Molecular Subtyping Resource: a user-friendly tool for rapid biological discovery from transcriptional data.
Molecular Subtyping Resource: a user-friendly tool for rapid biological discovery from transcriptional data.
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DOI:
10.1242/dmm.049257
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发表时间:
2022-03-01
影响因子:
4.3
通讯作者:
Dunne PD
中科院分区:
文献类型:
--
作者:
Ahmaderaghi B;Amirkhah R;Jackson J;Lannagan TRM;Gilroy K;Malla SB;Redmond KL;Quinn G;McDade SS;ACRCelerate Consortium;Maughan T;Leedham S;Campbell ASD;Sansom OJ;Lawler M;Dunne PD
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, , 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. . Summary: We developed an open source and user-friendly data analysis tool, Molecular Subtyping Resource (MouSR), which enables users to perform rapid transcriptomic analyses and comprehensive interrogation of the signalling underpinning transcriptional datasets, potentially facilitating biological discovery.
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