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Ahmed lab is organizing GVViZ and PAS workshop at the 14th annual RECOMB/ISCB, USA. 7-11 November, 2022

Nov 07, 2022

Ahmed lab is organizing GVViZ and PAS workshop at the 14th annual RECOMB/ISCB Conference on Regulatory & Systems Genomics with DREAM Challenges. 7-11 November, 2022.

Tutorial VT3:
GVViZ & PAS: Integrated bioinformatics and mobile applications for gene-disease data annotation, expression analysis, and visualization for translational research. Monday, November 8, 10:00 am – 12:00 pm PST.

Summary:
Over the last few decades, genomics has led in changing our views on conducting biomedical research, studying diseases, and understanding diversity in our society across the human species. Investigating disease-causing genes can support finding the root causes of uncertainties in patient care. Although approaches that combine clinical and genomic information are becoming increasingly common, scientists and health care providers still are faced with the daunting challenge of identifying what genes may be relevant to the part of the body or biological system they are studying, and how variants may impact health in unique ways for each patient. Independent, and timely high-throughput next generation sequencing data analysis is still a challenge for non-computational biologists and geneticists. We are focused on helping researchers, medical practitioners, and pharmacists in having a broad view of genetic makeup that may be implicated in the likelihood of developing certain diseases. We emphasize that automated, user-friendly, and interactive genomics data analysis, visualization, and sharing should be an indispensable component of modern era, as it has potential to bridge the gap between algorithmic approaches and the cognitive skills of users and investigators.

In this tutorial, we present two bioinformatics applications (GVViZ and PAS), which are designed, developed, and freely available to support translational research and precision medicine. GVViZ is a user-friendly, cross-platform, and database application for RNA-seq-driven variable and complex gene-disease data annotation, and expression analysis with dynamic heat map visualization. It can assimilate patients’ transcriptomics data with public, proprietary, and our in-house developed gene-disease databases to query, easily explore, and access information on gene annotation and classified disease phenotypes with greater visibility and customization. Our gene-disease databases are globally accessible through PROMIS-APP-SUITE (PAS), and consists of authentic and actionable genes, SNPs, and classified diseases and drugs data collected from different clinical and genomics databases worldwide. PAS is a smart phone application, integrating ICD (9 and 10) and NDC codes, and sets of over 50,000 genes, over million SNPs, over 200,000 gene-disease combinations. PAS is designed to simplify navigation across the landscape of gene annotation resources by an efficient mobile record search engine, which is based on standardized genes and related diseases to help explore multi-purpose clinical and genomics concepts in meaningful ways. The performance of GVViZ and PAS have been tested and validated in-house with multiple experimental analyses.

The execution of GVViZ and PAS is based on a set of simple instructions that users without a computational background can follow to design and perform customized data acquisition and analysis. It utilizes processed RNA-seq data, and robustly visualize patterns and problems that may give insight into a patient’s genomic profile, unravel genetic predisposition, and uncover genetic basis of multiple disorders. Experts in clinics and researchers in life sciences can use GVViZ to visualize and interpret the transcriptomics data, making it a powerful tool to study the dynamics of gene expression and regulation. With the successful deployment in clinical settings, GVViZ and PAS has the potential to enable high-throughput correlations between patient diagnoses based on clinical and genomics data.

Learning Objectives:

Genomics and transcriptomics, and of role RNA-seq driven data processing, gene expression analysis and interpretation.
Understanding the importance of data visualization, and mapping and rendering large RNA-seq datasets.
Learning using GVViZ platform for the automated gene-disease data annotation, expression analysis, and dynamic heat map visualization.
Learning using PROMIS-APP-SUITE (PAS) to access and explore information about authentic genes (protein-coding and noncoding), mutations, gene-disease relationships, and classified diseases and drugs and their codes.
Training to how to download, configure, use, and customize GVViZ and PAS applications, and model genomics databases.

Intended audience and level:
This tutorial will be aimed to the audience of any level (e.g., beginner, experience, advanced knowledge), mainly interested in learning about automated and integrated gene-disease data annotation, expression analysis, and visualization for translational research.

We will expect and equally appreciate presence of computational and non-computational scientists, bench scientists, bioinformaticians, biologist, geneticists, clinicians, and most importantly graduate and undergraduate students of life and medical sciences.

Maximum Participants: 50

Reference Articles:

– Ahmed, Z., Renart, E. G., Zeeshan, S., & Dong, X. (2021). Advancing clinical genomics and precision medicine with GVViZ: FAIR bioinformatics platform for variable gene-disease annotation, visualization, and expression analysis. Human Genomics. 15(1), 37. PMID: 34174938. (BMC, Springer Nature, Human Genome Organization)

– Ahmed, Z., Zeeshan, S., Mendhe, D., & Dong, X. (2020). Human gene and disease associations for clinical-genomics and precision medicine research. Clinical and Translational Medicine. 10(1), 297–318. PMID: 32508008. (Wiley)

– Zeeshan, S., Xiong, R., Liang, B. T., & Ahmed, Z. (2020). 100 Years of evolving gene-disease complexities and scientific debutants. Briefings in Bioinformatics. 21(3), 885–905. PMID: 30972412. (Oxford)

– Ahmed, Z., Zeeshan, S., Xiong, R., & Liang, B. T. (2019). Debutant iOS app and gene-disease complexities in clinical genomics and precision medicine. Clinical and Translational medicine. 8(1), 26. PMID: 31586224. (Wiley)