Verheggen Kenneth, Maddelein Davy, Hulstaert Niels, Martens Lennart, Barsnes Harald, Vaudel Marc
Medical Biotechnology Center, VIB , Albert Baertsoenkaai 3, Ghent B-9000, Belgium.
Department of Biochemistry, Ghent University , Albert Baertsoenkaai 3, Ghent B-9000, Belgium.
J Proteome Res. 2016 Mar 4;15(3):707-12. doi: 10.1021/acs.jproteome.5b00850. Epub 2015 Nov 6.
The use of proteomics bioinformatics substantially contributes to an improved understanding of proteomes, but this novel and in-depth knowledge comes at the cost of increased computational complexity. Parallelization across multiple computers, a strategy termed distributed computing, can be used to handle this increased complexity; however, setting up and maintaining a distributed computing infrastructure requires resources and skills that are not readily available to most research groups. Here we propose a free and open-source framework named Pladipus that greatly facilitates the establishment of distributed computing networks for proteomics bioinformatics tools. Pladipus is straightforward to install and operate thanks to its user-friendly graphical interface, allowing complex bioinformatics tasks to be run easily on a network instead of a single computer. As a result, any researcher can benefit from the increased computational efficiency provided by distributed computing, hence empowering them to tackle more complex bioinformatics challenges. Notably, it enables any research group to perform large-scale reprocessing of publicly available proteomics data, thus supporting the scientific community in mining these data for novel discoveries.
蛋白质组学生物信息学的应用极大地有助于增进对蛋白质组的理解，但这种新颖且深入的知识是以增加计算复杂性为代价的。跨多台计算机进行并行化处理，即一种称为分布式计算的策略，可用于应对这种增加的复杂性；然而，建立和维护分布式计算基础设施需要大多数研究团队难以轻易获取的资源和技能。在此，我们提出了一个名为Pladipus的免费开源框架，它极大地促进了用于蛋白质组学生物信息学工具的分布式计算网络的建立。由于其用户友好的图形界面，Pladipus易于安装和操作，使得复杂生物信息学任务能够在网络而非单台计算机上轻松运行。因此，任何研究人员都能受益于分布式计算带来的更高计算效率，从而使他们有能力应对更复杂的生物信息学挑战。值得注意的是，它使任何研究团队都能够对公开可用的蛋白质组学数据进行大规模重新处理，从而支持科学界挖掘这些数据以获得新发现。
