NGS 101 Panel Design and Quality. Adam Hauge Development Manager University of Minnesota Genomics Center

Similar documents
SureSelect XT HS. Target Enrichment

SURESELECTXT LOW INPUT TARGET ENRICHMENT

SureSelect Target Enrichment for the Ion Proton TM Next Generation Sequencing System

DNA concentration and purity were initially measured by NanoDrop 2000 and verified on Qubit 2.0 Fluorometer.

Next Generation Sequencing. Target Enrichment

Whole Genome, Exome, or Custom Targeted Sequencing: How do I choose? Aaron Thorner, PhD Clinical Genomics Group Leader

New Frontiers of Genetic Profiling Achieve Higher Sensitivity and Greater Insights with Molecular Barcodes, Long Read Capture and Optimized Exomes

Surely Better Target Enrichment from Sample to Sequencer and Analysis

Surely Better Target Enrichment from Sample to Sequencer

Incorporating Molecular ID Technology. Accel-NGS 2S MID Indexing Kits

Performance Characteristics drmid Dx for Illumina NGS systems

NGS Sample QC with the Agilent 2200 TapeStation. Rainer Nitsche Application Engineer Agilent Technologies, Inc.

Assay Validation Services

HyperCap, an automatable workflow on the Agilent Bravo B

G E N OM I C S S E RV I C ES

Outline General NGS background and terms 11/14/2016 CONFLICT OF INTEREST. HLA region targeted enrichment. NGS library preparation methodologies

Get to Know Your DNA. Every Single Fragment.

Agilent NGS Solutions : Addressing Today s Challenges

The New Genome Analyzer IIx Delivering more data, faster, and easier than ever before. Jeremy Preston, PhD Marketing Manager, Sequencing

Matthew Tinning Australian Genome Research Facility. July 2012

SEQUENCING FROM SAMPLE TO SEQUENCE READY

Lab methods: Exome / Genome. Ewart de Bruijn

Implementation of Automated Sample Quality Control in Whole Exome Sequencing

HaloPlex HS. Get to Know Your DNA. Every Single Fragment. Kevin Poon, Ph.D.

High Throughput Sequencing the Multi-Tool of Life Sciences. Lutz Froenicke DNA Technologies and Expression Analysis Cores UCD Genome Center

Single Cell Genomics

Genome Resequencing. Rearrangements. SNPs, Indels CNVs. De novo genome Sequencing. Metagenomics. Exome Sequencing. RNA-seq Gene Expression

Cancer Genetics Solutions

Next-generation sequencing technologies

SEQUENCING. M Ataei, PhD. Feb 2016

ACCEL-NGS 2S DNA LIBRARY KITS

Quality assurance in NGS (diagnostics)

Next Generation Sequencing in Genetic Diagnostics Alan Pittman, PhD

Powering the Synthetic Biology and Genomics Revolutions

Target Enrichment Strategies for Next Generation Sequencing

The Agilent Technologies SureSelect Platform for Target Enrichment

Next-Generation Sequencing. Technologies

A Genomics (R)evolution: Harnessing the Power of Single Cells

Course Overview: Mutation Detection Using Massively Parallel Sequencing

NGS-based innovations within the Leiden Network

SANGER SEQUENCING WHITE PAPER

Single Cell Genomics

SMARTer Ultra Low RNA Kit for Illumina Sequencing Two powerful technologies combine to enable sequencing with ultra-low levels of RNA

Experimental Design. Dr. Matthew L. Settles. Genome Center University of California, Davis

Illumina s Suite of Targeted Resequencing Solutions

Biomek Automated Genomic Sample Prep Accelerates Research

Deep Sequencing technologies

To determine MRK-003 IC50 values, cell lines were plated in triplicate in 96-well plates at 3 x

TECH NOTE Stranded NGS libraries from FFPE samples

NEBNext Direct Custom Ready Panels

Introducing combined CGH and SNP arrays for cancer characterisation and a unique next-generation sequencing service. Dr. Ruth Burton Product Manager

Targeted Sequencing Using Droplet-Based Microfluidics. Keith Brown Director, Sales

Genomic & RNA Profiling Core Facility

Impact of gdna Integrity on the Outcome of DNA Methylation Studies

SureSelect Clinical Research Exome V2. Optimized for Rare Diseases

Integrated NGS Sample Preparation Solutions for Limiting Amounts of RNA and DNA. March 2, Steven R. Kain, Ph.D. ABRF 2013

