Single cell RNA-seq data analysis, without coding
Turn your single cell RNA-sequencing data into meaningful insight with Trailmaker®
Explore supported data from 10x Genomics™ Chromium™, Parse Biosciences Evercode™ and BD Rhapsody™ — from quality control and sample integration to gene expression and publication-ready figures.
Free for all academic researchers (including those who are not Parse customers).
Looking for the Biomage-hosted community instance of Cellenics®?
The Biomage-hosted instance of Cellenics® was rebranded as Trailmaker® following Biomage’s acquisition by Parse Biosciences.
Trailmaker supports researchers working with supported data from multiple technology providers, including 10x Genomics Chromium, Parse Biosciences Evercode and BD Rhapsody, taking you through the essential data analysis steps without writing a single line of code.
Access remains FREE for all academic users - academic researchers do not need to purchase Parse products to use it.
Existing users can access Trailmaker® through the current application. All scientific data is preserved within your account.
Analyze the single cell data you already have
Working with 10x Genomics Chromium or another single-cell RNA-seq technology?
Explore supported processed data in Trailmaker Insights, without coding.
| Your data | What to upload or select | Where to start |
|---|---|---|
| 10x Genomics Chromium | Supported Cell Ranger™ gene expression count matrices or H5 files | Upload to Trailmaker Insights |
| Parse Biosciences Evercode | FASTQ files for processing, or processed count matrices for downstream analysis | Start with Pipeline for FASTQs, or upload processed data directly to Insights |
| BD Rhapsody and other technologies | Processed gene expression count matrices in a supported format | Upload to Trailmaker Insights |
| AnnData or Seurat objects | Supported objects that meet the requirements in the Trailmaker user guide | Upload to Trailmaker Insights |
| Just exploring? | A publicly available dataset from the Trailmaker data repository | Choose a repository dataset to explore in Insights |
See the Trailmaker user guide for specific file format requirements and upload instructions.
Explore your single cell RNA-seq data with Trailmaker
Navigate through the essential data processing steps with ease: data cleanup (filtering), normalization, integration, and UMAP and clustering generation. Then dive deep into gene expression, differential expression and pathway analysis, before finalizing your analysis with custom plots and figures.
What Researchers Say About Trailmaker®
Don’t believe us - believe our scientific users!
“I attended yesterday’s webinar and was impressed with this tool! This webinar was delivered at a really good time for me since I was about to start watching a lot of tutorials to learn how to use R and Seurat to analyze 10x Genomics data that we just received. So, you can imagine how happy I am to skip all that and just go ahead and analyze my data!”
Antonio Solis, Texas Biomedical Research Institute
Published papers with Cellenics®, now Trailmaker®:
Cancer immunology: Exploring how ENPP1 suppresses antitumour immunity — Wang et al., PNAS.
Infectious disease: Investigating viral reservoirs after treatment interruption — Solis-Leal et al., Science Translational Medicine.
Neuroscience: Investigating immune-cell responses after brain injury — Soliman et al., Journal of Neuroinflammation.
Learn single cell RNA-seq data analysis with our free certified course
Our free comprehensive course “Mastering Single Cell RNA-seq Data Analysis” is designed to equip you with the knowledge and skills needed to analyze single cell RNA-seq data using Trailmaker®.
From data uploading and processing to integration, exploration, and visualization, we will cover everything you need to know to become an expert in single cell RNA-seq data analysis.
Included in the online course: 41 video lessons over 9+ hours that you can watch at your own pace + downloadable PDF course material.
Single cell RNA-seq data analysis: your questions answered
Find out how to analyse single cell RNA-seq data without coding, which files you can upload to Trailmaker, and who can access the platform for free.
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Trailmaker is a browser-based option for analysing single cell RNA-seq data without writing code. It is free for academic researchers, including those who do not use Parse Biosciences products. For a full tool comparison, read this article.
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Yes - Trailmaker is free for all academic users and all Parse Biosciences customers, with no data or usage limits. If you work outside academia and are not a Parse customer, contact Parse Biosciences to discuss access.
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No. Academic researchers can use Trailmaker even if they have never purchased Parse Biosciences products. You can analyse supported single-cell RNA-seq data from technologies including 10x Genomics Chromium and BD Rhapsody. Explore Trailmaker.
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Yes. You can upload supported Cell Ranger gene-expression count matrices or H5 files to Trailmaker Insights module for downstream analysis without coding. This workflow starts with processed data and follows the essential data cleanup, normalization and integration steps (in Scanpy or Seurat) with interactive data visualization and publication-ready plot generation. Get started with Trailmaker (it’s free for all academic researchers!)
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Following Biomage’s acquisition by Parse Biosciences, the Biomage-hosted community instance of Cellenics was rebranded as Trailmaker. Existing users’ scientific data was preserved within their accounts. You can now access the platform through the Trailmaker application. Read the acquisition announcement.
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Yes. Trailmaker includes a repository of public datasets that you can explore without uploading your own data. It’s a useful way to try the platform, practise the analysis workflow and explore its visualisation tools. Follow the guide to exploring demo datasets to get started.
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Trailmaker Insights accepts supported gene-expression count matrices from Parse Evercode and 10x Chromium, BD Rhapsody expression files, Cell Ranger-style H5 files, AnnData objects (.h5ad), and Seurat v4 or v5 objects (.rds). Objects must meet specific formatting requirements. Trailmaker Pipeline separately accepts FASTQ files from supported Parse Evercode experiments. Check the file requirements and upload instructions for detailed requirements and instructions.
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The free certified course “Mastering Single Cell RNA-seq Data Analysis” guides you through the essential steps in the data analysis workflow. You can follow along with the analysis in Trailmaker. Register for the course here.
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Yes. As of November 2026, we have identified more than 100 published papers and preprints referencing Trailmaker or the Biomage-hosted Cellenics platform. These include research using the platform for single cell data processing, analysis and visualisation. (Note that preprints and their corresponding journal articles were counted once.)