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PanoromiX: a time-course network medicine platform integrating molecular assays and pathophenotypic data

Contributed equally
BMC Bioinformatics201819:458

https://doi.org/10.1186/s12859-018-2494-6

  • Received: 12 April 2018
  • Accepted: 13 November 2018
  • Published:

Abstract

Background

Network medicine aims to map molecular perturbations of any given diseases onto complex networks with functional interdependencies that underlie a pathological phenotype. Furthermore, investigating the time dimension of disease progression from a network perspective is key to gaining key insights to the disease process and to identify diagnostic or therapeutic targets. Existing platforms are ineffective to modularize the large complex systems into subgroups and consolidate heterogeneous data to web-based interactive animation.

Results

We have developed PanoromiX platform, a data-agnostic dynamic interactive visualization web application, enables the visualization of outputs from genome based molecular assays onto modular and interactive networks that are correlated with any pathophenotypic data (MRI, Xray, behavioral, etc.) over a time course all in one pane. As a result, PanoromiX reveals the complex organizing principles that orchestrate a disease-pathology from a gene regulatory network (nodes, edges, hubs, etc.) perspective instead of snap shots of assays. Without extensive programming experience, users can design, share, and interpret their dynamic networks through the PanoromiX platform with rich built-in functionalities.

Conclusions

This emergent tool of network medicine is the first to visualize the interconnectedness of tailored genome assays to pathological networks and phenotypes for cells or organisms in a data-agnostic manner. As an advanced network medicine tool, PanoromiX allows monitoring of panel of biomarker perturbations over the progression of diseases, disease classification based on changing network modules that corresponds to specific patho-phenotype as opposed to clinical symptoms, systematic exploration of complex molecular interactions and distinct disease states via regulatory network changes, and the discovery of novel diagnostic and therapeutic targets.

Background

Many diagnostic and therapeutic target discovery research studies are rapidly adopting a temporal systems biology design that combines multi-population, multi-tissue, and multi-omics. Many believe a disease is a disturbance enacted on the “driver” genes that lead to a cascade of changes of other genes: initially to their first-degree interaction neighbors, followed by downstream effects [1]. Ideally, the network can be seen as several molecular modules that can be activated sequentially from one to the other. For example, Hwang et al. summarized a prion progression genetic network using seven mouse strains [2]. Huang et al. used gene expression profiling to show that trajectories of neutrophil differentiation converge to common networks eventually although the perturbations originated from different networks [3].

Visually tracing temporal changes emerging from biological processes has a high value in light of the molecular ontology and clinical observations. However, most current network platforms (e.g. Cytoscape [4], Gephi [5], IPA [6]), tools (e.g.VisANT [7], PATIKA [8], NAViGaTOR [9], GeneVis [10], yfiles [11], VANTED [12], Cancer PanorOmics [13]), custom R codes [14] generate static snapshots, which are inadequate at consolidating large-scale heterogeneous data. Even for advanced users, making a dynamic modular network is very labor intensive, requiring knowledge of several toolboxes and custom coding.

Multi-omics time series network analysis has three major challenges:

Modularity

Components with similar functions need to be tightened, labelled, and moved together.

Interactivity

Multiple layers of heterogeneous information need to be visually distinguished. Animation, search, zoom, and basic functions that modify the characteristics of components on site must be integrated.

Sharability

The network model needs to be easily shared, restored, and compared, without installing software.

Implementation

PanoromiX is hosted online at https://bioinfo-abcc.ncifcrf.gov/panoromics/. It is written in PhP5 and open-source Javascript libraries D3.js, jquery.js, and dat.gui.js. Since the PanoromiX software application is completely web-based, there are no installation requirements and no restrictions on which operating systems can be used. The software can be launched on any computer system that is connected to the internet and capable of running one of the current web browser applications with JavaScript capabilities enabled (Internet Explorer, Google Chrome, Mozilla Firefox, Safari).

Results

The PanoromiX application accepts data in tab-delimited text files with the file extension .txt. PanoromiX requires a “node” file containing information about individual points (nodes) of interest that will be plotted as part of the network visualization. A “link” file containing information about links or relationships between each of these points (nodes) is optional.

PanoromiX uses standard node-edge design to denote molecules and their interactions, respectively. The node shape and fill color can represent molecule types and expression levels. For example, one can display a longitudinal study of cancer patients where a node reprents a patient, illness stages as different colors, and therapies as shape . PanoromiX requires an input text file to initially populate the node of a biological network. This node file defines the nodes and requires an ID and Color for each designated node. Additional biological group information and optional graphical descriptions can be provided. An optional edge file defines network edges through a list of connected Source and Target IDs. The detailed description of the characteristics of node and edge file is listed in Tables 1 and 2. After submitting simple text files of network attributes, users can easily design and display an interactive network online using the toolbar.
Table 1

PanoromiX Node file

Data Field

Data Type

Required

Description

id

General text

Required

This field denotes the unique id of this node or point. It is used by the application to reference each node individually and perform operations such as linking and defining colors, shape and so on. This field must be unique.

