Here, we explain everything about our collaboration with BioTuring, which provides our clients with a free license to the BBrowser.
BBrowser Installation Guide
Download and install the BBrowser
- The first step is to install the BBrowser on your computer: Create an account on the BioTuring website: https://bioturing.com/user/signup. Please note that you must use the same email address as we use to communicate with you to create your account. Otherwise, you will not be able to see your project.
- Download the BBrowser: https://bioturing.com/bbrowser/download. Windows users are recommended to download the portable version because this does not require the admin rights to run. However, please unzip the portable folder before running the application.
- Log in. Select status: academic and then click on get started.
You’ll be redirected to the BioTuring website where you can log in with your account details. Use the login details you just created at step 1. and click on log in.
After logging in to your account, close the browser window and go back to the BBrowser window.
Note: installing the BBrowser might take a while
Activate the license and connect to the server
Activate the SCD license. When you’re logged in for the first time, you’ll see the following screen:
Click on Enter license key and fill in the SCD Academic customer license key from the e-mail you received from us, and click ‘validate’.
If you do not see this screen on startup, go to settings > view and edit license > enter a license, and enter the SCD license key.
After validating the SCD license key, click on ‘License status’ to check if it was successfully validated. The license type should be ‘SCD Academic License’.
2. To get access to your data, you need to connect to the SCD BioTuring server (BESP server). To do this, go to settings > BESP settings and enter the following information:
Server IP: bioturing.scdiscoveries.com
Click on Connect to connect to the server.
Upload your own data
With the SCD Academic license, you have the possibility to import your own Seurat or Scanpy objects. To enable this functionality, you will need to use the “local” mode for the BioTuring Browser.
To activate the local mode, click on the Data tab and then on connected. The button will change to local. (To switch back to server mode, click the local button again.)
To add your own data in the form of a Seurat object, click on the plus sign on the left. When the menu opens, it says “New study”.
Next, click on “Custom data”
Then, click on “Seurat object” and select the Seurat object from your local computer to upload it. After this step, follow the instructions in the software to finish the import. NOTE: please make sure to switch to LOCAL mode, otherwise you will not be able to import your data.
Available analyses with the SCD License
These are the differences between the different licenses. Please note that you will receive an academic or commercial license based on if you are an academic (universities/institutes) or commercial (biotech/pharma) client of Single Cell Discoveries and that this cannot be changed. The BBrowser can be acquired at the BioTuring website.
|SCD Academic License||SCD Commercial License||BBrowser Advanced License|
|License duration||Unlimited||4 weeks||Unlimited|
|Query gene expression||Yes||Yes||Yes|
|Plot gene expression (e.g. violin plots, heatmaps, density plots)||Yes||Yes||Yes|
|Find marker genes||Yes||Yes||Yes|
|Study cell composition||Yes||Yes||Yes|
|Cell Type Prediction based on user-input knowledgebase||Yes||Yes||Yes|
|Run enrichment analysis||Yes||Yes||Yes|
|Import Seurat/Scanpy objects||Yes (local mode only)||Yes (local mode only)||Yes|
|Import FASTQ files, count matrices||No||Yes (local mode only)||Yes|
|Download BioTuring curated datasets||No||No||Yes|
|Run differential expression analysis||No||Yes||Yes|
|Find variable genes along cell trajectories||No||Yes||Yes|
Most important functionalities (for academic users)
Here, we highlight the top functionalities for the academic license.
Cell Search within published data (BioTuring database)
The Cell Search Engine is designed to help you find cell populations in BioTuring public database which have similar transcription profiles to your selected cells – suggesting the cell type and signature genes, enrichment processes of the selected group.
When you select any group of cells in the analysis dashboard, an info box will appear at the top left corner asking you to search for similar populations.
Just click on the Search button. A summary of the selected cells’ profiles will be sent to our server to query for similar profiles (this step needs a stable internet connection).
The server will return results in a pop-up window, including:
- A list of similarly expressed genes shared across all matched populations.
- Cell Ontology: This tree chart visualizes the cell search result using BioTuring’s Cell Ontology . The node size indicates the number of cells that are found to match the query profile. The node with bigger size shows that the algorithm found more cells that match the query profile than other cells. The edge width of a node indicates the number of all studies that are found to have a profile matches the query profile.
