Bioinformatics and Comparative Genomics Applications

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1 Bioinformatics and Comparative Genomics Applications 12OCT2016 Richard H. Scheuermann, Ph.D. Director of Informatics - JCVI

2 Outline Bioinformatics and the Big Data value proposition Viral database resources Influenza Research Database (IRD) Virus Pathogen Resource (ViPR) Comparative genomics applications Identification of diagnostic regions of Zika and other human flaviviruses Identification of virulence determinants of enterovirus D68

3 What is Bioinformatics? And related terms biomedical informatics, computational biology, systems biology Wikipedia Bioinformatics: an interdisciplinary field that develops and improves on methods for storing, retrieving, organizing and analyzing biological data. A major activity in bioinformatics is to develop software tools to generate useful biological knowledge. NIH Biomedical Information Science and Technology Initiative Consortium (BISTIC) Bioinformatics: Research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or visualize such data. Computational Biology: The development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and social systems.

4 Biological data types and analysis objectives Genomics Nucleotide genome sequences, metagenomic sequences Gene finding, functional annotation, homology determination, sequence alignment, comparative analysis, phylogenetic inferencing, association analysis, mutation functional prediction, species distribution analysis Transcriptomics RNA expression levels, transcription factor binding, chromatin structure information Differential expression, clustering, functional enrichment, transcriptional regulation/causal reasoning Proteomics Proteins levels, protein structures, protein interactions Protein identification, protein functional predictions, structural predictions, structural comparison, molecular dynamic simulation, mutation functional prediction, docking predictions, network analysis Metabolomics Metabolite/small molecule levels Pathway/network analysis Imaging Microscopy images, MRI images, CT scans Feature extraction, high content screening Cytometry Cell levels, cell phenotypes Cell population clustering, cell biomarker discovery Systems biology All of the above Network analysis, causal reasoning, reverse causal reasoning, drug target prediction, regulatory network analysis, information flow, population dynamics, modeling and simulation

5 BIG DATA Big Data

6 Data Science at NIH

7 Big Data 3 V s

8 Variety

9 No Variety 0.01 A/Boston/26/2008 A/Canada-MB/RV2018/2009 A/Canada-SK/RV1767/2009 A/Canada-MB/RV1975/2009 A/Mexico/InDRE13495/2009 A/Lyon/969/2009 A/Michigan/30/2009 A/Kanagawa/140/2009 A/Taiwan/90262/2011 A/England/328/2009 A/England/348/2009 A/England/345/2009 A/England/377/2009 A/England/360/2009 A/England/364/2009 A/England/349/2009 A/England/399/2009 A/England/342/2009 A/England/374/2009 A/England/350/2009 A/Beijing/16/2009 A/California/08/2009 A/California/07/2009 A/Helsinki/490/2013 A/Helsinki/753/2013 A/Helsinki/979/2013 A/California/07/2009 A/California/07/2009 A/California/07/2009 A/California/07/2009 A/England/201/2009 A/California/08/2009 A/California/08/2009 A/California/04/2009 A/Hangzhou/04/2009 A/Pennsylvania/09/2009 A/California/04/2009 A/California/04/2009 A/Hangzhou/06/2009 A/Hangzhou/10/2009 A/Fukuoka-C/3/2009 A/Fukuoka-C/2/2009 A/Fukuoka-C/1/2009 A/Kagoshima/1/2009 A/California/04/2009 A/California/07/2009

10 Big Data Volume + Variety = Value Variety = Metadata

11 DMID Genomics Courtesy of Alison Yao, DMID

12 Bioinformatics Resource Centers (BRCs)

13 IRD Datawww.fludb.org Summary

14 Virus Pathogen Resource

15 Database Query

16 Analysis Tools

17 Personal Workbench

18 Alex Lee Identification of diagnostic regions of Zika and other human flaviviruses

19 Zika virus background

20 Motivation Current ELISA and neutralization assays are not conclusive for detecting antibodies against specific flaviviruses due to cross reactivity between related flaviviruses. In order to achieve better incidence and prevalence estimates for Zika and other flavivirus infection, can we identify peptide regions in flavivirus proteins that are sensitive and specific for each of the different species co-circulating in endemic areas for detection of serum antibodies?