SO YOU WANT TO DO A: RNA-SEQ EXPERIMENT MATT SETTLES, PHD UNIVERSITY OF CALIFORNIA, DAVIS

with drmid Dx for Illumina NGS systems

KAPA hgdna QUANTIFICATION AND QC KIT:

Implementation & development of NGS in the diagnostic lab

Custom Panels via Clinical Exomes

Illumina TruSeq RNA Access Library Prep Kit Automated on the Biomek FX P Dual-Hybrid Liquid Handler

Targeted Sequencing in the NBS Laboratory

DNA. bioinformatics. genomics. personalized. variation NGS. trio. custom. assembly gene. tumor-normal. de novo. structural variation indel.

Automated target enrichment using SeqCapEZ DNA kits on ACSIA NGS Capture Edition

Access Array BRCA1 / BRCA2 / TP53 Target-Specific Panel Build the highest quality amplicon libraries with qualified assays

SureSelect Clinical Research Exome V2 Definitive Answers Where it Matters Most

Magnis NGS Prep System. Your lab s automated library preparation solution for next-generation sequencing

Welcome to the NGS webinar series

Bioinformatics Advice on Experimental Design

454 Sample Prep / Workflow at the BioMedical Genomics Center (BMGC) University of Minnesota. Sushmita Singh

Wet-lab Considerations for Illumina data analysis

Supplementary Information

Introduction to Microbial Sequencing

High Throughput Sequencing the Multi-Tool of Life Sciences. Lutz Froenicke DNA Technologies and Expression Analysis Cores UCD Genome Center

Detection of Rare Variants in Degraded FFPE Samples Using the HaloPlex Target Enrichment System

The Agilent Technologies

User Requirement Specifications

Whole Human Genome Sequencing Report This is a technical summary report for PG DNA

March 20-23, 2010 Sacramento, CA

Development of quantitative targeted RNA-seq methodology for use in differential gene expression

Next-generation sequencing technologies

VALIDATION OF HLA TYPING BY NGS

RIPTIDE HIGH THROUGHPUT RAPID LIBRARY PREP (HT-RLP)

Eliminating Bottlenecks with the Agilent Encore Multispan Liquid Handler

How much sequencing do I need? Emily Crisovan Genomics Core

Services Presentation Genomics Experts

DNA METHYLATION RESEARCH TOOLS

Complete protocol in 110 minutes Enzymatic fragmentation without sonication One-step fragmentation/tagging to save time

Application Note. Genomics. Abstract. Authors. Kirill Gromadski Ruediger Salowsky Susanne Glueck Agilent Technologies Waldbronn, Germany

Human Genome Sequencing Over the Decades The capacity to sequence all 3.2 billion bases of the human genome (at 30X coverage) has increased

Increase Sequencing Efficiency with the SeqCap EZ Prime Exome

How much sequencing do I need? Emily Crisovan Genomics Core September 26, 2018

GENOMICS WORKFLOW SOLUTIONS THAT GO WHERE THE SCIENCE LEADS. Genomics Solutions Portfolio

NextGen Sequencing Technologies Sequencing overview

Automating Genomics Applications with the Agilent Bravo Workstation

Non-coding Function & Variation, MPRAs. Mike White Bio5488 3/5/18

Transcription:

NGS 101 Panel Design and Quality Adam Hauge Development Manager University of Minnesota Genomics Center

Disclaimer SureSelect products are research use only SureSelect has not been validated by Agilent Technologies for clinical use All claims and uses presented today have been evaluated independently of Agilent Technologies

Overview Panel Design Quality at the Bench Future Considerations

Overview Panel Design Quality at the Bench Future Considerations

Introduction University of Minnesota Genomics Center Expression Analysis Genotyping Sequencing Minnesota Supercomputing Institute Computing Resources University Outreach Fairview Molecular Diagnostic Laboratory Clinical Diagnostics Disease Monitoring

Introduction Documentation Training Cost Assay 2 Assay 1 Assay 3 Single Assay

Panel Design Process Pediatrics Neurology Hematology 130 Disease Conditions 600 Genes 10,000 Exons Single Capture

