![2
Microbial NGS-Based Diagnostic Devices
• OIR/DMD working on a fast-tracked Draft Guidance
• On April 1st 2014 held Public Workshop
“Advancing Regulatory Science for High Throughput Sequencing
Devices for Microbial Identification and Detection of Antimicrobial
Resistance Markers” [FR Doc No: 2014-04940]
• Workshop agenda, discussion paper and webcast online:
http://www.fda.gov/MedicalDevices/NewsEvents/WorkshopsConferences/ucm386967.htm
Objectives:
1. Streamline/shorten clinical trials for microbial diagnosis/identification
2. Establish a new comparator algorithm for assays developed using this
new technology
3. Develop regulatory science standards for microbial genome sequencing
4. Investigate the regulatory science required for antimicrobial resistance
determination through microbial genome sequence information.](https://image.slidesharecdn.com/sichtightallonlmicrodbniststandards-141209162655-conversion-gate01/85/Development-of-FDA-MicroDB-A-Regulatory-Grade-Microbial-Reference-Database-2-320.jpg)
Uploaded byNathan Olson
Development of FDA MicroDB: A Regulatory-Grade Microbial Reference Database
The document discusses the development of a regulatory-grade microbial reference database (MicroDB) by the FDA to support microbial identification and the detection of antimicrobial resistance through next-generation sequencing. It outlines objectives, requirements for genomic data, and collaborations with clinical labs to ensure high-quality sequencing data is accessible and traceable. The project emphasizes the need for robust, standardized microbial sequence databases to enhance clinical diagnostics and research efforts.
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Similar to Development of FDA MicroDB: A Regulatory-Grade Microbial Reference Database
Development of FDA MicroDB: A Regulatory-Grade Microbial Reference Database
- 1. U.S. Food andDrug Administration Institute for Genome Sciences Development of FDA MicroDB: A Regulatory-Grade Microbial Reference Database Heike Sichtig, Ph.D. Division of Microbiology Devices OIR/CDRH/FDA/HHS Heike.Sichtig@fda.hhs.gov Genomics Resource Center Institute for Genome Sciences ljtallon@som.umaryland.edu October 21-22, 2014 Luke Tallon UMSOM NIST Workshop to Identify Standards Needed to Support Pathogen Identification via Next-Generation Sequencing, NIST, MD
- 2. 2 Microbial NGS-BasedDiagnostic Devices • OIR/DMD working on a fast-tracked Draft Guidance • On April 1st 2014 held Public Workshop “Advancing Regulatory Science for High Throughput Sequencing Devices for Microbial Identification and Detection of Antimicrobial Resistance Markers” [FR Doc No: 2014-04940] • Workshop agenda, discussion paper and webcast online: http://www.fda.gov/MedicalDevices/NewsEvents/WorkshopsConferences/ucm386967.htm Objectives: 1. Streamline/shorten clinical trials for microbial diagnosis/identification 2. Establish a new comparator algorithm for assays developed using this new technology 3. Develop regulatory science standards for microbial genome sequencing 4. Investigate the regulatory science required for antimicrobial resistance determination through microbial genome sequence information.
