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分子分型资源:一个用户友好的工具,可从转录数据中快速进行生物学发现。

Molecular Subtyping Resource: a user-friendly tool for rapid biological discovery from transcriptional data.

机构信息

The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast BT9 7AE, UK.

Information Services, Queen's University Belfast, Belfast BT7 1NN, UK.

出版信息

Dis Model Mech. 2022 Mar 1;15(3). doi: 10.1242/dmm.049257. Epub 2022 Mar 30.

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.

摘要

在过去的十年中,转录组数据的生成急剧增加,推动了分析算法的发展,这些算法能够探究所分析样本的生物学基础。然而,这些资源要求用户具备数据管理和分析方面的专业知识,这降低了具有有限编程技能的“湿实验室”用户进行生物学发现的机会。尽管存在商业解决方案,但软件访问的成本对于学术研究小组来说可能是昂贵的。为了解决这些挑战,我们开发了一个用于实时生物信息学分析源自人类或小鼠组织的转录组数据的开源且用户友好的数据分析平台,称为分子分型资源(MouSR)。这个可通过互联网访问的分析工具,https://mousr.qub.ac.uk/,使用户能够使用直观的“点击”界面轻松地分析他们的数据,其中包括一系列分子特征化选项,包括质量控制、差异基因表达、基因集富集和来自 RNA 测序的微环境细胞群体分析。MouSR 在线工具为用户提供了一个独特的免费选项,用于进行快速转录组分析和全面探究转录组数据集的信号,这缓解了生物学发现的一个主要瓶颈。本文附有该论文第一作者的第一人称采访。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10b5/8990914/46dc22ecc387/dmm-15-049257-g1.jpg

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