and ready to use.

GPU Support

Access CUDA GPUs for deep learning and intensive computations.

100% Cloud

Work from anywhere, your notebooks are always accessible.