Panel Design Process 2X bait coverage Single bait coverage

Panel Design Process % on Target Reads on Target Total Reads sample # unique paired reads 100 % coverage at 30X 100 % coverage at 20X 100 % coverage at 10X exons genes exons genes exons genes 1 15,408,360 9525 (94.6 %) 308 9755 (96.9 %) 392 9929 (98.6 %) 475 2 19,332,263 9517 (94.5 %) 317 9748 (96.8 %) 391 9926 (98.6 %) 473 3 14,946,525 9413 (93.5 %) 287 9705 (96.4 %) 371 9909 (98.4 %) 462 4 20,659,002 9664 (96.0 %) 352 9836 (97.7 %) 430 9953 (98.9 %) 489 5 17,512,596 9620 (95.6 %) 344 9815 (97.5 %) 413 9953 (98.9 %) 487 6 18,917,409 9606 (95.4 %) 351 9802 (97.4 %) 411 9940 (98.7 %) 487 7 24,029,918 9788 (97.2 %) 408 9906 (98.4 %) 461 9975 (99.1 %) 499 8 18,982,380 9653 (95.9 %) 355 9824 (97.6 %) 425 9952 (98.9 %) 490 9 15,194,449 9498 (94.3 %) 310 9745 (96.8 %) 379 9929 (98.6 %) 474 10 18,160,028 9632 (95.7 %) 349 9800 (97.3 %) 410 9946 (98.8 %) 484 11 18,176,710 9636 (95.7 %) 350 9801 (97.4 %) 409 9959 (98.9 %) 489 12 17,800,123 9630 (95.7 %) 349 9820 (97.5 %) 420 9946 (98.8 %) 479 30X Coverage Courtesy Geteria Onsongo

Panel Design Process 17 kb 575X Average 265X Minimum 170 bp 18X Average 7X Minimum Courtesy Geteria Onsongo

Panel Design Process Silverstein Rule Bower Rule

Panel Design Process Highest Coverage Lower Coverage Lower Coverage Highest Coverage Bower Rule Courtesy Geteria Onsongo

Panel Design Process Highest Coverage Lower Coverage Lower Coverage Highest Coverage Silverstein Rule Courtesy Geteria Onsongo

Panel Design Process No Effect Silverstein Rule Courtesy Geteria Onsongo

Percent of Exons Panel Design Process 100.00% Exons at 100% Coverage 99.00% 98.00% 97.00% 96.00% 95.00% 94.00% 93.00% 10X 20X 30X 92.00% 91.00% 90.00% 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Sample Courtesy Geteria Onsongo

Panel Design Process Think about Design What are your metrics? Run a Pilot! Revise and Improve

Overview Panel Design Quality at the Bench Future Considerations

Overview Panel Design Quality at the Bench Future Considerations

Workflow Overview Sample Collection and Extraction Fairview MDL UMGC Sample Receipt Shearing Pre-capture Library Creation Hybridization and Capture Post-Capture Amplification Normalization and Pooling Quality Control PicoGreen PicoGreen Bioanalyzer PicoGreen Bioanalyzer QPCR

Workflow Overview Variant Calling Report Generation Fairview MDL UMGC Sequencing Data Delivery Quality Control Sequence Quality

SureSelect Target Enrichment Shear DNA Add Adapters Sequence Library Solution-based hybridization of RNA baits to target DNA within the genome Isolate and enrich target regions of interest

Quality at the Bench Sample Handling Sample Barcode Sample A 835823885 Sample B 292058338 Sample C 242948290

Quality at the Bench Sample Handling Pre-Capture Library Capture Plate 1 2 3 4 1 2 3 4 A A B C D Hyb B C D E E F F G G H H Process Design

High-Throughput Processing Quality at the Bench

Quality at the Bench High-Throughput Processing Process Design

Quality at the Bench Hybridization and Capture Off-target On-target Assess Labware Avoid Evaporation! "#$%&'() *' +, --%". / 0#'(1! "#$ %&' ()*+, -. / 0001220343 5*6+7(%)+ %&' ()*+ 2. -4 0001202. 13 86*77 %&' ()*+ 2. -4/ 0001220343 9%: ; %&' ()*+ 2. -4/ 00010<0/0.! "#$ +%' (6=&)>=()*+?-/ 000120, <2, 5*6+7(%)+ +%' (6=&)>=()*+?-1 000120, /10 Process Design

Quality at the Bench Normalization and Loading Lanes Sample Pool 1, 2 1 3, 4 2 5, 6 3 7, 8 4 1 2 3 4 5 6 7 8 Process Design

