Visual Analytical Screening System for Disease Linked Gene Variants - Oyekanmi Nash

Presenter: Oyekanmi Nash, PhDNode Principal Investigator,

H3Africa Bioinformatics Network Node at National Biotechnology Development Agency (NABDA)

Abuja, Nigeria

Visual Analytical Screening System for Disease Linked Gene Variants

Visual Discovery Tools

Cardiometabolic Diseases

NIH/WT H3Africa Research Network

H3Africa/HVP: Leveraging Potentials

• NIH/WT-H3Africa • Collaborative Centers : 8• Research Projects: 9• BioBanks : 4• Bioinformatics Network : 1

Map of Africa showing the distribution of nodes in the H3ABioNet network

H3Africa: Bioinformatics Network

• H3ABioNet: a sustainable African Bioinformatics Network for H3Africa

The network provide:• computational infrastructure and hardware, • human resources, • tools and computational solutions for genomic and population-based research,

and • communications among African researchers and other interested parties.These aims are be achieved by:• providing user support, • training and capacity development, • research and tools development, and • outreach and communication.

ORGANIZATION OF THE HVP Nigeria Node

ICCAC Country Representative : Prof. Oyekanmi Nash, Alternate Representative: Hadiza Rasheed-Jada

Reports directly to the DG/CEO, NABDA/FMST

ORGANIZATION OF THE HVP NIGERIA NODE IIThe staff members of the Node include:• Alternate Representative - Hadiza Rasheed-Jada• Node Manager - Atinuke Hassan• Systems Administrator - Adekunle Farouk• Research Associates - Abimbola Kashim

- Deborah Fasesan - Taoheed Abdulkareem - Ayodele Fakoya - Adijat Ozohu Jimoh • Post-doctoral Researcher - Dr. Segun Fatumo

Institutional and Researchers Affiliation to the Node will drive the activities of the Node.

Background – Cardiometabolic Diseases• Worldwide cardiometabolic diseases are the major causes of:

• Disability; Rising Healthcare Costs and Deaths• Examples:

• Type 2 diabetes, hypertension, dyslipidemia, coronary heart disease and chronic kidney disease

• Over the next 7 years • Africa is projected to experience the largest increase in

death rates from cardiovascular disease, cancer, respiratory disease and diabetes (Aikins et al., 2010)

Noncommunicable Diseases AFR - 2015 AFR - 2030 Fold ChangeDiabetes mellitus 205,378.79 390,614.91 1.90Malignant neoplasms 521,029.65 966,876.53 1.86Other neoplasms 20,155.67 37,375.03 1.85Cardiovascular diseases 1,179,320.20 1,966,212.66 1.67Respiratory diseases 234,649.72 356,651.78 1.52

Source: Global Health Estimates (GHE) 2013: Deaths by age, sex and cause

A Strategy in Africa to Address Burden of Cardiometabolic Diseases

• Genomic and Environmental Determinants (H3Africa Projects)

• H3Africa Kidney Disease Research Network• Genomic and environmental risk factors for

cardiometabolic disease in Africans• Burden, spectrum and etiology of type 2

diabetes in sub-Saharan Africa• …..

Examples of Projected Massive and Complex Datasets from H3Africa Projects (2013….

Type 2 Diabetes Project• 12,000 Cases and 12,000 Controls • Sequencing of known T2DM regions • Genome-wide genotyping arrays• Whole exome/genome sequencing

Body Composition Project• African genome structure• Phenotyping and sampling for Cohorts • Genetic and environmental contribution to

body composition (~12,000 individuals)

These research investigations rely significantly on bioinformatics analysis and inferences from large and heterogeneous datasets

obtained from populations inside and outside Africa.

DATA SCIENCE

• Data Flow• Data Curation• Data Analysis

“The major bottleneck in genome sequencing is no longer data generation—the computational challenges around data analysis, display and integration are now rate limiting. New approaches and methods are required to meet these challenges”.

National Human Genome Research Institute Strategic Plan:Charting a course for genomic medicine from base pairs to bedside http://www.genome.gov/Pages/About/Planning/2011NHGRIStrategicPlan.pdf

Making Discoveries from the Massive and Complex Genomics Datasets and Bioinformatics Results

from H3Africa Projects

Visual Discovery Tools

Visual Discovery Tasks• Exploration • Mining • Analysis

To access and analyze data visually at the speed of thought with

minimal or no IT assistance and then share the results of their

discoveries with colleagues, usually in the form of an interactive

dashboard

Benefits• Data sharing • Collaboration• Easy to Deploy• Research in Limited or No Internet

Access

What is Visual Analytics?

http://www.slideshare.net/TableauSoftware/visual-analytics-best-practices

“Visual analytics is the representation and presentation of data that exploits our visual perception abilities in order to amplify cognition.”

- Andy Kirk, author of “Data Visualization: a successful design process”

Knowledge-Building Insights from Visual Analytics

http://www.flickr.com/photos/pnnl/6310387725/

Visual Interfaces

Examples of Visual Analytics Softwarehttp://www.vacommunity.org/Education+Resources

Toolkits

Analytic Tools

Jigsaw

Types of Visual Discovery Tools

H3ABioNet Workshop: Visual Analytics of Human Genomics Variation Datasets

July 2013Opportunities exist for African researchers to expand the use of visual

discovery tools and curated datasets to enable visual discovery (exploration, mining and analysis via interactive visual interfaces) of

bioinformatics results from high-quality genomics research.

