bioinformatics and comparative genomics applications · – bioinformatics: an interdisciplinary...

Bioinformaticsand

Comparative Genomics Applications

12OCT2016

Richard H. Scheuermann, Ph.D.Director of Informatics - JCVI

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

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.

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

Big DataBIG DATA

Data Science at NIH

Big Data 3 V’s

Variety

No VarietyA/Boston/26/2008

A/Canada-M

B/RV2018/2009

A/Canada-S

K/RV1767/2009

A/Canada-M

B/RV1975/2009

A/Mexico/InD

RE13495/2009

A/Lyon/969/2009

A/M

ichigan/30/2009A/Kanagaw

a/140/2009A/Taiw

an/90262/2011A/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/Helsinki/490/2013

A/Helsinki/753/2013

A/Helsinki/979/2013





A/England/201/2009




A/Hangzhou/04/2009

A/Pennsylvania/09/2009



A/Hangzhou/06/2009

A/Hangzhou/10/2009

A/Fukuoka-C

/3/2009A/Fukuoka-C

/2/2009A/Fukuoka-C

/1/2009A/Kagoshim

a/1/2009A/California/04/2009


0.01

Big Data

Volume + Variety = Value

Variety = Metadata

DMID Genomics

Courtesy of Alison Yao, DMID

www.viprbrc.org www.fludb.org

Bioinformatics Resource Centers (BRCs)

www.patricbrc.orgwww.eupathdb.org

www.vectorbase.org

IRD Data Summarywww.fludb.org

Virus Pathogen Resource

www.viprbrc.org

Database Query

Analysis Tools

Personal Workbench

Identification of diagnostic regions of Zika and other human flaviviruses

Alex Lee

Zika virus background

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?

Genome sequence selection

Mature peptide

prediction

Meta-CATS comparative

analysis

Sensitivity & specificity calculation

Diagnostic peptide region determination

Epitope and structure

prediction

Species X vs other species within the genusZIKV vs DENV1, 2, 3, 4, YFV, WNV, SLEV, JEV

Identification of diagnostic peptide regions

Flavivirus protein sequences

NS1 diagnostic sites

E diagnostic sites

Diagnostic peptide regions for ZIKV

Diagnostic peptide regions for all flaviviruses

Group 1 Count Group 2 CountNo. significant sites

(512 total sites)aNo. candidate

diagnostic sitesbNo. 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 5305 381 34 55 45

Dengue Virus 2(DENV2) 1,000 All Other 5302 379 59 143 94



Ilheus Virus (ILHV) 4 All Other 6298 85 38 63 37

Japanese Enchephalitis Virus (JEV) 1,000 All Other 5302 391 48 79 57

St. Louis Encephalitis Virus (SLEV) 178 All Other 6124 355 60 152 93

West Nile Virus (WNV) 993 All Other 5309 379 46 100 59

Yellow Fever Virus (YFV) 162 All Other 6140 382 160 448 254

Zika Virus (ZIKV) 71 All Other 6231 303 82 217 129a 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 E proteins

Group 1 Count Group 2 CountNo. significant sites

(353 total sites)aNo. candidate

diagnostic sitesbNo. 15-mers with at least

3 diagnostic sites

No. 15-mers with at least 3 diagnostic residues and is

surface exposed





Ilheus Virus (ILHV) 6 All Other 4493 24 23 63 45

Japanese Enchephalitis Virus (JEV) 227 All Other 4272 217 45 112 57

St. Louis Encephalitis Virus (SLEV) 36 All Other 4463 173 50 132 66

West Nile Virus (WNV) 988 All Other 3511 248 35 57 42

Yellow Fever Virus (YFV) 72 All Other 4427 239 115 302 169

Zika Virus (ZIKV) 34 All Other 4465 175 76 233 131a 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%

Summary statistics for NS1 proteins

0

5

10

15

201 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

361

370

379

388

397

406

415

424

433

442

451

460

469

478

487

496

505

ZIKV E protein

ab c d

ef g

ab

c

d

e

f

g

Note: Highlighted bars correspond to 15-mers where ZIKV have at least 3 diagnostic residue and is surface exposed

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.

