National Center for Emerging and Zoonotic Infectious Diseases

1. Sequencing and Bioinformatics in the CDC Biotechnology Core Facility Branch • Sequencing Lab • Mike Frace, Team Lead • Lori Rowe • Marina Khristova • Mark Burroughs • MilliSheth National Center for Emerging and Zoonotic Infectious Diseases Division of Scientific Resources • Computational Lab • Scott Sammons, Team Lead • Kevin Tang • Kristen Knipe

2. Genome Sequencing Lab sequencing platforms Roche 454 Titanium + Illumina 2500 Illumina GA IIx Ion Torrent PGM PacBioSMRT sequencer IlluminaMiSeq

3. Building 23 Server Room – Main ISLE

4. High Performance Computing Cluster (Aspen) • What is it? • 35 compute nodes each with 12 processor cores, total of 420 cores, 110GB of memory, and 2 Tesla 2050 GPU cards • What can it do today? • 40 cluster applications are currently enabled including MatLab, Beast, MrBayes, Blast, MPI Blast, PacBioanalysis tools, Celera Assembler, CLC Server, Geneious Server

5. Isilon • What is it? • High speed, scalable, and redundant Network Attached Storage • Connected to both the CDC network and the Aspen HPC cluster utilizing Infiniband • Total of 500TB usable space • What can it do today? • It provides user workspace for end-users and HPC applications • Solves the problem of being out of disk space on individual servers • What are we doing with it? • Data warehouse for all scientific equipment • Central network share for all scientific users • Integrating directly with ITSO’s Active Directory forest

6. Private Cloud • What is it? • Support science through front-end and back-end services • Implementation of virtualized infrastructure • Currently in the process of being deployed • What can it do today? • Provide test environments for scientific projects • Lay the foundation for hardware consolidation and migration • What are we doing with it? • Standardize platforms • Centralize management

7. Sequencing Lab Origins • Began in 2001 • Mission: sequence 8 human smallpox viruses before the WHO revisits destruction of all smallpox stocks • By 2005, had sequenced over 150 smallpox and related poxvirus genomes. • 2006: Roche 454, focus moved to small bacterial genomes • 2010: Illumina GAIIx • 2011: Ion Torrent, PacBio

8. Sequencing: extended PCR Position of E-PCR overlapping amplicons A3 A5 End-R A9 A15 A17 A7 A13 A11 A1 End-L A10 A2 A4 A6 A8 A12 A14 A16 A18 D P O C E R K H M L I F N A S J B G Q HindIII map • Primers designed using VAR-BSH and VAC-CPN sequences • Primers target genes involved in reproduction & host response • Sequence sample: primers 40 sites, 1 enz. RFLP ~120 sites • PCR uses minimal DNA amounts, often no need to grow virus • PCR uses hifi expand long-template Taq & Pwo enzymes (Roche)

9. Sequencing Assembly: Phred/Phrap/Consed

10. Gene Prediction • Heuristic algorithm to assign quality scores to ORFs (from 1 to 100) • Quality scores are based on a number of factors including • Gene Predictions (glimmer, genemark, getorf) • Primary sequence homology to known genes (BLAST) • Presence of predicted promoter (MEME/MAST) • Size of predicted ORF • Presence of transcription terminal signals

11. Visualizing Gene Predictions and Differences

12. ITR ITR crm-D ORFs of CPVXs from 4 different clades

13. 45 Smallpox Strains C-1. non-West-African-African int CFR ~10% C-2. non-West-African African minor CFR <1% A. West African int. CFR ~10% C. Asian major CFR ~5 - 35% B. American alastrim minor CFR <1%

14. Taterapox Camelpox Cowpox clade IV CPXV90_ger2 Variola BRZ66 gar AF375130 AF375142 AF375138 JAP46 yam AF375129 AY902260 AF375143 AY009089 AF375141 AF375081 AF375135 AF377877 AF375093 L22579 X65516 AY902269 AF377878 AY902277 AF375085 X69198 AF377886 AF375090 AF375083 AY902289 AY902294 AF482758 AY902301 AY902295 AY902274 AY902272 AY902283 Ectromelia Z99054 AY902275 AY902286 AY902299 AY902276 AF012825 AY902303 AY902257 AY902304 AY902256 AY902268 AY902300 Cowpox clade III (CPXV91_ger3) AF375086 AY298785 AY298785 AY902298 AY902252 AY902270 AY902271 AF375087 AF375084 AY902253 CPV91 ger3 AY902308 X94355 AY902287 AY366477 VACLS1 AY902297 Cowpox clade II AY603355 AF377885 Z99045 NC 001559 AF375088 AY902288 AF375123 CPV90 ger2 AY243312 AF375077 AY902296 AF375078 AF375119 AF377884 Cowpox clade I AF375118 M14783 Vaccinia AF229247 AY523994 AF095689 AF375102 AF375112 AF375096 AF375098 Z99052 AF375099 AF375113 AF375095 Monkeypox Unrooted tree phylogenetic relationships of ORF encoding the hemagglutinin protein