Identification of diagnostic peptide regions Species X vs other species within the genus ZIKV vs DENV1, 2, 3, 4, YFV, WNV, SLEV, JEV Genome sequence selection

21 Identification of diagnostic peptide regions Species X vs other species within the genus ZIKV vs DENV1, 2, 3, 4, YFV, WNV, SLEV, JEV Genome sequence selection Mature peptide prediction Meta-CATS comparative analysis Sensitivity & specificity calculation Diagnostic peptide region determination Epitope and structure prediction

22 Flavivirus protein sequences

24 NS1 diagnostic sites

25 E diagnostic sites

26 Diagnostic peptide regions for ZIKV

27 Diagnostic peptide regions for all flaviviruses

28 Diagnostic peptide regions for all flaviviruses

29 Summary statistics for E proteins Group 1 Count Group 2 Count No. significant sites (512 total sites) a No. candidate diagnostic sites b No. 15-mers with at least 3 diagnostic sites No. 15-mers with at least 3 diagnostic residues and is surface exposed Dengue Virus 1(DENV1) 997 All Other Dengue Virus 2(DENV2) 1,000 All Other Dengue Virus 3(DENV3) 1,000 All Other Dengue Virus 4(DENV4) 897 All Other Ilheus Virus (ILHV) 4 All Other Japanese Enchephalitis Virus (JEV) 1,000 All Other St. Louis Encephalitis Virus (SLEV) 178 All Other West Nile Virus (WNV) 993 All Other Yellow Fever Virus (YFV) 162 All Other Zika Virus (ZIKV) 71 All Other a Significant sites determined by Meta-CATS group comparison at a p value cutoff of 9.766e-5 (0.05/512) b Candidate diagnostics sites that were significant by Meta-CATS group comparison and showed average sensitivity/specificity above 98%

Summary statistics for NS1 proteins Group 1 Count Group 2 Count No. significant sites (353 total sites) a No. candidate diagnostic sites b No. 15-mers with at least 3 diagnostic sites No.

30 Summary statistics for NS1 proteins Group 1 Count Group 2 Count No. significant sites (353 total sites) a No. candidate diagnostic sites b No. 15-mers with at least 3 diagnostic sites No. 15-mers with at least 3 diagnostic residues and is surface exposed Dengue Virus 1(DENV1) 1,000 All Other Dengue Virus 2(DENV2) 1,000 All Other Dengue Virus 3(DENV3) 951 All Other Dengue Virus 4(DENV4) 185 All Other Ilheus Virus (ILHV) 6 All Other Japanese Enchephalitis Virus (JEV) 227 All Other St. Louis Encephalitis Virus (SLEV) 36 All Other West Nile Virus (WNV) 988 All Other Yellow Fever Virus (YFV) 72 All Other Zika Virus (ZIKV) 34 All Other a Significant sites determined by Meta-CATS group comparison at a p value cutoff of 1.416e-4 (0.05/353) b Candidate diagnostics sites that were significant by Meta-CATS group comparison and showed average sensitivity/specificity above 98%

ZIKV E protein 20 15 a b c d e f g 10 5 0 1 10 19 28 37 46 55 64 73 82 91 100 109 118 127 136 145 154 163 172 181 190 199 208 217 226 235 244 253 262 271 280 289 298 307 316 325 334 343 352

31 ZIKV E protein a b c d e f g g d f e c Note: Highlighted bars correspond to 15-mers where ZIKV have at least 3 diagnostic residue and is surface exposed b a

32 Summary Using sequence data and comparative genomics analysis tools in ViPR, identified regions of the E and NS1 proteins that are sensitive and specific for each of the major human flavivirus species These regions are now being used to develop peptide arrays for the detection of serum antibodies against the different species for epidemiological analysis of disease outbreaks and spread Lee A, et al. (2016) Identification of Diagnostic Peptide Regions that Distinguish Zika Virus from Related Mosquito-Borne Flaviviruses PLOS One, submitted.

