Advancing clinical genomics and precision medicine with GVViZ
Presented by Ann Laigong'
Table of�CONTENTS:
INTRODUCTION
DISCUSSION
METHODOLOGY
CONCLUSION
RESULTS
INTRODUCTION
Genetic disposition enables the detection of individuals at high risks of disease development.
Next generation sequencing data can be heavy for non-computational scientists.
GVVIS(visualizing genes with disease-causing variants) provides a user friendly platform for RNA-seq driven analysis.
WHY GVVIS?
It has the potential to find patterns across millions of features and extract actionable information.
It can assimilate patient information with a wide array of databases to easily explore and access information on gene annotation.
It has simple instructions for execution.
01
DATABASE CONECTION
DATA SELECTION
02
03
GENE SELECTION
04
QUERY DATABASE
05
HEATMAP CUSTOMIZATION AND VISUALIZATION
06
EXPORTING RESULTS
METHODOLOGY�These are the steps followed by GVVIS:
RNA-SEQ PIPELINE
The first step is utilising the RNA-seq pipeline for data processing
The output from this process is used as the input for GVVIS.
1. DATABASE CONNECTION
This establishes a connection to the SQL server using verified user credentials.
GVVIS provides SQL-features to search and select genes and their corresponding diseases for gene annotation.
It gives the user the chance to customize the analysis.
2. DATA SELECTION
This allows the user to select among gene types, expression values, and samples
The user can choose the database and expression.
The user can also select between coding or non- coding genes.
3. GENE SELECTION
The user can search and select the gene by name.
It also allows searching and selection of the specific associated diseases.
The right category should be selected by defining the abundance type, maximum and minimum values and the analytic options.
4. QUERY DATABASE
The analysis is done on gene annotation and gene expression.
The sequences are searched against a disease- gene-variants database with data from several genomic database.
The gene datasets comprise of :
59293 genes; 19989 coding,39304 non-coding more than 200,00 gene-disease combinations.
5. HEAT MAP CUSTOMIZATION AND VISUALIZATION
It provides features to customize data visualization.
These features include a title on the axis, color scheme and the selection and positioning of values.
Users can visualize the heat maps on the data visualization panel.
6. EXPORTING THE RESULTS.
GVVIS provides the option of exporting the heat maps as images.
The images are saved as TIFF and PNG.
The results can also be exported in text format as CSV files.
GVVIS was tested with randomly selected laboratory-generated RNA- seq samples.
The annotation and expression analysis linked expression genes to more than one chronic disease.
34 genes were linked to Alzheimer's disease, 32 to asthma, 43 to diabets mellitus, 2 to obesity, 9 to osteoperosis, 2 to heart failure, 20 to hypertension and 184 to multiple types of cancer.
RESULTS
DISCUSSION
GVVIS operates in a product-line architecture.
It has been programmed in JAVA and has been well tested and executed in Windows, Linux, Unix and MacOS
GVVIS bridges the gap between data analysis and precision medicine.
Information from GVVIS will support better alignment of known disease biomarkers with established treatment for personalized care.
CONCLUSION
GVVIS has shown great achievements in testing.
With a few improvements , GVVIS can become a powerful tool in the clinical setting and will go a long way in enabling physicians to make better and faster diagnosis.
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