An Academic presentation byDr. Nancy Agens, Head, Technical Operations, Pubrica Group: www.pubrica.comEmail: sales@pubrica.com

A SYSTEMATIC REVIEW OF NETWORK ANALYST- A WEB BASED BIOINFORMATICS TOOL FOR INTEGRATIVE VISUALIZATION OF EXPRESSION DATA

In-Brief IntroductionSteps Involved in PPI AnalysisKey Features of the Network Analyst Program Description and Methods ImplementationLimitations Conclusion

Outline

Today's Discussion

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 Reviewwriting Services for network analysis purposes explain you about the integrative

visualization of data expression used in health care sectors

In-Brief

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 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

Steps Involved in PPI Analysis To identify the gene or protein of interest which

includes differentially expressed genes, the gene with nucleotide polymorphism and gene-targeted bymicroRNAs

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

After c onducting a systematic review, there are three significant steps involved in PPI analysis

Key Features of the NetworkAnalyst

Supports gene or protein list and single or multiple gene expression data.

Flexible differential expression and analysis for multiple experimental designs.

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.

Contd..

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

Data processing to identify the genes

Network construction for mapping, building and refining networks

Network analysis and visualization

There are three significant steps in working of network analyst based on Systematic Review writing

Program Description and Methods

1. Data Processing Data processing involves

Data formats and uploading

Data processing and annotation

Data normalization and analysis

2. 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.

Contd..

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

3. 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

Contd..

4. Network Analysis

Network explorer- shows all networks created from seed proteins

Hub explorer – consist of detailed information of nodes within the current network

Module explorer -permits the user to decompose the current network into condensed modules

F unctional explorer – permits the user to detect the shortest path between two nodes

There are five significant panels

5. 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

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 algorithmbased on Gephi tool kit, PPI database are stored in Neo4j graph database.

The n etwork analyst takes a test with majormodern browsers with HTML support such as Google Chrome, Mozilla Firefox and Microsoft Internet Explorer

LimitationsPPI 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

ConclusionBiological network analysis is difficult to get insight into complex diseases or biological systems, network analyst easy to use web based tool assist benchresearchers 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.

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