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. 2019 Mar 27:7:e6623.
doi: 10.7717/peerj.6623. eCollection 2019.

Pixel: a content management platform for quantitative omics data

Thomas Denecker #  1 , William Durand #  2 , Julien Maupetit #  2 , Charles Hébert  3 , Jean-Michel Camadro  4 , Pierre Poulain #  4 , Gaëlle Lelandais #  1
Affiliations

Pixel: a content management platform for quantitative omics data

Thomas Denecker et al. PeerJ. .

Abstract

Background: In biology, high-throughput experimental technologies, also referred as "omics" technologies, are increasingly used in research laboratories. Several thousands of gene expression measurements can be obtained in a single experiment. Researchers are routinely facing the challenge to annotate, store, explore and mine all the biological information they have at their disposal. We present here the Pixel web application (Pixel Web App), an original content management platform to help people involved in a multi-omics biological project.

Methods: The Pixel Web App is built with open source technologies and hosted on the collaborative development platform GitHub (https://github.com/Candihub/pixel). It is written in Python using the Django framework and stores all the data in a PostgreSQL database. It is developed in the open and licensed under the BSD 3-clause license. The Pixel Web App is also heavily tested with both unit and functional tests, a strong code coverage and continuous integration provided by CircleCI. To ease the development and the deployment of the Pixel Web App, Docker and Docker Compose are used to bundle the application as well as its dependencies.

Results: The Pixel Web App offers researchers an intuitive way to annotate, store, explore and mine their multi-omics results. It can be installed on a personal computer or on a server to fit the needs of many users. In addition, anyone can enhance the application to better suit their needs, either by contributing directly on GitHub (encouraged) or by extending Pixel on their own. The Pixel Web App does not provide any computational programs to analyze the data. Still, it helps to rapidly explore and mine existing results and holds a strategic position in the management of research data.

Keywords: Data cycle analyses; Omics; Open source; Pixel Web App.

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Conflict of interest statement

William Durand and Julien Maupetit are employed by TailorDev SAS. Charles Hébert is employed by Biorosetics.

Figures

Figure 1
Figure 1. Dataset flow through the Pixel Web App.
(A) Different types of datasets, which are managed in a multi-omics biological project. Primary and secondary datasets are two types of information arising from HT experimental technologies (see ‘Introduction’). Only secondary data and their associated Pixel Sets are stored in the Pixel Web App. Note that several Pixel Sets can emerge from multiple secondary data analyses. They comprise quantitative values (Value) together with quality scores (QS) for several hundred of different “Omics Units” elements (for instance mRNA or proteins, see the main text). Omics Units are identified with a unique identifier (ID). (B) Screenshot of the home page of the Pixel web interface. (C) Schematic representation of the data analysis cycles that surrounds the integration of Pixel Sets in the Pixel Web App (see the main text).
Figure 2
Figure 2. Stack overview of the Pixel Web App.
Open source solutions used to develop Pixel are shown here. They are respectively used for the software development and test (blue section), the data storage (green section) and the web application for both staging and production (orange section).
Figure 3
Figure 3. Data modelling in the Pixel Web App.
The Pixel Set is the central information (see Fig. 1A), the corresponding table in the model is highlighted in red. Information that is required before Pixel Set import in the Pixel Web App is surrounded in blue, whereas information required during Pixel Set import is highlighted in orange. Other tables are automatically updated during the Pixel Web App data analysis life cycle (see Fig. 1C). An enlarged version of this picture together with full documentation is available online https://github.com/Candihub/pixel/blob/master/docs/pixel-db.pdf.
Figure 4
Figure 4. Procedure to import new Pixel Sets in the Pixel Web App.
(A) New data-sets are submitted following a dedicated workflow that comprised 6 successive actions named “Download”, “Upload”, “Meta”, “Validation”, “Tags” and “Import archive” (see 1). Several files are required (see 2): the secondary data from which the Pixel Sets were calculated, the notebook in which the procedure to compute Pixel Sets from secondary data is described and the Pixel Set files (2 files in this example). A progression bar allows the user to follow the sequence of the submission process. (B) Excel spreadsheet in which annotations of Pixel Sets are written. Information related to the Experiment (see 1), the Analysis (see 2) and the Pixel datasets (see 3) is required. Note that this file must be downloaded at the first step of the submission process (“Download”, see A), allowing several cells to be pre-filled with annotations stored in the database (see 4 as an illustration, with Omics area information). (C) All information filled in the Excel file (see B) is extracted and can be modified anytime through a dedicated web page as shown here. User can edit the Pixel Set (see 1), edit the analysis(see 2), edit the experiment (see 3) and add “Tags” (see 4). The Tags are of interest to further explore Pixel Sets in the Pixel Web App.
Figure 5
Figure 5. Functionalities to explore the Pixel Sets stored in the Pixel Web App.
(A) Screenshot of the exploration menu available via the web interface. (B) Screenshot of the table that comprises all Pixel Sets, which match the filter criteria (see A). Particular Pixel Sets can be selected here (for instance “Pixel_C10.txt” and “Pixel_C60.txt”). They will therefore appear in the “Selection” list (see A). (C) Screenshot of the web interface that gives detailed information for the selected subset of Pixel Sets (see A). Distribution of values and quality scores are shown and individual Omics Unit are listed at the bottom of the page.
Figure 6
Figure 6. Case study in the pathogenic yeast Candida glabrata.
Our Pixel Web App was explored with the keywords “Candida glabrata” and “alkaline pH”. Two Pixel Sets were thus identified because of their tags. Two other tags were identical between the two Pixel Sets (“WT” and “logFC”), indicating that (i) C. glabrata strains are the same, i.e., Wild Type, and (ii) Pixel values are of the same type, i.e., log Fold Change. Notably Pixel Set A is based on transcriptomics experiments (RNAseq, see the main text), whereas Pixel Set B is based on proteomics experiments (mass spectrometry, see the main text). Omics Unit were next explored using the keyword “pathogenesis” resulting in the identification of 17 Pixels (respectively 6 Pixels) in transcriptomics (respectively proteomics) results. They were combined and exported from the Pixel Web App, hence starting a new data analysis cycle.
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Grants and funding

This work was funded by the Agence Nationale pour la Recherche (CANDIHUB project, grant number ANR-14-CE14-0018-02). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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