- 3. 3 Inter-Agency WorkingGroup on Feasibility Approach: • Formed a diverse working group FDA, NIH-NCBI, NIAID, DTRA, LLNL, and CDC • Conducted small pilot study to generate information to evaluate quality of existing sequences in the public domain (In Progress) • Identify the pre-existing high-quality deposits, and build from there • Will use information to set quality bar for sequence outputs for our ongoing sequencing efforts • Utilized existing standards (if available) for technical and isolate metadata –no need to re-invent • Attention given to connecting antimicrobial resistance phenotype to genomic deposits – clinical collection site
- 4. Looking ahead: Predictionsfor Reference Databases – Multiple levels of Reference DBs likely • “High quality” genomes only – For validation and clinical use • “High quality” + other available genomes – For testing and development • Requires definition of “high quality” that must include some draft genomes – Extensive screening required • Human and other hosts; chimeras • Artificial constructs – Separate bacterial, viral, fungal reference DBs – Publicly available (NCBI/EMBL/DDBJ) 4 Courtesy of Tom Slezak
- 5. 5 Current Need Robust, Standardized, and High Quality Microbial Sequence Database in the Public Sector Cover illustration (Copyright © 2009, American Society for Microbiology. All Rights Reserved.) • Representative Samples • Metadata • High quality raw sequences • Assemblies • Annotation • Public Domain
- 6. Latest NCBI GenbankReport on Bacterial Genome 25000 20000 15000 10000 5000 6 Growth 0 Bacterial Genomes Report Jul-‐98 Aug-‐99 Oct-‐00 Nov-‐01 Dec-‐02 Jan-‐04 Feb-‐05 Mar-‐06 Apr-‐07 Jun-‐08 Jul-‐09 Aug-‐10 Sep-‐11 Oct-‐12 Nov-‐13 Dec-‐14 Count Date #Genomes #Real Species Courtesy of NCBI
- 7. Microbial Reference Database(MicroDB)($1,67M) • Identify “gaps” and target sequencing efforts (Funding awarded by FDA/OCET) 7 • All raw reads, assemblies, annotations, metadata sent to NCBI and accessible to the PUBLIC • Traceable results that could be reevaluated as necessary >600 Clinically Relevant and MCM Microorganisms Highly Controlled and Documented Approach Collaborations with Clinical Labs and Repositories • Children’s National Hospital • DoD Critical Reagents Program (CRP, USAMRIID) • FDA-CFSAN, FDA-CBER, FDA-CDER • DHS National Biodefense Analysis and Countermeasures Center (NBACC) • The Rockefeller University • Culture Collections: ATCC, DSMZ Sequencing Center (UMD IGS) • Hybrid Approach (PacBio and Illumina) • Deposit of Raw Reads at NCBI (SRA) • Deposit of Assemblies at NCBI • Deposit of Annotations at NCBI • FDA Interface to Access Data
- 8. MicroDB Requirements A.Extracted Genomic DNA (gDNA) – Extracted gDNA should be of high quality and purity, and at sufficient concentration to achieve a suitable yield to assure adequate depth and breadth of genomic coverage for the type of sequencing method employed. B. BioSample Metadata – A minimal description of the isolate source material is necessary for traceability. We are using 14 descriptors as outlined below. (Note: Minimal metadata is modeled in part after NCBI’s minimal pathogen template) – Unique ID, organism, strain/isolate, sample site, specimen type, host disease, collection date, collected by, patient age, gender, geographic location, AST method*, AST method manufacturer*, Antimicrobial Susceptibilities* C. Sequencing Data – The minimum requirement for sequencing data is that the generated raw reads should be deposited in NCBI’s Sequence Read Archive (SRA) and assemblies should be deposited at NCBI’s Assembly division. The availability of raw reads and assemblies will provide a pathway to re-analyze the data as newer technologies emerge. Furthermore, annotation data should be deposited when available. – Raw reads, assemblies, annotations* *not used as a criteria for exclusion 8
- 9. MicroDB Requirements D.Sequencing Metadata – A minimal description of the sequencing process is necessary for traceability. We are using 7 descriptors as outlined below including bioinformatics tool information for assembly and annotation, and genomic coverage information. – Library, platform, submitted by, fold coverage, pipeline, assembler, annotation tool* E. Suggested phenotypic metadata* – A description of the phenotypic information is suggested to create a link between the phenotypic traits of particular organisms and their genomic sequence. We are recommending 5 descriptors as outlined below (1-4 are also included in sections B and C). – Annotation, AST method, AST method manufacturer, antimicrobial susceptibilities, additional phenotypic data *not used as a criteria for exclusion 9
