Molecular Subtyping Resource: a user-friendly tool for rapid biological discovery from transcriptional data.
Baharak AhmaderaghiRaheleh AmirkhahJames JacksonTamsin R M LannaganKathryn GilroySudhir B MallaKeara L RedmondGerard QuinnSimon S McDadenull ACRCelerate ConsortiumTim MaughanSimon LeedhamAndrew S D CampbellOwen J SansomMark LawlerPhilip David DunnePublished in: Disease models & mechanisms (2022)
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. This article has an associated First Person interview with the first author of the paper.
Keyphrases
- data analysis
- gene expression
- big data
- electronic health record
- single cell
- small molecule
- transcription factor
- high throughput
- machine learning
- endothelial cells
- rna seq
- single molecule
- healthcare
- oxidative stress
- artificial intelligence
- cell therapy
- social media
- deep learning
- copy number
- bone marrow
- heat stress
- loop mediated isothermal amplification