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. Continue Reading: https://bit.ly/3nAa3ek Reference: https://pubrica.com/services/research-services/systematic-review/ Why Pubrica? When you order our services, Plagiarism free|on Time|outstanding customer support|Unlimited Revisions support|High-quality Subject Matter Experts. Contact us : Web: https://pubrica.com/ Blog: https://pubrica.com/academy/ Email: sales@pubrica.com WhatsApp : +91 9884350006 United Kingdom: +44- 74248 10299

1. Copyright © 2020 pubrica. All rights reserved 1
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

2. Copyright © 2020 pubrica. All rights reserved 2
 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.