33 Yun Zhang Identification of virulence determinants of Enterovirus D68 (EV-D68)

Enterovirus D68 2014 Outbreak Mid-August 2014 mid-january 2015: 1,153 confirmed cases including 14 deaths in the US; likely many more

jpg 103 paralysis cases of unknown etiology in the US o o 4/10 paralyzed children in CO were EV-D68 positive 2/23 Acute Flaccid Paralysis

34 Enterovirus D Outbreak Mid-August 2014 mid-january 2015: 1,153 confirmed cases including 14 deaths in the US; likely many more cases of mild EV- D68 infections (CDC) paralysis cases of unknown etiology in the US o o 4/10 paralyzed children in CO were EV-D68 positive 2/23 Acute Flaccid Paralysis (AFP) cases in CA (June 2012 June 2014) were EV-D68 positive EV-D68 positive AFP cases Canada, France, Norway, and Australia orial/girlhospital_40286.jpg

35 Enterovirus D68 in 2016 On October 3, 2016 the US CDC reported that as of August 2016 there have been 50 cases of confirmed AFM across 24 states More than double the 21 cases reported in all of 2015

36 US AFM cases Source -

37 Enterovirus clinical symptoms Most infections are asymptomatic Polioviruses, types 1-3 Paralysis (complete to slight muscle weakness) Aseptic meningitis Undifferentiated febrile illness, particularly during the summer Coxsackieviruses, group A, types 1-24 Herpangina Acute lymphatic or nodular pharyngitis Aseptic meningitis Paralysis Exanthema Hand-foot-and-mouth disease (A10, A16) Pneumonitis of infants "Common cold" Hepatitis Infantile diarrhea Acute hemorrhagic conjunctivitis (type A24 variant) Coxsackieviruses, group B Pleurodynia Aseptic meningitis Paralysis (infrequently) Severe systemic infection in infants, meningoencephalitis, and myocarditis Pericarditis, myocarditis Upper respiratory illness and pneumonia Rash Hepatitis Undifferentiated febrile illness Echoviruses, types 1-33 Aseptic meningintis Paralysis Encephalitis, ataxia, or Guillain-Barre syndrome Exanthema Respiratory disease Others: Diarrhea Pericarditis and myocarditis Hepatic disturbance Enterovirus, types Pneumonia and bronchiolitis Acute hemorrhagic conjunctivitis (type 70) Paralysis (types 70, 71) Meningoencephalitis (types 70, 71) Hand-foot-and-mouth disease (type 71 ) Fields Virology, 2007

38 Non-enveloped +ssrna Enterovirus Genome

Goal & Analysis Workflow Are genetic changes in recent D68 outbreak isolates responsible for the increased disease severity and severe neurologic symptoms?

39 Goal & Analysis Workflow Are genetic changes in recent D68 outbreak isolates responsible for the increased disease severity and severe neurologic symptoms? Select all D68 nucleotide and protein sequences Mature peptide prediction Phylogenytrait association with BaTS Meta-CATS, sensitivity & specificity calculation Comparison with other paralysiscausing enteroviruses Functional prediction

40 EV-D68 VP1 Nucleotide Tree

Phylogeny-trait correlation Objective: Test for phylogeny-trait correlations Given a discrete trait for each tip in the phylogenetic tree, are more closely related taxa more likely to share the same

41 Phylogeny-trait correlation Objective: Test for phylogeny-trait correlations Given a discrete trait for each tip in the phylogenetic tree, are more closely related taxa more likely to share the same trait values than we would expect by chance? Parker J, Rambaut A, Pybus OG (2008) Correlating viral phenotypes with phylogeny: accounting for phylogenetic uncertainty. Infection, Genetics & Evolution8: BaTS

42 Bayesian Tip Significance (BaTS)

43 BaTS Algorithm Sample posterior distribution of phylogenies produced by BEAST, with the more likely phylogenies sampled more frequently For every tree in the sample, calculate the PS, AI, MC statistics forming the posterior distribution of the statistics Generate n random trait-taxon association sets - null distribution If the median of the null distribution is more extreme than the median of the posterior distribution of the statistics observed, then the p-value is significant

