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生物信息学在湿实验科学家中的应用:测序分析中的实际应用。

Bioinformatics for wet-lab scientists: practical application in sequencing analysis.

机构信息

Neurological Institute (Edinger Institute), University Hospital Frankfurt, Goethe University, Frankfurt, Germany.

Department of Biological Sciences, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad, Telangana, India.

出版信息

BMC Genomics. 2023 Jul 7;24(1):382. doi: 10.1186/s12864-023-09454-7.

Abstract

BACKGROUND

Genomics data is available to the scientific community after publication of research projects and can be investigated for a multitude of research questions. However, in many cases deposited data is only assessed and used for the initial publication, resulting in valuable resources not being exploited to their full depth. MAIN: A likely reason for this is that many wetlab-based researchers are not formally trained to apply bioinformatic tools and may therefore assume that they lack the necessary experience to do so themselves. In this article, we present a series of freely available, predominantly web-based platforms and bioinformatic tools that can be combined in analysis pipelines to interrogate different types of next-generation sequencing data. Additionally to the presented exemplary route, we also list a number of alternative tools that can be combined in a mix-and-match fashion. We place special emphasis on tools that can be followed and used correctly without extensive prior knowledge in programming. Such analysis pipelines can be applied to existing data downloaded from the public domain or be compared to the results of own experiments.

CONCLUSION

Integrating transcription factor binding to chromatin (ChIP-seq) with transcriptional output (RNA-seq) and chromatin accessibility (ATAC-seq) can not only assist to form a deeper understanding of the molecular interactions underlying transcriptional regulation but will also help establishing new hypotheses and pre-testing them in silico.

摘要

背景

基因组学数据在研究项目发表后可供科学界使用,可用于研究多个研究问题。然而,在许多情况下,存储的数据仅被评估并用于最初的出版物,导致有价值的资源没有被充分利用。主要原因之一是,许多基于湿实验的研究人员没有接受过应用生物信息学工具的正式培训,因此可能认为他们缺乏自己动手所需的经验。在本文中,我们介绍了一系列免费提供的、主要基于网络的平台和生物信息学工具,这些工具可以组合在分析管道中,以分析不同类型的下一代测序数据。除了本文介绍的示例流程外,我们还列出了一些可以混合搭配使用的替代工具。我们特别强调那些无需广泛的编程知识就可以遵循和正确使用的工具。这些分析管道可以应用于从公共领域下载的现有数据,也可以与自己的实验结果进行比较。

结论

将转录因子与染色质结合(ChIP-seq)与转录产物(RNA-seq)和染色质可及性(ATAC-seq)相结合,不仅可以帮助我们更深入地了解转录调控背后的分子相互作用,还可以帮助建立新的假设,并在计算机上进行预测试。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c50e/10326960/2f866275b185/12864_2023_9454_Fig1_HTML.jpg

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