Unknown

Dataset Information

0

MinOmics, an Integrative and Immersive Tool for Multi-Omics Analysis.


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.

SUBMITTER: Maes A 

PROVIDER: S-EPMC6167043 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

altmetric image

Publications

MinOmics, an Integrative and Immersive Tool for Multi-Omics Analysis.

Maes Alexandre A   Martinez Xavier X   Druart Karen K   Laurent Benoist B   Guégan Sean S   Marchand Christophe H CH   Lemaire Stéphane D SD   Baaden Marc M  

Journal of integrative bioinformatics 20180621 2


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  ...[more]

Similar Datasets

| S-EPMC5918456 | biostudies-literature
| S-EPMC3901289 | biostudies-other
| S-EPMC9922241 | biostudies-literature
| S-EPMC6018986 | biostudies-literature
| S-EPMC10019735 | biostudies-literature
| S-EPMC4133046 | biostudies-literature
| S-EPMC4945831 | biostudies-literature
| S-EPMC10518515 | biostudies-literature
| S-EPMC7754008 | biostudies-literature
| S-EPMC8795899 | biostudies-literature