44 BaTS results

45 Candidate genetic determinants

46 1_Mahoney * * * Multiple sequence alignments /GX/CHN/2001_71 * /GX/CHN/2001_71 * D68 Fermon D68:A CA/RESP/10_786 US/KY/14_18953 ns/usa/n0051u5/2012 EVD68/Homo_sapiens/USA/N0051U5/2012 D68:C JPOC10_290 JPOC10_378 5 UTR/697C 6 D68:B1 CA/AFP/v12T00346 * * US/CA/14_6092 * * US/CA/14_6100 * * US/CO/13_60 * * US/CO/14_93 * * US/CO/14_94 * * 712/2014 EV68/Ontario/C818712/2014 US/MO/14_18947 US/MO/14_18950 D68:B2 US/IL/14_18952 NY73 2C/1G VP2/222T 5 UTR/697C VP3/24A VP2/222T VP1/98A VP1/290S VP3/24A VP1/308N VP1/98A VP1/290S 2A/66N VP1/308N 2A/66N D68 D68:A D68:C Fermon CA/RESP/10_786 US/KY/14_18953 EVD68/Homo_sapiens/USA/N0051U5/2012 JPOC10_290 JPOC10_378 D68:B1 CA/AFP/v12T00346 * US/CA/14_6092 * US/CA/14_6100 * US/CO/13_60 * US/CO/14_93 * US/CO/14_94 * EV68/Ontario/C818712/2014 US/MO/14_18947 US/MO/14_18950 D68:B2 US/IL/14_18952 NY73 D70 J670/71 1_Mahoney PV Human_poliovirus_1_Mahoney * * PV2/Bel_2 * * P3/Leon/37 * * /GX/CHN/2001_71 A71 AFP /EV71/GX/CHN/2001_71 * * /GX/CHN/2001_71 AFP /EV71/GX/CHN/2001_71 * * D70 J670/71 PV Human_poliovirus_1_Mahoney * PV2/Bel_2 * P3/Leon/37 * A71 AFP /EV71/GX/CHN/2001_71 * AFP /EV71/GX/CHN/2001_71 * 2C/1G 2C/34T 2C/102V 2C/1G 2C/273G 2C/34T 2C/102V 3D/135S 2C/273G 3D/274K 3D/135S 3D/345Q 3D/274K 3D/345 D68 D68:A Fermon CA/RESP/10_786 US/KY/14_18953

47 Capsid heterotrimer structure

48 IRES structure 262C 280C 339T IRES IV GNRA sequence V 496G VI I 127U (123) II 188A (185) III Pyrimidinerich region Variable region 743 Coding region

