Abstract
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.
Original language | English |
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Journal | Disease Models and Mechanisms |
Volume | 15 |
Issue number | 3 |
Early online date | 03 Feb 2022 |
DOIs | |
Publication status | Published - 30 Mar 2022 |
Bibliographical note
© 2022. Published by The Company of Biologists Ltd.Keywords
- General Biochemistry, Genetics and Molecular Biology
- Immunology and Microbiology (miscellaneous)
- Medicine (miscellaneous)
- Neuroscience (miscellaneous)
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Data Portal for human and mouse molecular data analyses
Dunne, P. (Creator), Queen's University Belfast, 2022
DOI: 10.1242/dmm.049257
Dataset
Student theses
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Ontology-led molecular classification of colorectal cancer uncovers distinct tumour subtypes with therapeutic and clinical relevance
Author: Malla, S., Jul 2022Supervisor: Dunne, P. (Supervisor), Longley, D. (Supervisor) & Lawler, M. (Supervisor)
Student thesis: Doctoral Thesis › Doctor of Philosophy