- A list of the public datasets that matches with your selected cells. They will be ordered from the most similar to least similar cell population.
- Similarity score: Similarity scores of matched population will be calculated by Jaccard index. It is also called the Jaccard similarity coefficient. The list of published studies with matched populations will be ordered from highest to lowest similarity score.
- The pie chart, showing the composition of each similar population, e.g. by therapies, tissue, patients, treatments, response levels to therapies, etc.
This is a beta version of the function. We are looking forward to your thoughts and comments to improve it for future release. Click on “Share your thoughts on this function” to leave your feedback.
All the cell search queries will be automatically saved in the Cell search tab to reopen, rename or reselect . You also can delete them. You can watch the video tutorial here.
Find marker genes or proteins and enriched processes
Finding marker genes/proteins and enriched processes in a group of cells helps you to see the genes/proteins and processes that are differently expressed in that selected group, compared to the rest of the cell population. The information is essential to define which cell type the cluster belongs to. It can be used as an alternative to the differential gene expression function.
To run the analysis:
Select a group of cells by following the Go to the Marker features tab on the right, click on Find markers.
Or go to the Enrichment analysis and click Run enrichment analysis. All enriched processes will be ready to be explored.
Each gene or process comes with the p-value and biological details related to it. You can use the Search box to look for a gene or a process of interest.
Details on the markers and enrichment analysis include:
By default, marker genes or proteins will be sorted by order of significant (p-value) with the most significant gene coming first. Each page shows you 10 marker genes, to continue browsing, go to the next page. To view all details, click on the View table in a new window icon on the top right corner.
Together with gene name and p-value, the software will also show you the type of marker gene, dissimilarity, log2FC, Ensembl ID, Percentage 1 (percentage of positive cells within the population of interest), Percentage 2 (percentage of positive cells in the remaining cells). To view such information, click the middle column’s header in the marker gene table (next to the Gene name column).
Type of marker genes/proteins: up-regulated, down-regulated or transitional. Transitional marker genes are genes that are not exclusively expressed or repressed in the given cluster but show expression in multiple clusters, and its expression level is distinctive for each cluster. The classification is taken from Venice.
Dissimilarity: this score indicates if the selected cells are different and can be separated from the non-selected population by constructing a simple classifier based on the given gene expression. If the classifier can determine whether a cell is coming from the selected or non-selected group with 100% accuracy, dissimilarity will be 1.
Log2FC: log2-fold-change of each gene is the ratio of the means of expression of that gene in the cells selected, compared to the rest of the cell population.
From version 2.10.3, all marker detection will be performed by Venice (Vuong et al. 2019).
By default, enriched biological processes are displayed by order of significant (p-value) with the most significant process coming first. Each page shows you 15 enriched processes, to continue browsing, go to the next page.
Each enriched biological process will come with an enrichment score (ES), which is based on Gene Set Enrichment Analysis (GSEA) method. The GSEA method can identify classes of genes or proteins that are over-represented in a large set of genes or proteins; these classes can be associated with biological functions or disease phenotypes (Aravind Subramanian et al, 2005).
To view enriched molecular functions, cellular components, and pathways, click on the drop-down next to the first column name.
To view details about each process or pathway, click on the icon in the Source column. This will connect you to the database (gene ontology or reactome) and go directly to the specific page of the chosen process.
No, this is included with our single-cell sequencing services.
Please contact BioTuring to arrange this.
Please contact us at email@example.com
For commercial clients, this is possible. For academic users, this function is not available. However, you can do a similar analysis with the Marker Features function.
It is possible to upload the Seurat object provided by us, in local mode only. We cannot upload data generated before January 19, 2022 for you.
Yes, your colleagues can install the BBrowser to analyze the same dataset for themselves. However, you can only log in with the email address that we use to send you your data, since this is coupled to your dataset.
We upload your data from our SORT-seq, VASA-seq and 10x Genomics Single Cell Gene Expression services.
For 10x Genomics Single Cell Immune Profiling projects, you will be able to analyze the gene expression library in the BBrowser.
Please check if you are in local mode, if you are connected to the server it will not work. Please contact us if this doesn’t work.
Unfortunately, this is not possible.