Journal
DISEASE MODELS & MECHANISMS
Volume 15, Issue 3, Pages -Publisher
COMPANY BIOLOGISTS LTD
DOI: 10.1242/dmm.049257
Keywords
Bioinformatics; Data analytics; RNA-seq
Categories
Funding
- Cancer Research UK [A26825, A28223, A29834]
- Fundacion Cientifica Asociacion Espanola Contra el Cancer [GEACC18004TAB]
- Associazione Italiana per la Ricerca sul Cancro [22795]
- Health Data Research UK
Ask authors/readers for more resources
We have developed an open source data analysis platform called MouSR, which allows 'wet-lab' users to easily analyze transcriptional data without requiring programming skills. This platform provides a user-friendly interface and a suite of molecular characterization options for rapid transcriptomic analysis, addressing a major bottleneck in biological discovery.
Generation of transcriptional data has dramatically increased in the past decade, driving the development of analytical algorithms that enable interrogation of the biology underpinning the profiled samples. However, these resources require users to have expertise in data wrangling and analytics, reducing opportunities for biological discovery by 'wet-lab' users with a limited programming skillset. Although commercial solutions exist, costs for software access can be prohibitive for academic research groups. To address these challenges, we have developed an open source and user-friendly data analysis platform for on-the-fly bioinformatic interrogation of transcriptional data derived from human or mouse tissue, called Molecular Subtyping Resource (MouSR). This internet-accessible analytical tool, https:/ mousr.qub.ac.uk/, enables users to easily interrogate their data using an intuitive 'point-and-click' interface, which includes a suite of molecular characterisation options including quality control, differential gene expression, gene set enrichment and microenvironmental cell population analyses from RNA sequencing. The MouSR online tool provides a unique freely available option for users to perform rapid transcriptomic analyses and comprehensive interrogation of the signalling underpinning transcriptional datasets, which alleviates a major bottleneck for biological discovery.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available