Transform raw in-house data to insights
Not just creating a gateway to published works, BioTuring Browser is an end-to-end solution for YOUR own single-cell RNA sequencing data.
Quantify transcripts at unparalleled speed using Hera-T. No commands are required.Learn more
Batch effect removal
Retain true biological variances with various batch effect removal methods:
Dimension reduction and clustering
- Perform dimension reduction with t-SNE and UMAP
- Cluster the cells by k-means and graph-based clustering
- Explore novel cell sub-types on an interactive sub-clustering dashboard
Annotation and prediction
- Predict cell types in real time upon selecting any groups of cells. The knowledge base for prediction can be customized to your own definition.
- Explore marker genes and run enrichment analysis
- Track the compositional changes in different treatments, or which treatment enriches different cell types.
- Find differentially expressed genes in any two groups of cells such as two cell types, sub-types, conditions, or any two stages of the disease
Harness the synergy of single-cell multi-omics
Study cell fate decisions with single cell trajectory analysis
Build the trajectory graph from your single-cell data, order single cells in pseudotime, and explore differentially expressed genes along any selected branch.
Search similar cell populations in the entire database
Look for populations that own similar transcriptional profiles to your selected cells in the entire public database, at the same time study what genes are similarly expressed and enrichment processes.
on a laptop
BioTuring Single-cell Browser is optimized to visualize up to 1.3 million single cells at a time on a standard laptop with interactive t-SNE and UMAP.
Bioturing scRNA-seq software greatly accelerates and enables our research, through easy to use intuitive analytics, visualization and extensive database of curated and pre-processed experiments
The BioTuring Browser is a game changer in the Immune Oncology field to speed up single cell data sharing among biologists, immunologists and bioinformaticians. Empowering collaboration in annotating cells, discovering unknown cell populations, cell states or cell interactions is crucial to draw the best picture ever of the highly heterogenous tumor microenvironment. Users have access to a growing public as well as their private knowledge base, which makes the BioTuring platform a swiss-army-knife for better understanding cells at the single cell level.
BBrowser allows biologists to handle scRNA-seq data without programming knowledge. Intuitive operation. Functions are updated. Public data can be imported. It is very helpful.
I came across BBrowser while going through the Bioturing website and have been very impressed by the ease of use, capacity and capability of this unique platform. Within minutes from starting to use this tool, I was able to download single-cell sequencing datasets with metadata, obtain visualizations and biostatistical analyses including correlation and hierarchical clustering, analyse data by age, sex and pathology and carry differential gene expression analyses for my genes of interest. Overall, an amazing tool to have for every researcher engaged in scRNA approaches. Kudos to the developers for bringing this amazing platform to academics engaged in single-cell research. Looking forward to the addition of more studies/workflows and features to this already exciting software!
The Bioturing Browser with single cell add-on provides one of the simplest interfaces for quickly moving from raw data to cluster analyses with minimal experience. The built-in HeraT alignment processes are remarkably quick and less computationally intensive than other scRNA-seq offerings.
Bioturing Browser is an intuitive and powerful software for exploration and visualization of scRNA-Seq data. Its fast and easy access to the vast amounts of curated datasets is very helpful for our drug discovery research.
BBrowser is an amazing software for analysis of single cell RNA sequencing data. It provides seamless integration with the analyzed data from R (not that it's required), and allows you to do differential expression, gene set enrichment, and pseudotime trajectory analysis very quickly and efficiently. I would also have to say that the "identify cell cluster" function, which compares your cell clusters to all the cell annotations available on the BBrowser database, has been amazing for me in identifying cell types that I didn't even know were present within my dataset. I highly recommend that everyone try out this software - both novice and veteran computational biologists alike will enjoy using it! The user interface is very intuitive, and the learning curve is not steep at all. Plus, Bioturing customer support is always available to help with any issues you may encounter during the course of your analysis.