VarBase: A Platform for the Storage and Clinical Interpretation of Next Generation Sequencing Data

S L I D E 0

VarBase: A Platform for the Storage and Clinical Interpretation of Next Generation Sequencing Data

Wade L. Schulz, MD, PhD, John G. Howe, PhD, Karl Hager, PhD, Henry M. Rinder, MD

Yale University, Department of Laboratory Medicine

S L I D E 1

The Problem

Next generation sequencing panel brought online for leukemia and myelodysplastic syndrome, needed interpretive and data management software

Interpretation efficiency

Patient safety

Turnaround time

S L I D E 2

Project Goals

Integrate annotation information from disparate data silos

Provide interpretation interface

Generate digital and printed reports

Provide robust research tools for ongoing clinical and laboratory studies

S L I D E 3

The VarBase Platform

Research Applications

Web Interface

VarBase - Kepler

Internal Clients

Galileo Services

IonReporter

Cloud (Kepler)

Local (Galileo)

S L I D E 4

Relations vs. Documents: SQL vs NoSQL

SQL/Relational

ACID Compliant

Column-level encryption

Well-known deployment

NoSQL/Document

Dynamic schema

Improved read/write speeds

Easy scaling and redundancy

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Tool Selection: Public Data Repository (Kepler)

Elasticsearch

Web service wrapper

Administrative import interface COSMIC

ClinVar

OMIM

dbSNP

Index updated every 3 months


Web Interface

VarBase - Kepler

Internal Clients

Galileo Services

IonReporter

S L I D E 6

Tool Selection: Private Data Warehouse (Galileo)

MSSQL + Elasticsearch

Patient data encrypted in SQL

Panel information stored in SQL

Non-demographic information in SQL+Elasticsearch

Variants in Elasticsearch+Disk

Document database caveat: Most are not ACID compliant, should not be used as a primary data store

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Tool Selection: Authentication/Authorization

Institutional Active Directory

Role-based authorization

Web application restrictions Laboratory personnel

Trainee (Resident/Fellow)

Attending

Web interface restrictions Patient-based restriction

Data-type restriction


Web Interface

VarBase - Kepler

Internal Clients

Galileo Services

IonReporter

S L I D E 8

Variants in JSON

{ "chromosome": "chr7", "position": 148506396, "type": "snv", "refAllele": "A", "altAllele": "C", "totalReads": 1998, "forwardReads": 1038, "forwardRefReads": 524, "forwardAltReads": 514, "reverseReads": 960, "reverseRefReads": 500, "reverseAltReads": 460, "refReads": 1024, "altReads": 974, "vaf": 48.749, "variantRegion": "intronic", "variantEffect": "", "snvEffect": "A>C", "gene": "EZH2"

}

- Variant location in genome

- Nucleotide change

- Sequencing statistics

- Variant allele frequency

- Variant coding/protein effects

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Research Integration: Data Visualization (Kibana)

S L I D E 10

System Statistics

Cloud-Based Elasticsearch Cluster

60 million variant annotations

10 million oncology annotations

Local Elasticsearch+MSSQL Instance

>80 specimens + validation specimens

Turnaround time: 1-2 weeks

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Conclusions

Hybrid data store can efficiently and securely store complex data types

Cloud-based variant annotation can be integrated into multiple services and provides auditable interpretation information

Technology agnostic web service interfaces provide easily accessible data interchange

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Acknowledgements

Henry Rinder, MD

Alexa Siddon, MD

Richard Torres, MD

Christopher Tormey, MD

Thomas Durant, MD

Molecular Pathology Laboratory

John G. Howe, PhD

Karl Hager, PhD

Laboratory Informatics

Rodion Rathbone, MD

Nathan Price