15. GSL sequencing 2013 INFLUENZA NCIRD NCEZID NCHHSTP Neiseriaspp Hepatitis Mycobacterium tuberculosis Haemophilusinfluenzae Legionella pneumophila Legionella spp. Mycoplasmapneumonia Water cooling tower metagenomics Respiratory filter metagenomics Bordetella spp. Tick metagenomics Vibrio cholera Vibrio spp Camphylobacter Salmonella Bacillus anthracis Listera Bukholderiaspp Yersinia pestis Brucella spp. Klebsiella pneumonia Fungal Meningiditis Rift Valley Fever virus Lujo virus Marburg virus CCHF virus Lassa Fever virus Clinical sample metagenomics CGH Rhodoccocus Cryptosporidium Fasciolaspp Balamuthiaspp

16. Next-Gen Diagnostic Sequencing Applications Shotgun / Paired-End Sequencing: random shearing of DNA, even sequence coverage over entire genome. ‘Massively parallel’ sequencing not only produces throughput, it provides sequences of potentially millions of individual molecules (instant cloning). By sequencing a PCR reaction it allows the detailed search for low expression quasi-species or mutations which may signal growing drug or vaccine resistance – a process called ultra-deep or amplicon sequencing. Example: clinical case of poxvirus infection with samples exhibiting a reduced sensitivity to an antiviral drug. Complex clinical, laboratory or environmental samples can be sequenced to provide a diagnostic ‘snapshot’ of the resident organisms - an approach called metagenomic sequencing. Examples: tissue culture, soil, blood serum, sputum, stool

17. Shotgun / Paired-End Sequencing • De novo Assembly • Newbler • CLCBio • Mira • Geneious • Velvet • Celera Assembler • Reference Mapping • Newbler • CLCBio • Mira • Geneious • BWA • Bowtie

18. Genome Assembly Visualization

19. Genome Assembly Visualization

20. Genome Comparison

21. HGAP – Hierarchical Genome Assembly Process • PreAssembly • Generation of long accurate reads • Assembly • Choice of assemblers, but OLC (Overlap Layout Consensus) are best, MIRA and Celera Assembler • Consensus Polishing • Quiver – a quality aware consensus algorithm maps all reads back to the assembly and creates a new consensus

22. HGAP: PreAssembly 30X

23. HGAP: PreAssembly/Assembly • Correct seed reads with short reads • Assemble with Celera Assembler or MIRA

24. HGAP - Quiver • To reduce the remaining InDel and base substitution errors in the draft assembly, we use the PacBio Quiver, aquality-aware consensus algorithm. Four different per-base Quality Values (QV scores) represent the intrinsically calculated error probabilities for inserted, deleted, substituted and merged base calls in single pass reads. These values allow Quiver to generate a highly accurate consensus for the final assembly, which frequently exceeds QV50 (99.999% accuracy).

25. HGAP Example

26. HGAP Confirmation with Physical Mapping

27. HGAP Assembly Structural Confirmation

28. HGAP Sequence Confirmation with Illumina reads

29. Amplicon (deep) sequencing project Li, Damon- NCZEID/DVRD/PRB • Clinical case of progressive vaccinia infection from smallpox vaccination of an immune compromised patient • Pox antiviral ST-246 administered which targets pox gene F13L, a major envelope protein which mediates production of extracellular virus • Oral ST-246 given daily and vaccination site sampled over 3 week period

30. A region of gene F13L was amplified from clinical samples, deep sequenced, and compared to the smallpox vaccine reference sequence (Acambis 2000) Control swab prior to ST-246

