Abstract
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.
Highlights
The analysis of massive biological datasets available on public repositories is nowadays one of the key challenges biologists face due to the increasing size, variety and complexity of such data
We explored an alternative way for MinOmics and UnityMol to communicate by using a standalone version of UnityMol outside a web context receiving data via a web socket or checking for new files to read in a folder
Considering the MinOmics framework and the different devices available to explore and analyse our dataset, we identified different scenarios for different tasks in which multiple users can interact
Summary
The analysis of massive biological datasets available on public repositories is nowadays one of the key challenges biologists face due to the increasing size, variety and complexity of such data. Modern high-throughput and large-scale analytical technologies create an explosion of experimental datasets of quantitative and qualitative biological information, often called “omics” [1]. One of many related challenges is to provide integrated and meaningful information for the biologist, accessible even without programming expertise. Visual analytics is a key strategy for this task [13]. Immersion and interactivity to delve into large biological datasets require the combination of ultra-fast access to databases, complex on-the-fly queries, analyses and efficient visualization tools
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