49 CA/AFP/ USA EV-D68 VP1 Nucleotide Tree CA/RESP/ USA Fermon USA Clade A US/KY/ /2014 USA CA/RESP/ USA Clade C Clade B B1 B CHN/CQ2860/2012 NA China /15/2011 China /20/2013 China /19/2011 China CA/AFP/v12T USA CA/AFP/v12T USA US/CO/13-60-EV-D68 11/2013 USA EVD68/Homo sapiens/usa/mo41/2014-d USA EV68_Alberta4693_2014-D68 08/31/2014 Canada EVD68/Homo sapiens/usa/mo12/2014-d USA EVD68/Homo sapiens/usa/mo19/2014-d USA EVD68/Homo sapiens/usa/mo51/2014-d USA EVD68/Homo sapiens/usa/mo8/2014-d USA EVD68/Homo sapiens/usa/mo35/2014-d USA ITA/25702/14 10/27/2014 Italy BRE1466-FRA14 11/27/2014 France ITA/25700/14 10/27/2014 Italy CAE1133-FRA14 10/20/2014 France ITA/25686/14 10/26/2014 Italy ITA/25663/14 10/25/2014 Italy ITA/25185/14 10/20/2014 Italy ITA/25861/14 10/28/2014 Italy ITA/23987/14 10/04/2014 Italy ITA/25571/14 10/24/2014 Italy NY160 10/05/2014 USA US/CO/ /2014 USA NY329 09/26/2014 USA EV68/Ontario/C818712/ /15/2014 Canada CAE1103-FRA14 09/11/2014 France CF298032_FRA14 10/2014 France VERS342154_FRA14-nterovirus D68 11/2014 France VALE2314_FRA14 10/23/2014 France VALE2315_FRA14 10/23/2014 France CAE1283-FRA14 10/27/2014 France EVD68/Homo sapiens/usa/mo33/2014-d USA CAE1428-FRA14 11/25/2014 France NY210 10/14/2014 USA EVD68/Homo sapiens/usa/mo58/2014-d USA EVD68/Homo sapiens/usa/mo38/2014-d USA EVD68/Homo sapiens/usa/mo39/2014-d USA EVD68/Homo sapiens/usa/mo21/2014-d USA BRE1464-FRA14 10/21/2014 France BRE1460-FRA14 10/31/2014 France NY77 09/28/2014 USA NY278 09/19/2014 USA CAE1108-FRA14 09/25/2014 France NY305 09/22/2014 USA EVD68/Homo sapiens/usa/mo49/2014-d USA EVD68/Homo sapiens/usa/mo6/2014-d USA EV-D68/Haiti/1/ /01/2014 Haiti NY153 10/02/2014 USA CAE1125-FRA14 10/16/2014 France LYO FRA14 11/20/2014 France LYO _FRA14 11/12/2014 France LYO FRA14 11/12/2014 France EV-D68/environment/Gainesville/1/ /08/2015 USA NY316 09/24/2014 USA NY328 09/26/2014 USA CA/AFP/v14T USA EVD68/Homo sapiens/usa/mo18/2014-d USA EVD68/Homo sapiens/usa/mo3/2014-d USA US/CA/14-R1 09/2014 USA EVD68/Homo sapiens/usa/mo30/2014-d USA EVD68/Homo sapiens/usa/mo53/2014-d USA EVD68/Homo sapiens/usa/mo5/2014-d USA EVD68/Homo sapiens/usa/mo44/2014-d USA EVD68/Homo sapiens/usa/mo37/2014-d USA EVD68/Homo sapiens/usa/mo23/2014-d USA EVD68/Homo sapiens/usa/mo25/2014-d USA EVD68/Homo sapiens/usa/mo50/2014-d USA NY309 09/23/2014 USA US/CA/ /2014 USA US/CA/ EV-D68 08/2014 USA US/MO/ EV-D68 08/2014 USA EVD68/Homo sapiens/usa/mo32/2014-d USA EVD68/Homo sapiens/usa/mo27/2014-d USA AMI030176_FRA14-nterovirus D68 10/2014 France EVD68/Homo sapiens/usa/mo14/2014-d USA US/CO/14-94-EV-D68 09/2014 USA EVD68/Homo sapiens/usa/mo31/2014-d USA EVD68/Homo sapiens/usa/mo2/2014-d USA EVD68/Homo sapiens/usa/mo1/2014-d USA EVD68/Homo sapiens/usa/mo17/2014-d USA EVD68/Homo sapiens/usa/mo48/2014-d USA EVD68/Homo sapiens/usa/mo4/2014-d USA EVD68/Homo sapiens/usa/mo20/2014-d USA EVD68/Homo sapiens/usa/mo56/2014-d USA AMI030123_FRA14-nterovirus D68 09/2014 France AMI030164_FRA14-nterovirus D68 10/2014 France VERS339130_FRA14-nterovirus D68 10/2014 France EVD68/Homo sapiens/usa/mo46/2014-d USA US/CO/ /2014 USA EVD68/Homo sapiens/usa/mo57/2014-d USA US/CA/ EV-D68 10/2014 USA US/CA/ SIB-EV-D68 10/2014 USA EVD68/Homo sapiens/usa/mo42/2014-d USA EVD68/Homo sapiens/usa/mo26/2014-d USA EVD68/Homo sapiens/usa/mo13/2014-d USA EVD68/Homo sapiens/usa/mo40/2014-d USA EVD68/Homo sapiens/usa/mo10/2014-d USA US/MO/ EV-D68 08/2014 USA EVD68/Homo sapiens/usa/mo7/2014-d USA LYO FRA14 12/12/2014 France NY275 09/19/2014 USA NY126 09/28/2014 USA EVD68/Homo sapiens/usa/mo11/2014-d USA EV-D68_STL_2014_ USA US/MO/ EV-D68 08/2014 USA EVD68/Homo sapiens/usa/mo47/2014-d USA EVD68/Homo sapiens/usa/mo24/2014-d USA EVD68/Homo sapiens/usa/mo28/2014-d USA EVD68/Homo sapiens/usa/mo16/2014-d USA EVD68/Homo sapiens/usa/mo15/2014-d USA CF311065_FRA14 11/2014 France CF287062_FRA14 10/2014 France US/CA/ /2014 USA EVD68/Homo sapiens/usa/mo60/2014-d USA EVD68/Homo sapiens/usa/mo22/2014-d USA NY130 09/30/2014 USA NY120 09/28/2014 USA MEX/DGO/2014-InDRE /16/2014 Mexico EVD68/Homo sapiens/usa/mo9/2014-d USA EVD68/Homo sapiens/usa/mo43/2014-d USA AMI030174_FRA14-nterovirus D68 10/2014 France EVD68/Homo sapiens/usa/mo54/2014-d USA EV68_Alberta17390_2014-D68 08/18/2014 Canada EVD68/Homo sapiens/usa/mo45/2014-d USA EVD68/Homo sapiens/usa/mo34/2014-d USA US/MO/ EV-D68 08/2014 USA EVD68/Homo sapiens/usa/mo59/2014-d USA EV68/Ontario/C818710/ /15/2014 Canada NY326 09/25/2014 USA NY263 09/18/2014 USA US/CA/14-R2 09/2014 USA MEX/DF/2014-InDRE /23/2014 Mexico NY124 09/29/2014 USA NY314 09/24/2014 USA CHN/CQ7170/ /18/2014 China CHN/CQ5571/ /16/2013 China CHN/CQ7208/ /22/2014 China CHN/CQ7280/ /13/2014 China CHN/CQ7233/ /30/2014 China 2014-R970 10/15/2014 China CHN/CQ7349/ /22/2014 China CHN/CQ7174/ /18/2014 China 2014-R /20/2014 China CHN/CQ7188/ /11/2014 China 2014-R /20/2014 China 2014-R /20/2014 China EV68_Alberta2985_2014-D68 09/02/2014 Canada CHN/CQ7214/ /23/2014 China CHN/CQ7226/ /26/2014 China CHN/CQ7225/ /27/2014 China 2014-R /19/2014 China Beijing-R China CHN/CQ7283/ /30/2014 China CHN/CQ7307/ /07/2014 China CHN/CQ7360/ /25/2014 China + * * * * * * * * LEGEND Year * AFM + Encephalitis