name

General text

Required

This field is the label that is displayed under each node on the visualization and although similar to the id in our example data, this label can be whatever the user chooses, and in addition does not need to be a unique value.

group

General text

Required

Since the PanoromiX application creates modules or groups from the data it processes, this field is necessary to indicate which group, or module, a particular node is a member of. This field does not need to be unique as many nodes may belong to the same group.

type

General text

Required

This field is used to denote a characteristic about each node, aside from its group. For example, one can define both ‘Male’ and ‘Female’ nodes.

description

General Text

Optional

This field allows the user to enter a short paragraph of text (256 characters or less) containing a description, or additional information related to each node. We will discuss how this is displayed when we illustrate how to interact with the visualization.

size

Numerical

Optional

Depending on the nature of your data, it may be beneficial to render some nodes larger on the screen, and some smaller. This numerical value (1–5) allows you to do just that. It controls the dimensions or size of each node on screen.

color

Numerical

Optional

This field allows the user to enter a numerical value (1–5) to display a time-point profile associated with the node.

shape

Numerical

Optional

This field allows the user to specify a general shape for each of the nodes by using a numerical value (0–5) in this column. 0 – circle, 1 – square, 2 – diamond, 3 – cross, 4 - triangle (downward pointing), 5 – triangle (upward pointing)

Table 2

PanoromiX Edge file

Data Field

Data Type

Required

Description

sourceId

General text

Required

This field must contain the id value of the node (from your corresponding nodes file) where the link will be drawn from. If you enter an invalid node id here the link will be ignored.

targetId

General text

Required

This field must contain the id value of the node (from your corresponding nodes file) where the link will be drawn to. If you enter an invalid node id here the link will be ignored.

link_scale

Numerical

Optional

The user can enter the width of a link

marker_start

Numerical

Optional

The user can indicate the style of the marker that is drawn at the start of the link using a value (0–3).

0 – circle, 1 – square, 2 – arrow, 3 - stub

marker_end

Numerical

Optional

The user can indicate the style of the marker that is drawn at the end of the link using a value (0–3).

0 – circle, 1 – square, 2 – arrow, 3 - stub

linkName

General text

Optional

The user can enter the name of a link

linkColor

Numerical

Optional

The user can enter the color of a link

Modularity

PanoromiX has a special meta-node design that allows user to define modules (“group” function in Table 1). All the components belonging to a module connect to its meta-node (Fig. 1). One can move the entire module by its meta-node. In addition, the users can also upload icons that will represent meta-nodes. The icons need to be .jpeg files and image dimension or resolution is not restricted as long as they are not greater than 5 MB in size each.
Fig. 1
Fig. 1

An illustration of prion accumulation network. (Full animation is available on the PanoromiX website)

Interactivity

PanoromiX has a full interactive toolbar with search, zoom, animation play/stop, theme change, save, download, share functions, and a time point labelling tool. On the right hand side, there is a control panel to adjust animation speed, link width, font color, as well as other useful functions including sensitivity filtering. The sensitivity filtering feature allows the users to see only the node with desired changes between two adjacent time points.

PanoromiX also has a unique dynamic legend panel to associate the network model with external resources, such clinical variables, images, behavioral observations. (Additional files 1, 2 and 3) The node shape outline color is defined as a ‘type’ (Table 1), which is also displayed as a link on the legend panel. Clicking the link highlights all nodes of the same type. The users can employ this attribute to refer to any external knowledge, such as tissue-specific, disease/drug-related, ethnicity/gender-specific information, and even correlations to clinical variables. Furthermore, users can upload an associated image, such as a histology or MRI image, and insert a description for each time point. Figure 1 shows such an example, where the user has uploaded brain images and behavior descriptions corresponding to prion disease severity in mice, while the six types of nodes underneath the image panel represent the six mouse-prion strains used in the study.

Sharability and scalability

In an avenue comparable to how Jupyter notebooks [15] and R markdown [16] enable the sharability and reproducibility of modelling, PanoromiX also allows users to build and deposit their network models as templates to a permanent web space where other researchers can freely download and reproduce their results. The cloud-sourcing of network models significantly enhances the opportunity for collaboration between researchers, and will drastically reduce the manual labor required for designing visual representations. The users can designate the model to be read-only or writable. In read-only mode, the user can still modify the network and input their own contributions, but not overwrite the original work.

