Evidence map›Paper›PMID 29927748›Full record

ArticleJournal of integrative bioinformatics2018

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

Alexandre Maes, Xavier Martinez, Karen Druart, Benoist Laurent, Sean Guégan, Christophe H Marchand, Stéphane D Lemaire, Marc Baaden

Open access · diamondAbstract read
In one paragraph

Article in Journal of integrative bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
2.0field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 32 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Review
  5. From Virtual Reality to Immersive Analytics in Bioinformatics.Journal of integrative bioinformatics · 2018
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 1 country.

Alexandre MaesLaboratoire 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.
Xavier MartinezLaboratoire 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.
Karen DruartLaboratoire 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.
Benoist LaurentInstitut de Biologie Physico-Chimique, FRC 550, CNRS, Paris, France.
Sean GuéganLaboratoire 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.
Christophe H MarchandLaboratoire 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.
Stéphane D LemaireLaboratoire 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.
Marc BaadenLaboratoire 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.
Centre National de la Recherche Scientifique · FRInstitut de Biologie Physico-Chimique · FRUniversité Paris Sciences et Lettres · FRSorbonne Université · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Computer GraphicsProtein Processing, Post-TranslationalSoftwareAlgal ProteinsChlamydomonas reinhardtiiImaging, Three-DimensionalModels, StructuralOxidation-ReductionProtein ConformationProtein Interaction MapsProteomeProteomicsVirtual RealityAlgal ProteinsProteomeDatabaseDisplay wallOmicsProtein and proteomeVirtual Reality (VR)

Identifiers

PMID29927748
PMCPMC6167043
OpenAlexW2877867551

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.