a systematic review of network analyst – pubrica
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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: [email protected] WhatsApp : +91 9884350006 United Kingdom: +44- 74248 10299TRANSCRIPT
An Academic presentation byDr. Nancy Agens, Head, Technical Operations, Pubrica Group: www.pubrica.comEmail: [email protected]
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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