Maes Alexandre, Martinez Xavier, Druart Karen, Laurent Benoist, Guégan Sean, Marchand Christophe H, Lemaire Stéphane D, Baaden Marc
Laboratoire de Biologie Moléculaire et Cellulaire des Eucaryotes, Institut de Biologie Physico-Chimique, UMR8226, CNRS, Sorbonne Université, 13 rue Pierre et Marie Curie, 75005, Paris, France.
Laboratoire de Biochimie Théorique, CNRS, UPR9080, Institut de Biologie Physico-Chimique, Univ Paris Diderot, Sorbonne Paris Cité, PSL Research University, 13 rue Pierre et Marie Curie, 75005, Paris, France.
J Integr Bioinform. 2018 Jun 21;15(2):20180006. doi: 10.1515/jib-2018-0006.
Proteomic and transcriptomic technologies resulted in massive biological datasets, their interpretation requiring sophisticated computational strategies. Efficient and intuitive real-time analysis remains challenging. We use proteomic data on 1417 proteins of the green microalga Chlamydomonas reinhardtii to investigate physicochemical parameters governing selectivity of three cysteine-based redox post translational modifications (PTM): glutathionylation (SSG), nitrosylation (SNO) and disulphide bonds (SS) reduced by thioredoxins. We aim to understand underlying molecular mechanisms and structural determinants through integration of redox proteome data from gene- to structural level. Our interactive visual analytics approach on an 8.3 m2 display wall of 25 MPixel resolution features stereoscopic three dimensions (3D) representation performed by UnityMol WebGL. Virtual reality headsets complement the range of usage configurations for fully immersive tasks. Our experiments confirm that fast access to a rich cross-linked database is necessary for immersive analysis of structural data. We emphasize the possibility to display complex data structures and relationships in 3D, intrinsic to molecular structure visualization, but less common for omics-network analysis. Our setup is powered by MinOmics, an integrated analysis pipeline and visualization framework dedicated to multi-omics analysis. MinOmics integrates data from various sources into a materialized physical repository. We evaluate its performance, a design criterion for the framework.
蛋白质组学和转录组学技术产生了大量的生物学数据集,对其进行解读需要复杂的计算策略。高效且直观的实时分析仍然具有挑战性。我们使用莱茵衣藻1417种蛋白质的蛋白质组学数据,来研究控制三种基于半胱氨酸的氧化还原翻译后修饰(PTM)选择性的物理化学参数:谷胱甘肽化(SSG)、亚硝基化(SNO)和由硫氧还蛋白还原的二硫键(SS)。我们旨在通过整合从基因水平到结构水平的氧化还原蛋白质组数据,来理解潜在的分子机制和结构决定因素。我们在一个分辨率为2500万像素、面积为8.3平方米的展示墙上采用交互式视觉分析方法,通过UnityMol WebGL进行立体三维(3D)展示。虚拟现实头戴设备补充了用于完全沉浸式任务的一系列使用配置。我们的实验证实,对于结构数据的沉浸式分析而言,快速访问丰富的交联数据库是必要的。我们强调了以三维形式显示复杂数据结构和关系的可能性,这对于分子结构可视化来说是内在的,但在组学网络分析中不太常见。我们的设置由MinOmics提供支持,MinOmics是一个致力于多组学分析的集成分析管道和可视化框架。MinOmics将来自各种来源的数据整合到一个物化的物理存储库中。我们评估了它的性能,这是该框架的一个设计标准。