Long-Term Goal of Project• Visual Analytical System for

• discovery of molecular consequences of variants and linked transcript expression for sets of genes or gene families

http://www.ensembl.org/info/genome/variation/predicted_data.html

Molecular Consequences of Gene Variants

Transcripts

Research Approach Obtain Datasets

Ensembl Genome Browser (www.ensembl.org) BioMart for genes and variants

Database of Alternate Transcript Expression Data Download for transcript expression values

Data Cleaning and Preparation Scripting and Spreadsheets

Construct Views and Dashboards To address scientific questions such as:

Identify molecular consequences of gene variants (Single Nucleotide Variants) in specific disease or trait.

Identify gene variants that result in multiple molecular consequences in gene transcripts.

Identify gene variant specific for transcript Compare RNA-Seq expression values for gene transcripts in

tissues.

Use Case – Gene Families AQUAPORIN – Water and glycerol transporter

13 Mammalian Aquaporins (AQP0-AQP12). Malfunction or absence linked to disease. Adipose AQP7 deficiency is associated with an increase of

intracellular glycerol content. Up-regulation of AQP1 in the glomeruli of most diseased

kidneys.

Reference: Hibuse et al. (2005). Aquaporin 7 deficiency is associated with development of obesity through activation of adipose glycerol kinase. Proc Natl Acad Sci U S A. 2005 Aug 2;102(31):10993-8. http://www.ncbi.nlm.nih.gov/pubmed/16009937

Molecular Consequences of Single Nucleotide Variants of Aquaporin Genes - Overview

Consequence to transcript

Ensembl Gene ID / Approved Symbol

ENSG00000135517

MIP

ENSG00000240583

AQP1

ENSG00000167580

AQP2

ENSG00000165272

AQP3

ENSG00000171885

AQP4

ENSG00000161798

AQP5

ENSG00000086159

AQP6

ENSG00000165269

AQP7

ENSG00000103375

AQP8

ENSG00000103569

AQP9

ENSG00000143595

AQP10

ENSG00000178301

AQP11

ENSG00000184945

AQP12A

ENSG00000185176

AQP12B

3_prime_UTR_variant5_prime_UTR_variantcoding_sequence_variantdownstream_gene_variantfeature_elongationfeature_truncationframeshift_variantinframe_deletioninframe_insertioninitiator_codon_variantintron_variantmissense_variantnc_transcript_variantNMD_transcript_variantnon_coding_exon_variantsplice_acceptor_variantsplice_donor_variantsplice_region_variantstop_gainedstop_loststop_retained_variantsynonymous_variantupstream_gene_variant 270

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Visual Analytical System for Screening Disease Linked Gene Variants Integrates data from ENSEMBL and Database of Alternate Transcript Expression (DBATE)

Data

Sou

rces Blending of Data Dimensions from multiple Data Sources

Identifies Variants linked to TranscriptsInsights: rs199936776 is unique to AQP7-004 and could affect expression of transcript or properties of protein isoform

Identification of variants that could affect transcript expression in adipose tissues

Variation Name PolyPhen predictionAssociated TranscriptName Ensembl Transcript ID

0 50 100adipose_GSE12946

0 20 40adipose_GSE30611

rs2381002 probably damaging AQP7-001 ENST00000379507rs2381003 probably damaging AQP7-001 ENST00000379507rs2381004 possibly damaging AQP7-001 ENST00000379507rs62542743 probably damaging AQP7-001 ENST00000379507rs62542744 probably damaging AQP7-004 ENST00000447660

AQP7-001 ENST00000379507rs62542746 possibly damaging AQP7-204 ENST00000541274rs74668961 probably damaging AQP7-204 ENST00000541274rs114937176 probably damaging AQP7-204 ENST00000541274rs138290992 probably damaging AQP7-004 ENST00000447660

AQP7-001 ENST00000379507rs139024279 possibly damaging AQP7-004 ENST00000447660

AQP7-001 ENST00000379507probably damaging AQP7-004 ENST00000447660

AQP7-001 ENST00000379507rs140405277 probably damaging AQP7-001 ENST00000379507rs140411243 probably damaging AQP7-204 ENST00000541274rs199936776 possibly damaging AQP7-004 ENST00000447660TMP_ESP_9_33385689 probably damaging AQP7-004 ENST00000447660

AQP7-001 ENST00000379507

SummaryIn Africa, researchers will be able to use visual discovery tools to make

DISCOVERIES from large-scale molecular and clinical datasets to support decision-making on genetic and environmental determinants of cardiometabolic

diseases.

Visual Analytics can facilitate collaboration between Data Experts and Subject Matter Experts

Acknowledgments• H3Africa Bioinformatics Network (H3ABioNet)

– National Human Genome Research Institute – NIH Common Fund– Grant U41HG006941

• National Institutes of Health• Dr. Raphael Isokpehi, Bethune-Cookman University,

Florida, USA• National Biotechnology Development Agency, Federal

Ministry of Science and Technology, Nigeria• Visual Analytics in Biology Curriculum Network