50 Hypothetical severity distribution Paralysis in symptomatic: PV - ~3.6% D68 B1 - ~6.9%

51 Conclusions 3 distinct clades of EV-D68 are co-circulating during the recent outbreak Unique amino acid and nucleotide substitutions were identified in the recent EV-D68 isolates Several of the EV-D68 unique substitutions are also found in the equivalent positions of paralysis-associated poliovirus, EV-D70, and/or EV-A71 isolates These substitutions may be responsible for the apparent change in symptomatology associated with the new D68 outbreak Zhang Y, et al. (2016) Genetic changes found in a distinct clade of Enterovirus D68 associated with paralysis during the 2014 outbreak Virus Evolution, 2(1):vew015. doi: /ve/vew015. RPRC Innovation Project - Test affects of these substitutions on IRES function, replication efficiency, and receptor binding using synthetic genomics

52 Summary Brief overview of IRD and ViPR resources Identification of diagnostic regions of Zika and other human flaviviruses Identification of virulence determinants of enterovirus D68

53 Team J. Craig Venter Institute Richard Scheuermann (PI) Brian Aevermann Douglas Greer Alexander Lee Lucy Stewart Yun Zhang Brian Reardon Northrop Grumman Mary Shaffran, Program Manager Ed Klem, Project Manager Zhiping Gu Sherry He Sanjeev Kumar Xiaomei Li Jason Lucas Tom Smith Bryan Walters Sam Zaremba Hongtao Zhao Univ. Auckland Catherine Macken, Co-PI Univ. Calif. Davis Nicole Baumgarth, Co-PI Harvard Medical School David Knipe, Co-PI Univ. Texas Medical Branch Slobodan Paessler, Co-PI Univ. Georgia Daniel Perez, Co-PI Purdue Univ. Richard Kuhn, Co-PI Vecna Chris Larsen Al Ramsey Guangyu Sun Scientific Working Group Gillian Air, Univ. Oklahoma Ralph Baric, Univ. North Carolina Ruben Donis, CDC Naomi Forrester, UTMB Adolfo Garcia-Sastre, Mt Sinai Elodie Ghedin, Univ. Pittsburgh Elliot Lefkowitz, Univ. Alabama Phil Pellett, Wayne State Univ. David Topham, Univ. Rochester Richard Webby, St Jude NIAID / DMID Alison Yao Data providers NIAID HHSN C

Conserved Epitope Regions

Conserved Epitope Regions R. Burke Squires Influenza Research Database www.fludb.org ASV 2011, Minneapolis, MN Influenza Research Database (IRD) NIAID BioinformaLcs Resource Center (BRC) Free, public resource