Hi, I would like to introduce BioGraphServ, which is a webapp for quickly and easily generating graphs and analysis.
Drag & drop small bioinformatics files such as BED, expression and VCF files onto the page to upload, BGS will detect the file type, generating the appropriate default graphs and analysis.
Graphs can be further customised and downloaded in multiple formats, and data from multiple files combined for download or further analysis.
A quick overview (with screenshots) can be found here:
Some things you can do:
The motivation for this project was seeing large numbers of biologists who wish to produce the same "standard plots" for their data. Long term, I'd love for others to collaborate and contribute new graphs and ideas, so we can expand out what biologists can do without needing to code (so we bioinformaticians have more time to work on newer, cooler projects!)
Technical implementation: Server side is Python using the Django framework. Graphs/Analysis use Pandas and Matplotlib. Work is dispatched to asynchronous workers (Celery), which may call a reference server (with annotations pre-loaded into RAM for quick results)
Graphs are pickled and stored as binary blobs in the database so you can change the colors/title etc or render them to different image formats.
I look forward to hearing your feedback / advice. Thanks!
--
David M Lawrence
Bioinformatics Research Fellow
CCB ACRF Cancer Genomics Facility
Adelaide, South Australia
Drag & drop small bioinformatics files such as BED, expression and VCF files onto the page to upload, BGS will detect the file type, generating the appropriate default graphs and analysis.
Graphs can be further customised and downloaded in multiple formats, and data from multiple files combined for download or further analysis.
A quick overview (with screenshots) can be found here:
Some things you can do:
- Generate SVD / PCA plots, and color them by sample groups
- Correlations, volcano plots, heat maps, chromosome ideograms
- Assign bed file peaks to genes (using customisable ranges)
- Join together data by common row name (eg expression counts for genes from different files)
The motivation for this project was seeing large numbers of biologists who wish to produce the same "standard plots" for their data. Long term, I'd love for others to collaborate and contribute new graphs and ideas, so we can expand out what biologists can do without needing to code (so we bioinformaticians have more time to work on newer, cooler projects!)
Technical implementation: Server side is Python using the Django framework. Graphs/Analysis use Pandas and Matplotlib. Work is dispatched to asynchronous workers (Celery), which may call a reference server (with annotations pre-loaded into RAM for quick results)
Graphs are pickled and stored as binary blobs in the database so you can change the colors/title etc or render them to different image formats.
I look forward to hearing your feedback / advice. Thanks!
--
David M Lawrence
Bioinformatics Research Fellow
CCB ACRF Cancer Genomics Facility
Adelaide, South Australia