31. 2 weeks after ST-246 T > A 943 C > T 869

32. 3 weeks after ST-246 C > T 869 T > A 943

33. What is Metagenomics? • Is the genomic study of DNA from uncultured microorganisms, generally from environmental samples • Related • Metatranscriptomics • Metaproteomics

34. Sample CoverageRarefaction Curves Samples Wooley JC, Godzik A, Friedberg I, 2010 A Primer on Metagenomics. PLoSComputBiol 6(2)

35. Classification Techniques • Supervised Taxonomic Classification • Homology-based • Database searching by similarity (BLAST, SW) • BLAST, BLASTX: genbank, specialized DBs: NCBI-ENV-NT, NCBI-ENV-NR • Composition-based • N-mer frequency • Markov Models, Support Vector Machines (SVM), need training set • Unsupervised Taxonomic Classification • Clustering methods • SOM - self-organizing maps • PCA – principal component analysis

36. Remove redundant sequences Unique sequences Mask repetitive and low complexity seqs Good sequences Non-human sequences BLASTN vsnt BLASTX vs nr Viral Metagenomic Pipeline (Wash U scripts implemented at CDC) Contigs, Reads Sample Collection DNA Library Construction BLASTN against Human Genome (e ≤ 1e-10) Sequencing Basecalling Vector Trimming BLASTN vs GB-viral Assembly Report Generation, Display in MEGAN, inspect top hits

37. Megan

39. Ugandan Outbreak Samples • 4 patients • Total RNA from patient sera • 2 samples per 454 run • ~ 565,000 reads/sample, avg length = 235nt • Sequences were screened for random library amplication primers and low quality • Assembled each run de novo using the 454 gsAssembler • Performed a blastx database search using the assembled contigs (overnight) • Visualized the blast output using MEGAN.

40. MEGAN (MetaGenomeANalyzer)

41. Ugandan Outbreak - results • Run1 - 5 contigs (out of 2463 > 100nt) matched YF virus, covering 98% of the genome (10,441 of 10,823bp) • Mapped each sample from Run1 using an Ethiopian YF virus as reference. 3229 individual reads from Sample 1 indentified as YF. • Run 2 – no YF reads found

42. Phylogenetic analysis of yellow fever virus sequences Laura McMullan (DHPP/VSPB)

43. Comparative Metagenomics • One 454 run • Two samples • Sample 1 – ~578,000 reads, avg read length 438 bases • Sample 2 – ~550,000 reads, avg read length 425 bases • Total number of bases sequenced - ~488,000,000

44. Sample 1 – Rarefaction Curve

45. Sample 1 Taxa tree (collapsed at the Order level)

46. Comparison of Sample 1 and 2

47. Bioinformatics Tools • Bioinformatics Packages • EMBOSS • CLCbio • Geneious • LaserGene-Ngen • Galaxy • General Tools/ Languages • Java/BioJava • Perl/BioPerl • R • BLAST Suite • BioEdit • Genome Comparison/Alignment Tools • Mavid • Mauve • Clustal • Muscle • MAFFT • Gene Prediction • Glimmer • GeneMark • Assembly/Mapping Tools • 454 Suite • Mosaik Tools • Mummer • BWA • Velvet • AHA (pacbio) • Functional Annotation • Manatee • Phylogenetics • Paup • Phylip • MrBayes • Beauti/Beast • MEGA • DnaSP • Metagenomics • MEGAN • Galaxy • Carma • In-House • WAMS • POCs/VOCs

48. Challenges Data Management – image files are large moving these files around the network is slow Assembly/Mapping Software – Some are provided with the instrument, but additional methods and algorithms are needed Finishing Tools – gap filling, primer design Visualization Tools – tools to graphically display contigs on reference sequence as well as genome multiple alignments Generic Robust Annotation Tools – Researchers need tools to intelligently choose predicted ORFs as genes, assign function, and submit to GenBank

50. What are the weaknesses of current next-gen sequencers? Complicated and time consuming library preparation • Requires micrograms of DNA to begin 3 days to prepare library Requires amplification of library Low copy number polymorphisms may be missed Emulsion PCR is an inefficient, time consuming, oily mess Potential to introduce PCR bias into sample Instruments require repetitive sequential ‘flows’ of reagents Repetitive flows of nucleotides, blocking/unblocking chemistry, washing out reaction byproducts all slow synthesis and hinder read-length Consumes liters of reagents ($) Repetitive flows and imaging extend sequence runs to days (or weeks)