- 10. NCBI Submission Cases 1. Childrens National Medical Center – Submit all data when available – Register sample metadata via BioSample – Submit raw reads and assemblies generated by IGS when available 2. FDA/CFSAN – Collaborative agreement: Wait for genome announcements – Follow same procedures as for 1 and put a ‘6 month hold’ to release data, lift hold when genome announcements are out 3. Rockefeller University – Collaborative agreement: Wait for publication – Follow same procedures as for 1 and put a ‘6 month hold’ to release data, lift hold when publication is out Similar agreements in place with other collaborators depending on their needs 10
- 11. Project Approach •Sequencing in large batches – Illumina HiSeq paired-‐end sequencing: >200x – PacBio long-‐insert SMRT P4-‐C2 sequencing: >80-‐100x • Assembly – PacBio only (HGAP, PBcR CA) – Illumina only (CA, MaSuRCA) – PacBio/Illumina hybrid (CA) – Minimal manual QA/QC & curaon • Automated Annotaon • Base modificaon detecon • Raw reads -‐> NCBI SRA • Assembled & annotated genomes -‐> Genbank – NCBI BIOPROJECT ID: PRJNA231221 • FDA Web interface to aggregate data
- 12. Progress -‐ Batch 1 Rockefeller (50) • Uniform sample set – Staphylococcus aureus – 2.8Mbp genome size – 32.8 %GC – Significant metadata CNH/CFSAN (41) • Diverse sample set – 18 genera represented – 2 – 8 Mbp genome size range – 38 – 67 %GC range Wikimedia Commons Wikimedia Commons NCBI BioProject: PRJNA231221
- 13. Rockefeller Samples •Sequencing – Avg Illumina cvg: 578x – Avg PacBio cvg: 185x – 1 or 2 SMRT cells each • Assembly: – 32 of 50 in single cong chromosome – Average cong count = 5 – “Best” assembly: • HGAP = 29 • CA hybrid = 21 • Most differences subtle • Annotaon complete • Final QC & data submissions underway
- 14. CNH/CFSAN Samples •Sequencing – Avg Illumina cvg: 315x – Avg PacBio cvg: 167x • 2 SMRT cells each • Assembly – 12 of 41 in single cong chromosome • 29 in <= 5 congs – Avg cong count = 4.5 – Median cong count = 3 – “Best” assembly (of 41): • HGAP = 24 • PBcR CA = 14 • CA hybrid = 3 • Annotaon underway
- 15. ROCK_290 Celera8 ctgvs. ref 0 500000 1000000 1500000 2000000 2500000 gi|374362062|gb|CP003033.1| 2500000 2000000 1500000 1000000 500000 0 ctg7180000000002 100 80 60 40 20 0 Assembly QC & Curaon %similarity CA8 – Ill/PB hybrid Largest Ctg Len: 2,759,091bp Total asm Ctg Len: 2,770,822 bp ROCK_290 HGAP2 ctg vs. ref 0 500000 1000000 1500000 2000000 2500000 gi|374362062|gb|CP003033.1| ssccff77118800000000000000001134||qquuiivveerr QRY ssscccfff777111888000000000000000000000000111012|||qqquuuiiivvveeerrr 100 80 60 40 20 0 %similarity HGAP2 Largest Ctg Len: 2,128,476bp Total asm Ctg Len: 2,802,621 bp
- 16. 4bp overlap? 0500000 1000000 1500000 2000000 2500000 gi|595636499|gb|CP007454.1| 2500000 2000000 1500000 1000000 500000 0 scf7180000000002|quiver Assembly QC & Curaon 100 80 60 similarity %40 20 0 HGAP2 Largest Ctg Len: 2,764,709bp Total asm Ctg Len: 2,764,709bp 1X coverage TAAC 1X coverage TAGC
- 17. Challenges & Opportunies • Sample acquision & quality • Efficiency/throughput vs. accuracy/quality – Sequencing strategy – Assembly QA/QC & curaon • Ever longer reads! – Reduced coverage -‐> higher efficiency sequencing – More “closed” genomes! • Small plasmids – SageELF & Illumina
- 18. FDA Micro Team Peyton Hobson, Brittany Goldberg, Kevin Snyder, Tamara Feldblyum, Uwe Scherf, Sally Hojvat C ollaborators 18 Thank You LLNL Tom Slezak NIH-NCBI Bill Klimke, Martin Shumway, David Lipman NIH-NIAID Vivien Dugan, Maria Giovani DTRA Matt Tobelmann, Chris Detter, Eric VanGieson, Nels Olsen CDC Duncan MacCannell FDA-CFSAN Maria Hoffmann, Cary Pirone, Andrea Ottessen, Marc Allard, Eric Brown NMRC Kim Bishop-Lilly, Ken Frey IGS@UMD Lisa Sadzewicz, Luke Tallon, Naomi Sengamalay, Al Godinez, Sandy Ott, Sushma Nagaraj, Claire Fraser Rockefeller University Bryan Utter, Douglas Deutsch Children’s National Medical Center Brittany Goldberg, Joseph Campos DOD-CRP Shanmuga Sozhamannan, Mike Smith DOD-USAMRIID Tim Minogue NBACC Adam Phillippy, Nick Bergman ATCC Liz Kerrigan DSMZ Cathrin Sproer