Identification of virulence determinants of Enterovirus D68 (EV-D68)

Yun Zhang

Enterovirus D68 2014 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)

• 103 paralysis cases of unknown etiology in the USo 4/10 paralyzed children in CO were EV-D68

positiveo 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

http://media.healthday.com/images/editorial/girlhospital_40286.jpg

http://www.cdc.gov/amd/images/aia-girl-breathing.jpg

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

US AFM cases

• Source - http://www.cdc.gov/acute-flaccid-myelitis/afm-surveillance.html

Enterovirus clinical symptoms

Most infections are asymptomatic

Polioviruses, types 1-3Paralysis (complete to slight muscle weakness)Aseptic meningitisUndifferentiated febrile illness, particularly during the summer

Coxsackieviruses, group A, types 1-24HerpanginaAcute lymphatic or nodular pharyngitisAseptic meningitisParalysisExanthemaHand-foot-and-mouth disease (A10, A16)Pneumonitis of infants"Common cold"HepatitisInfantile diarrheaAcute hemorrhagic conjunctivitis (type A24 variant)

Coxsackieviruses, group BPleurodyniaAseptic meningitisParalysis (infrequently)Severe systemic infection in infants, meningoencephalitis, and myocarditisPericarditis, myocarditisUpper respiratory illness and pneumoniaRashHepatitisUndifferentiated febrile illness

Echoviruses, types 1-33Aseptic meningintisParalysisEncephalitis, ataxia, or Guillain-Barre syndromeExanthemaRespiratory diseaseOthers: DiarrheaPericarditis and myocarditisHepatic disturbance

Enterovirus, types 68-71Pneumonia and bronchiolitisAcute hemorrhagic conjunctivitis (type 70)Paralysis (types 70, 71)Meningoencephalitis(types 70, 71)Hand-foot-and-mouth disease (type 71 )

Fields Virology, 2007

Enterovirus Genome

Non-enveloped+ssRNA

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

Phylogeny-trait

association with BaTS

Meta-CATS, sensitivity & specificity calculation

Comparison with other paralysis-causing

enteroviruses

Functional prediction

EV-D68VP1

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 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 & Evolution8:239-46 BaTS

Bayesian Tip Significance (BaTS)

