Making NGS Data Analysis Clinically Practical: Repeatable and Time-Effective Workflows

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Autumn Laughbaum, Biostatistician with introduction by Dr. Andreas Scherer, President & CEO Making NGS Data Analysis Clinically Practical: Repeatable and Time-Effective Workflows

description

Exploring next-generation sequence data requires an iterative process whereby a researcher can find a "needle in the haystack" that contributes to a particular disease or other phenotype. Once that needle has been found, a workflow can be established for analyzing other samples or to create a repeatable, time-effective process for clinical usage. Yet, repeating a workflow that involves several different quality control, filtering, and analysis steps is burdensome and error-prone. To solve this problem, we introduce custom workflow automation in SVS, which allows you to collapse dozens of steps into a few run-specific options. This click-and-go process saves an exponential amount of time while eliminating the inevitable user error that happens with tedious repetition and ensures that the exact same protocol is followed with each run, a critical requirement for use in the clinic.

Transcript of Making NGS Data Analysis Clinically Practical: Repeatable and Time-Effective Workflows

Page 1: Making NGS Data Analysis Clinically Practical: Repeatable and Time-Effective Workflows

Autumn Laughbaum, Biostatistician

with introduction by Dr. Andreas Scherer, President & CEO

Making NGS Data Analysis Clinically Practical: Repeatable and Time-Effective Workflows

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Use the Questions pane in your GoToWebinar window

Questions during the presentation

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Introduction

Clinician

Researcher

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Hands-on Time Savings

2 weeks

2 minutes

2 hours

1 trio using Excel 100 trios using SVS

Unlimited trios using automated

workflow

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Today’s Agenda

Status Quo

Example Workflow: Ogden Syndrome

Example Workflow: Trio Analysis

Discussion

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Moving from Excel > SVS > Automated Workflows

General sequencing workflow

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Analyzing Data from a secondary analysis pipeline: Using Excel

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Analyzing Data from a secondary analysis pipeline: Using SVS

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Analyzing Data from a secondary analysis pipeline: Automated Workflow

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Analyzing Sequencing Data

Filter to coding regions

Non-synonmyous

variants

® 2 variants

Filter based on population frequencies

Functional prediction

® 10,000

® 30,000

® 35,000

® 40,000

Data from secondary analysis

pipeline (VCF) – 2 million variants

Inheritance pattern

Filter on read depth & quality score

® 3,000

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WORKFLOW EXAMPLE – TRIO ANALYSIS

Workflow Example – Ogden Syndrome

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Pedigree

Ref_Alt

Ref_Alt Ref

Ref Alt AffectedMaleFemaleDeceasedSequenced

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SVS Demo

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Ogden Syndrome

Filter based on VCF quality metrics

Filter based on

population frequencies

Annotate based on functional predictions

Filter based on variant

classification

Filter based on

inheritance pattern

5 Samples107,000 variants

1 damaging variant

Ref_Alt

Alt

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WORKFLOW EXAMPLE – TRIO ANALYSIS

Workflow Example – Trio Analysis

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Trio Analysis – 3 Distinct Mutation types

• de Novo Mutations

• Rare Recessive Mutations

• Compound Heterozygous Mutations

Import variant data (VCF)

Exon Regions

Filter on VCF Quality Metrics

Damaging Variants

Variant Classification

(remove synon)

Find de Novo Variants

Filter on Variant Frequencies

Heterozyous Child

Compound Heterozygous

Genes

Rare Recessive Mutations

Additional Annotations

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de Novo Mutation

Ref_Ref

Ref_AltChild

Ref_Ref

Father Mother

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Rare Recessive Mutation

Ref_Alt

Alt_AltChild

Ref_Alt

Father Mother

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Compound Heterozygous Mutation

Ref_Ref

Re

Ref_Alt

Ref_Alt

Within a Gene

Ref_AltRef_Ref

Ref_Alt

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Trio Analysis – Automated Workflow

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Trio Analysis – Automated Workflow

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Trio Analysis – Automated Workflow

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Trio Analysis – Automated Workflow

2.3 Million

38K

36K

30K

10K

de Novo: 2 5K

3K

Compound Heterozygous:

85

Rare Recessive: 12

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Automated Workflow - Results

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Automated Workflow Results

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Additional Annotations

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WORKFLOW EXAMPLE – TRIO ANALYSIS

Example in GenomeBrowse

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WORKFLOW EXAMPLE – TRIO ANALYSIS

Discussion

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Clinicians vs. Researchers

CliniciansRunning well-defined workflow on additional samples

Minimum user-interface knowledge

Small learning curve

Limited hands-on time

ResearchersBuilding and testing workflows

More complex but intuitive interface

Larger learning curve

Power to investigate and manipulate data

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Conclusion – The GHI Approach

Work closely with clients to

learn about needs and

develop workflow

specification

Create document and workflow

diagram outlining

specifications and

requirements

Build automated-workflow prototype

Thorough internal testing and complete

documentation

Finished product works seamlessly

within SVS

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Use the Questions pane in your GoToWebinar window

Questions during the presentation