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Powerful single-cell transcriptome analysis in a simple UI

Explore hundreds of curated single-cell transcriptome datasets, along with your own data, through interactive visualizations and analytics. The software also supports multimodal omics, e.g. CITE-seq, TCR-seq, spatial transcriptomics.

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Interactively explore the world's largest single-cell expression database

Access and query insights from a single cell database of millions of cells, fully annotated with cell type labels and experimental metadata.

Learn more about the curated database

Transform raw in-house data into insights

Not just creating a gateway to published works, BBrowser is an end-to-end solution for YOUR own single-cell data. Import your fastq files, count matrices, Seurat or Scanpy objects and reveal the biological stories inside them.

Single-cell data transforming in BBrowser

All-in-one preprocessing and downstream analytics. Simple to run.

Get a rich package of visualizations and analyses in an intuitive interface, making insight mining from any curated or in-house single-cell dataset become such a breeze.

Free download

Query gene expression

Find differentially expressed genes between two groups

Find marker genes and enriched processes (source: Reactome)

Sub-cluster any cell populations

Study cell fate decisions with single cell trajectory analysis

Search similar cell populations in the entire database

Harness the synergy of multimodal single-cell omics

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Single-cell CRISPR screening data analysis

Import single-cell CRISPR screening or Perturb-seq data. Query guide RNA sequences

CITE-seq data analysis

Unravel protein expression together with gene activity

Combine TCR information with gene expression

Study clonotype expansion in combination with single-cell RNA-seq data

Compatibility with spatial transcriptomics data

BBrowser single-cell analytics and visualizations are also compatible with spatial transcriptomics data from 10X Genomics Visium and NanoString DSP technologies, accommodating effortless exploration of gene activities in the tissue context.

View more spatial capabilities

Bioturing BBrowser is a very helpful software for researchers to explore and discover the hidden gems in the scRNA-seq data. It is easy to install and learn, and does not require coding background. It is also user friendly and full of customizations for both exploratory and publication purposes. One of the best parts of this software is that it can easily load and process a wide range of data types, such as scRNA-seq, scATAC-seq, and spatial transcriptomics. It also provides some of the best tools to filter, cluster and analyze the data.

Li Cai - PhD Student, MD Anderson Cancer Center

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

Eugene Bolotin - Senior Bioinformatics Scientist, Kite Pharma

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.

Researcher (Pharmacology), Chugai Pharmaceutical

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.

Chris Ahuja - University of Toronto.

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.

Niv Sabath - Senior Scientist, Compugen

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.

Molecular Oncology and Immunology Lab, IFOM

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!

Anand Hardikar, Western Sydney University, Sydney Australia

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.

Sanjid Shahriar, Agalliu Lab, Department of Neurology, Columbia University Irving Medical Center

BBrowser is an outstanding software to analyze single-cell datasets intuitively without programming knowledge. It powerfully supports not only identifying the known cluster or marker genes, but also performing further downstream analysis such as trajectory or gene set enrichment analysis.

Department of Preventive Medicine, The University of Tokyo

Bioturing BBrowser is a very helpful software for researchers to explore and discover the hidden gems in the scRNA-seq data. It is easy to install and learn, and does not require coding background. It is also user friendly and full of customizations for both exploratory and publication purposes. One of the best parts of this software is that it can easily load and process a wide range of data types, such as scRNA-seq, scATAC-seq, and spatial transcriptomics. It also provides some of the best tools to filter, cluster and analyze the data.

Li Cai - PhD Student, MD Anderson Cancer Center

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

Eugene Bolotin - Senior Bioinformatics Scientist, Kite Pharma

Publications

Dysfunctional ERG signaling drives pulmonary vascular aging and persistent fibrosis

Nat Commun 13, 4170 (2022).

Caporarello, Nunzia, et al.. - July 25, 2022.

Epicutaneous allergen immunotherapy induces a profound and selective modulation in skin dendritic cell subsets

Journal of Allergy and Clinical Immunology (2022).

Laoubi, Léo, et al. - Online June 30, 2022.

Understanding of Retinal Degeneration Through the Lens of High Throughput Gene Expression

The Australian National University (Australia) ProQuest Dissertations Publishing, 2022. 29070861.

Cioanca, Adrian V. - May, 2022.

The Role of FOXDin Clear Cell Renal Cell Carcinoma .

Electronic Theses and Dissertations. 3567.

Bond, Kyle H. - May, 2022.

Interferon regulatory factor (IRF1) controls the metabolic programmes of low-grade pancreatic cancer cells

Gut (2022)

Alfarano, Gabriele, et al. - Published Online First: May 13, 2022.

Refining colorectal cancer classification and clinical stratification through a single-cell atlas.

Genome biology 23.1 (2022) 

Khaliq, Ateeq M., et al. - May 11, 2022

Epithelial LIF signaling limits apoptosis and lung injury during bacterial pneumonia

American Journal of Physiology-Lung Cellular and Molecular Physiology.

Na, Elim, et al. - Published February 09, 2022

Soxinduces glioblastoma cell stemness and tumor propagation by repressing TET2 and deregulating 5hmC and 5mC DNA modifications

Signal Transduction and Targeted Therapy, 7(1), 1-12.    

Lopez-Bertoni, Hernando, et al. - February 09, 2022

The Role of the Extracellular Matrix and Tumor-Infiltrating Immune Cells in the Prognostication of High-Grade Serous Ovarian Cancer

Cancers, 14(2), 404.     

Belotti, Yuri, Elaine Hsuen Lim, and Chwee Teck Lim - January 14, 2022

DNA Methylome Alterations are Associated with Airway Macrophage Differentiation and Phenotype During Lung Fibrosis

American Journal of Respiratory and Critical Care Medicine ja (2021).

McErlean, Peter, et al. - July 19, 2021

Heterogeneity of meningeal B cells reveals a lymphopoietic niche at the CNS borders

 Science, 373(6553), eabf9277.  

Brioschi, Simone, et al. -  June 03, 2021

Pancreatic Cancer Cells Require the Transcription Factor MYRF to Maintain ER Homeostasis

Developmental Cell 55, 1–15. 

Milan, Marta, et al. - November 23, 2020

The Chemical Biology of Long Noncoding RNAs

Springer Nature Switzerland AG, RNA Technologies 11, p.122. 

Stefan Jurga, Jan Barciszewski (Editors) - 2020

The balance of stromal BMP signaling mediated by GREMand ISLR drives colorectal carcinogenesis

Gastroenterology, 160(4), 1224-1239.

Kobayashi, Hiroki, et al. - November 13, 2020

Hemolysis transforms liver macrophages into antiinflammatory erythrophagocytes

The Journal of clinical investigation, 130(10), 5576-5590. 

Pfefferlé, Marc, et al.  - September 14, 2020

Comparison of visualization tools for single-cell RNAseq data

NAR genomics and bioinformatics, 2(3), lqaa052. 

Cakir, Batuhan, et al. - July 29, 2020
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