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MinOmics an Integrative and Immersive Tool for Multi-Omics Analysis

机译:MinOmics用于多组学分析的集成式和浸入式工具

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摘要

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.
机译:蛋白质组学和转录组学技术产生了大量的生物学数据集,其解释需要复杂的计算策略。高效,直观的实时分析仍然具有挑战性。我们使用蛋白质组学数据,对绿色微藻衣藻(Chlamydomonas reinhardtii)的1417种蛋白质进行研究,探讨控制三种基于半胱氨​​酸的氧化还原后翻译修饰(PTM)的选择性的理化参数:谷胱甘肽化(SSG),亚硝基化(SNO)和硫氧还蛋白还原的二硫键(SS) 。我们旨在通过整合氧化还原蛋白质组数据从基因水平到结构水平来理解潜在的分子机制和结构决定因素。我们的交互式视觉分析方法在分辨率为25 MPixel的8.3 m 2 显示屏上具有UnityMol WebGL进行的三维立体(3D)表示。虚拟现实头戴式耳机补充了完全沉浸式任务的使用配置范围。我们的实验证实,对结构数据进行沉浸式分析必须快速访问丰富的交联数据库。我们强调了在分子结构可视化中固有的,以3D显示复杂数据结构和关系的可能性,但对于组学网络分析,这种可能性不那么普遍。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集成到物化的物理存储库中。我们评估其性能,这是该框架的设计标准。

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