Quality at the Bench and Beyond Laboratory Information Systems Process Design

Coefficient of Variation Quality at the Bench Quantitation 12.00% Sample Balance in Sequencing 10.00% 8.00% 6.00% 4.00% 2.00% 0.00% Sequence Run Normalized Sample Input Quality Control

Quality at the Bench Agilent Bioanalyzer Adapter Genomic Insert Adapter

Percent of Bases Quality at the Bench Agilent Bioanalyzer % of exon bases with 30X coverage 100 80 60 40 20 Sample 1 Sample 2 0 150 300 400 Insert Size (bp) Adapter Genomic Insert Adapter Quality Control

Fold Enrichment Quality at the Bench qpcr Capture Efficiency 40.00 Post-Capture Amplified 40 30 20 10 Pre-Capture On-Target #1 On-Target #2 On-Target #3 Off-Target #1 Off-Target #2 30.00 20.00 10.00 0.00 1 3 5 7 9 11 13 15 17 19 21 23 1000000 On-Target #1 On-Target #2 On-Target #3 Off-Target #1 0 Off-Target #3 100000 10000 40 30 20 10 0 Post-Capture On-Target #1 On-Target #2 On-Target #3 Off-Target #1 Off-Target #2 Off-Target #3 1000 100 10 1 Sample Quality Control

Pass-Filter Reads Quality Score Quality at the Bench and Beyond Sequence Output Quality Cutoff 40 35 30 25 20 15 10 5 0 R1 R2 R1 R2 R1 R2 R1 R2 R1 R2 R1 R2 L1 L2 L3 L4 L5 L6 Lane/Read 80,000,000 70,000,000 60,000,000 50,000,000 40,000,000 30,000,000 20,000,000 10,000,000 0 Sample Quality Control

Quality at the Bench and Beyond Blinded Proficiency Samples Sample A- 1 1call 27 calls Sample A- 2 Quality Control

Percent of Exons Percent of Exons Quality at the Bench and Beyond Quality Control Process Design 100.00% 98.00% 96.00% 94.00% 92.00% 90.00% 100.00% 98.00% 96.00% 94.00% 92.00% 90.00% Exons at 100% Coverage (10, 20, 30X) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Sample Exons at 100% Coverage (30X) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Sample

Overview Panel Design Quality at the Bench Future Considerations

Overview Panel Design Quality at the Bench Future Considerations

Future Design Considerations Metabolism Nephrology Cardiology Pulmonary >1,600 Genes Single Capture?

Future Design Considerations Exome Capture? Assay B Assay A Assay C Cost? Logistics? Single Assay Performance Functionality Turnaround

Turnaround time (weeks) Future Design Considerations 18 Trend for turnaround time in first year 16 14 12 10 8 6 4 2 0 Sep 2012 Oct 2012 Nov 2012 Dec 2012 Jan 2013 Feb 2013 Mar 2013 Apr 2013 May 2013 June 2013 Turnaround time (weeks) Operational Efficiency Courtesy Matt Bower

Future Design Considerations Workflow Overview Workflow Overview Physician Test Order Physician Interpretation of Report and Decision Sample Collection and Extraction Variant Calling Report Generation Fairview MDL UMGC Fairview MDL UMGC Sample Receipt Shearing Pre-capture Library Creation Hybridization and Capture Post-Capture Amplification Normalization and Pooling Sequencing Data Delivery Quality Control PicoGreen PicoGreen Bioanalyzer PicoGreen Bioanalyzer QPCR Quality Control Sequence Quality 1-4 1-3 2-4 2-4 Total Turnaround (weeks)

Future Design Considerations Capture Efficiency Speed Complexity Cost

Cost Speed Length Capacity Future Design Considerations HiSeq 2000 HiSeq 2500 MiSeq

Future Design Considerations How can you improve? What are your options? How should you sequence?

Final Thoughts SureSelect is a Great Tool Invest in Design and Run Pilots Don t Underestimate Quality Control

Acknowledgements University of Minnesota Genomics Center Kenneth Beckman Archana Deshpande Aaron Becker Karina Sartorio Adam Hauge Minnesota Supercomputing Institute Kevin Silverstein Getiria Onsongo Jesse Erdman Fairview Molecular Diagnostic Laboratory Bharat Thyagarajan Matt Bower Matt Schomaker Teresa Kemmer Sophia Yohe

Thank You! Questions? Adam Hauge haug0296@umn.edu