PanoromiX also supports imported network files from Cytoscape [4], IPA [6], and custom files. Once the files are uploaded, a “Data Label Assignment” page will pop up (Fig. 2) for the user to indicate which PanoromiX data fields will correspond to the columns in the Cytoscape/IPA data files. The visualization module of PanoromiX is written by Javascript. However, the computational module in the backend server can call Python and R scripts, whenever more advanced computational techniques are needed. The output will be stored in a text file that the Java script can read and parse. By hosting these computations on a powerful remote server, PanoromiX alleviates the problem of visualizing massive networks on small workspaces, and helps make large-scale network analysis available to scientists without extensive high-performance computing capabilities. In comparion to the popular tools like Cytoscape [4], Gephi [5], and IPA [6], PanoromiX has unique and robust features. Particularly valuable is PanoromiX’ interactive animation, which is very helpful in exploring and presenting time-series trends (Table 3, Additional file 4).
Fig. 2
Fig. 2

Data Label Assignment to import the network files from Cytoscape and IPA

Table 3

Comparison of PanoromiX to other major network packages

Name

web-based design, display, and share

Time-series interactive animation

Support Metanode/Module

Temporal profile analysis

Reproducibility

Learning curve

Open Source

Panoromix

web-based, full interactivity

Playing mode with highlight, annotation, search, filtering

Yes

Sentivity filtering; display progfiles

web-based model depository

No software intallation, no programming needed

Free

Cytoscape

desktop version and Cytoscape.js

Non-interactive videos using DynNetwork

Yes

Sensitivity filtering; display profiles

share session files

Need to install; need to learn plugins; extensive programming required for Cytoscape.js

Free

Gephi

desktop

Non-interactive videos

Metanode pugin

No

share session files

Need to install; need to learn plugins

Free

IPA

Web-based

No

No

No

share session files

Need toinstall; no programming required

Commercial

R packages

Shiny server

Non-interactive videos using animation + igraph

No

Statistical Packages

R Notebook/R Markdown

Need to install R and many packages; Extensive programming needed

Shiny server (have commercial version)

VisANT

Web-based

No

Yes

No

web sharing

Need to install, no programming needed

Free

yED

desktop and yED live

No

No

No

share session files

No software installation, no programming needed

Free

VANTED

desktop

No

No

display profiles

share excel files

Need to install, no programming needed

Free

NAViGaTOR

desktop

No

No

display profiles

share text files

currently unsupported, no programming needed

Free

PATIKA

web-based

No

Yes

display profiles

share text file

currently unsupported, no programming needed

Free

Cancer PanorOmics

web-based

No

No

No

share text file

No software installation, no programming needed

Free

GENeVis

desktop

No

No

display profiles

share text file

Need to install, no programming needed

Free

Limitation and future work

Displaying huge-interconnected networks on the computer or smart device with limited memory is a universal challenge. The computational complexity of force-directed graph in D3.js is O(nlog n), thus rending 5000 nodes and edges will expect a slowdown. For very large networks, offline static network tools like Gephi [5] will be more practical. In the future, we will implete more analytic components, including network reconstruction algotihms to automatically create network modules.

Availability and requirements

PanoromiX is publicly available at https://bioinfo-abcc.ncifcrf.gov/panoromics/

Operating systems: Windows/OSX.

Programming language: PHP and Javascript.

Browsers: IE 9, Firefox 31, Chrome 31, Safari 5.1, Opera 24, Opera Mini 8, iOS safari 7.1, Android Browser 4.4, or later.

Conclusions

PanoromiX is a comprehensive platform to enhance network model reproducibility, time-series interpretability, and data associability. This emergent tool of network medicine visualizes the interconnectedness of tailored genome assays to pathological networks and phenotypes for cells or organisms in a data-agnostic manner. As an advanced network medicine tool, PanoromiX allows monitoring of panel of biomarker perturbations over the progression of diseases, disease classification based on changing network modules that corresponds to specific patho-phenotype as opposed to clinical symptoms, systematic exploration of complex molecular interactions and distinct disease states via regulatory network changes, and the discovery of novel diagnostic and therapeutic targets.

Notes

Abbreviations

IPA: 

Ingenuity Pathway Analysis

MRI: 

Magnetic Resonance Imaging

Declarations

Acknowledgements

We appreciate Major Derese Getnet assistance in revising and editing manuscript.

Funding

This project has been funded in part or whole with federal funds from the Office of the Assistant Secretary of Defense for Health Affairs, the US Army Medical research and Materiel Command NO: 09284002., and the National Cancer Institute, National Institutes of Health, under contract HHSN261200800001E and IAA number XCO15002–001-02001. The funding body did not play any roles in the design of the study and collection, analysis, nor interpretation of data nor in writing the manuscript.

Availability of data and materials

The source code is available at https://github.com/panoromix/PanoromiX.

Disclaimers

The views, opinions, and/or findings contained in this report are those of the authors and should not be construed as official Department of the Army position, policy, or decision, unless so designated by other official documentation. Citations of commercial organizations or trade names in this report do not constitute an official Department of the Army endorsement or approval of the products or services of these organizations.

The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services and Department of the Army, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.

Authors’ contributions

RY, DW, JW, RK, RC, UM, RH, and MJ conceived and designed the research. RY, DW, and JW developed the platform, RY, DW, RC wrote the paper. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Competing interests

The authors declare no competing financial interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research sponsored by the National Cancer Institute, Frederick, MD 21702-5010, USA
(2)
Integrative Systems Biology Program, US Army Center for Environmental Health Research, 568 Doughten Drive, Fort Detrick, MD 21702-5010, USA

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Copyright

© The Author(s). 2018

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