a systematic review of network analyst – pubrica

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A Systematic Review of Network Analyst- A Web Based Bioinformatics Tool

for Integrative Visualization of Expression Data

Dr. Nancy Agens, Head,

Technical Operations, Pubrica

sales@pubrica.com

In-Brief

In a Systematic Review Writing, the

network analyst is a bioinformatics tool

designed to perform efficient PPI network

analysis for data generated from gene

expression experiments the following

contents explain about the network analyst

and their methods, in brief, using the help

of pubrica blog. Systematic Review writing

Services for network analysis purposes

explain you about the integrative

visualization of data expression used in

health care sectors.

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I. INTRODUCTION

Network analyst is a web based visual

analytics tool for comprehensive profiling,

Meta analysis and system-level

interpretation of gene expression data which

is based on PPI (protein-protein interaction)

network analysis and visualization. The first

version of Network analyst was launched in

2014; there are various updates attached

afterwards based on the community

feedback and technology progress. In the

latest version users able to perform gene

expression for 17 different species and other

benefits such as creating cell or tissue-

specific PPI networks, gene regulatory

networks, gene co-expression networks

using systematic review services

After conducting a systematic review, there

are three significant steps involved in PPI

analysis

To identify the gene or protein of

interest which includes differentially

expressed genes, mutated genes, genes

with copy number variations, the gene

with nucleotide polymorphism and gene-

targeted by microRNAs

The input data is to search and find

binary information from a systemized

PPI database

There are two complementary

approaches performed in the third step,

Topology analysis and Module analysis

Network analyst combines all three steps

and provides result via a robust online

network visualization framework, the key

features of the network analyst from a

systematic review paper are

Supports gene or protein list and single

or multiple gene expression data

Flexible differential expression and

analysis for multiple experimental

designs

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Multiple options provide the control of

network size

Interactive network visualization with

other features such as facile searching,

zooming and highlighting by writing a

systematic review

Supports topology, module and shortest-

path analysis

Functional enrichment analysis on

current selection includes GO, KEGG,

Reactome

Customize options with layout, edge

shapes and node size, colour, visibility

Network features including node

deletion and module extraction

The output downloads the network files

(edge list, graphML), Images (PNG,

PDF) and Topology or Functional

analysis result

The current version allows analysis and

rapid visualization of resulting PPI networks

from small to large size (100-1000s nodes)

II. PROGRAM DESCRIPTION AND

METHODS

There are three significant steps in working

of network analyst based on Systematic

Review writing

Data processing to identify the genes

Network construction for mapping,

building and refining networks

Network analysis and visualization

Data Processing

Data processing involves

Data formats and uploading

Data processing and annotation

Data normalization and analysis

Network Construction

Network analyst will give a detailed, high-

quality PPI database obtained from

InnateDB in the International Molecular

Exchange (IME) Consortium. The

experimental PPI database is from IntAct,

MINT, DIP, BING, and BioGRID. The

database consists of 14,775 proteins, 1,

45,995 experimentally confirmed interaction

for humans and 5657 proteins, 14,491

interactions for mouse

For every individual protein, a search

algorithm is created, which is capable of

direct interaction with seed protein. The

results utilize to build the default networks.

The users advise controlling the number of

nodes within 200 to 2000 for practical

reasons because larger systems lead to

Hairball effect

Hairball Effect

When the network becomes large and

complex, it suffers from the hairball effect,

which significantly affects the practical

utilities and uptake. Two steps follow to

resolve this issue

Trimming the default network to retain

only those significant nodes or edges

Developing better visualization methods

to reduce edge and node occlusion

Network Analysis

There are five significant panels

Network explorer- shows all

networks created from seed proteins

Hub explorer – consist of detailed

information of nodes within the

current network

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Module explorer -permits the user to

decompose the current network into

condensed modules

Functional explorer – permits the

user to detect the shortest path

between two nodes

Network Visualization

There are certain events recommended to

follow for visualization and these events are

carried using the mouse, there are various

user-friendly options are available such as

Node display option

Network option

Node deletion and module extraction

III. IMPLEMENTATION

The construction of Network analyst

interface using java server faces 2.0

technology relies based on visualization is

sigma. Js Java script library, backend

statistical computation was implemented

using R program language, construction of

the layout algorithm based on Gephi tool kit,

PPI database are stored in Neo4j graph

database. The network analyst takes a test

with major modern browsers with HTML

support such as Google Chrome, Mozilla

Firefox and Microsoft Internet Explorer

IV. LIMITATIONS

PPI database may contain false positives

Unable to determine new interactions

which are condition-specific

The plans include

Increase its support for more organisms

More updates in the Visualization field

V. CONCLUSION

Biological network analysis is difficult to

get insight into complex diseases or

biological systems, network analyst easy to

use web based tool assist bench researchers

and clinicians to perform various tasks and

highly user friendly. Pubrica helps you to

know about the workflow of network analyst

in a detailed manner with writing a

systematic literature review for future

purposes.

REFERENCES

1. Guan, Y., Xu, F., Wang, Y., Tian, J., Wen, Z.,

Wang, Z., & Chong, T. (2020). Identification of

critical genes and functions of circulating tumour

cells in multiple cancers through bioinformatic

analysis. BMC medical genomics, 13(1), 1-11.

2. Xia, J., Gill, E. E., & Hancock, R. E. (2015).

NetworkAnalyst for statistical, visual and network-

based meta-analysis of gene expression data. Nature

protocols, 10(6), 823-844.

3. Zhou, G., Soufan, O., Ewald, J., Hancock, R. E.,

Basu, N., & Xia, J. (2019). NetworkAnalyst 3.0: a

visual analytics platform for comprehensive gene

expression profiling and meta-analysis. Nucleic

acids research, 47(W1), W234-W241.