• 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

BaTS Algorithm

BaTS results

Candidate genetic determinants

Multiple sequence alignments

D68 Fermon

D68:A CA/RESP/10_786 US/KY/14_18953 EVD68/Homo_sapiens/USA/N0051U5/2012

D68:C 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

PV Human_poliovirus_1_Mahoney * PV2/Bel_2 * P3/Leon/37 *

A71 AFP2001064/EV71/GX/CHN/2001_71 * AFP2001071/EV71/GX/CHN/2001_71 *

5’UTR/127T 5’UTR/188A 5’UTR/262C 5’UTR/280C 5’UTR/339T 5’UTR/496G 5’UTR/669C

2C/1G 2C/102V 3D/135S 3D/274K2C/273G 3D/345Q2C/34T

VP2/222T VP3/24A VP1/98A VP1/290S VP1/308N 2A/66N5’UTR/697C

D68 Fermon


D68:C JPOC10_290 JPOC10_378


D68:B2 US/IL/14_18952 NY73

D70 J670/71



D68 Fermon


D68:C JPOC10_290 JPOC10_378


D68:B2 US/IL/14_18952 NY73

D70 J670/71



D68 Fermon


D68:C JPOC10_290 JPOC10_378


D68:B2 US/IL/14_18952 NY73

D70 J670/71




2C/1G 2C/102V 3D/135S 3D/274K2C/273G 3D/345Q2C/34T


D68 Fermon


D68:C JPOC10_290 JPOC10_378


D68:B2 US/IL/14_18952 NY73

D70 J670/71



D68 Fermon


D68:C JPOC10_290 JPOC10_378


D68:B2 US/IL/14_18952 NY73

D70 J670/71



D68 Fermon


D68:C JPOC10_290 JPOC10_378


D68:B2 US/IL/14_18952 NY73

D70 J670/71




2C/1G 2C/102V 3D/135S 3D/274K2C/273G 3D/345Q2C/34T


D68 Fermon


D68:C JPOC10_290 JPOC10_378


D68:B2 US/IL/14_18952 NY73

D70 J670/71



D68 Fermon


D68:C JPOC10_290 JPOC10_378


D68:B2 US/IL/14_18952 NY73

D70 J670/71



Capsid heterotrimer structure

IRES structure

Coding region

I II III

IV

V

VI

88 124 162 186 220 234 440

448 556

561 625

Variable region

743

Pyrimidine- rich region

GNRA sequence

IRES

262C 280C 339T

188A (185)

127U (123)

496G

CA/AFP/11-1767|2013|USA

CHN/CQ2860/2012|NA|China

2011-21186|11/15/2011|China

2013-0720-6|07/20/2013|China

2011-21282|12/19/2011|China

CA/AFP/v12T04950|2013|USA


US/CO/13-60-EV-D68|11/2013|USA

EVD68/Homo sapiens/USA/MO41/2014-D68|2014|USA

EV68_Alberta4693_2014-D68|08/31/2014|Canada





EVD68/Homo sapiens/USA/MO35/2014-D68|2014|USAITA/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/14-86|08/2014|USA

NY329|09/26/2014|USA

EV68/Ontario/C818712/2014|09/15/2014|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


CAE1428-FRA14|11/25/2014|France

NY210|10/14/2014|USAEVD68/Homo sapiens/USA/MO58/2014-D68|2014|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



EV-D68/Haiti/1/2014|12/01/2014|Haiti

NY153|10/02/2014|USA

CAE1125-FRA14|10/16/2014|France

LYO1245353-FRA14|11/20/2014|France

LYO014118426101_FRA14|11/12/2014|France

LYO118426101-FRA14|11/12/2014|France

EV-D68/environment/Gainesville/1/2015|09/08/2015|USA

NY316|09/24/2014|USA

NY328|09/26/2014|USA


EVD68/Homo sapiens/USA/MO18/2014-D68|2014|USAEVD68/Homo sapiens/USA/MO3/2014-D68|2014|USA

US/CA/14-R1|09/2014|USA









NY309|09/23/2014|USA

US/CA/14-6089|08/2014|USA

US/CA/14-6092-EV-D68|08/2014|USA

US/MO/14-18949-EV-D68|08/2014|USA



AMI030176_FRA14-nterovirus D68|10/2014|France


US/CO/14-94-EV-D68|09/2014|USA



EVD68/Homo sapiens/USA/MO1/2014-D68|2014|USAEVD68/Homo sapiens/USA/MO17/2014-D68|2014|USA







VERS339130_FRA14-nterovirus D68|10/2014|France


US/CO/14-93|09/2014|USA


US/CA/14-6100-EV-D68|10/2014|USA

US/CA/14-6103SIB-EV-D68|10/2014|USA






US/MO/14-18950-EV-D68|08/2014|USA


LYO1415548-FRA14|12/12/2014|France

NY275|09/19/2014|USA

NY126|09/28/2014|USAEVD68/Homo sapiens/USA/MO11/2014-D68|2014|USA

EV-D68_STL_2014_12|2014|USA

US/MO/14-18947-EV-D68|08/2014|USA






CF311065_FRA14|11/2014|France

CF287062_FRA14|10/2014|France

US/CA/14-6067|08/2014|USA



NY130|09/30/2014|USA

NY120|09/28/2014|USA

MEX/DGO/2014-InDRE2271|10/16/2014|Mexico





EV68_Alberta17390_2014-D68|08/18/2014|Canada


EVD68/Homo sapiens/USA/MO34/2014-D68|2014|USAUS/MO/14-18948-EV-D68|08/2014|USA


EV68/Ontario/C818710/2014|09/15/2014|Canada

NY326|09/25/2014|USA

NY263|09/18/2014|USA

US/CA/14-R2|09/2014|USA

MEX/DF/2014-InDRE2351|10/23/2014|Mexico

NY124|09/29/2014|USA

NY314|09/24/2014|USA

CHN/CQ7170/2014|09/18/2014|China

CHN/CQ5571/2013|10/16/2013|China

CHN/CQ7208/2014|09/22/2014|China

CHN/CQ7280/2014|10/13/2014|China

CHN/CQ7233/2014|09/30/2014|China

2014-R970|10/15/2014|China

CHN/CQ7349/2014|10/22/2014|China

CHN/CQ7174/2014|09/18/2014|China

2014-R0672|09/20/2014|China

CHN/CQ7188/2014|09/11/2014|China

2014-R1011|10/20/2014|China

2014-R1153|10/20/2014|China

EV68_Alberta2985_2014-D68|09/02/2014|CanadaCHN/CQ7214/2014|09/23/2014|China

CHN/CQ7226/2014|09/26/2014|China

CHN/CQ7225/2014|09/27/2014|China

2014-R1357|11/19/2014|China

Beijing-R0132|2014|China

CHN/CQ7283/2014|09/30/2014|China

CHN/CQ7307/2014|10/07/2014|China

CHN/CQ7360/2014|10/25/2014|China0.01

100

100

97

98

61

57

55

LEGENDYear- 2012

201320142015

* AFM + Encephalitis

*

*

**

*

*

*

*

+

***

0.01

Fermon|USA

Clade A

Clade C

Clade B

100

100

100

100

100

100

100

100B1

B2

US/KY/14-18953|08/2014|USA

CA/RESP/10-786|2013|USA

CA/RESP/09-817|2013|USA

EV-D68VP1

Nucleotide Tree

Hypothetical severity distribution

Paralysis in symptomatic:PV - ~3.6%D68 B1 - ~6.9%

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: 10.1093/ve/vew015.

• RPRC Innovation Project - Test affects of these substitutions on IRES function, replication efficiency, and receptor binding using synthetic genomics

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

Team

NIAIDHHSN272201400028C

J. Craig Venter InstituteRichard Scheuermann (PI)Brian AevermannDouglas GreerAlexander LeeLucy StewartYun ZhangBrian Reardon

Northrop GrummanMary Shaffran, Program ManagerEd Klem, Project ManagerZhiping GuSherry HeSanjeev KumarXiaomei LiJason LucasTom SmithBryan WaltersSam ZarembaHongtao Zhao

Univ. AucklandCatherine Macken, Co-PI

Univ. Calif. DavisNicole Baumgarth, Co-PI

Harvard Medical SchoolDavid Knipe, Co-PI

Univ. Texas Medical BranchSlobodan Paessler, Co-PI

Univ. GeorgiaDaniel Perez, Co-PI

Purdue Univ.Richard Kuhn, Co-PI

VecnaChris LarsenAl RamseyGuangyu Sun

Scientific Working GroupGillian Air, Univ. OklahomaRalph Baric, Univ. North CarolinaRuben Donis, CDCNaomi Forrester, UTMBAdolfo Garcia-Sastre, Mt SinaiElodie Ghedin, Univ. PittsburghElliot Lefkowitz, Univ. AlabamaPhil Pellett, Wayne State Univ.David Topham, Univ. RochesterRichard Webby, St Jude

NIAID / DMIDAlison Yao

Data providers

bioinformatics and comparative genomics applications · – bioinformatics: an interdisciplinary...

Documents