Mapping Influenza A Virus Transmission Networks with Whole Genome Comparisons (Methods) Adrienne...

Mapping Influenza A Virus Transmission Networks with

Whole Genome Comparisons(Methods)

Adrienne Breland

TTGTGGATTCTTGATCGTCTTTTCTTCAAATGTAT TTATCGTCGCCTTAAATACGGA

AAGAACCTTTATGACAAGGTTCGACTACA GCTTAGGGATAATGCAAAGGAGCTGGT

Goal

- to characterize global Influenza A Virus

transmission as a complex network





Russell (2008) The global circulation of seasonal influenza A (H3N2) viruses

Proposed global H3N2 circulation



• Motivation

• Major Questions

• Data

• Genome Comparison Method



Outline

• Motivation

• Major Questions

• Data




Outline

Motivation

• Delineating real disease networks is difficult– Infection tracing: Detecting exact

transmission links– Contact tracing: All potential

transmission contacts– Diary Based: Subject records all contacts



Motivation



Infection tracing Contact tracing Diary Based

Keeling M & K Eames (2005) Networks and epidemic models. J. R. Soc. Interface 2:295-307

Motivation

• Delineating real disease networks is very useful



Motivation


-targeting an attack



Motivation



AAGAACCTTTATGACAAGGTTCGACTACA GCTTAGGGATAATGCAAAGGAGCTGGTError and attack tolerance of complex networks. Réka Albert, Hawoong Jeong and Albert-László Barabási

Motivation



AAGAACCTTTATGACAAGGTTCGACTACA GCTTAGGGATAATGCAAAGGAGCTGGThttp://prblog.typepad.com/strategic_public_relation/images/2007/06/22/simple_social_network.png

Motivation


-correlation coefficients

ji,ijji

ii

ABD [DB] BD typeof pairs

D [D] D typeof singles

correlatednot B and D if 1

[A][B]n

N[DB]CDB

Motivation


-detecting more probable global routes



Motivation

• Global routes



Motivation

• Global routes



Breland A, S Nasser, K Schlauch, M Nicolescu, F Harris (2008) Efficient Influenza A Virus Origin Detection. Journal of Electronics and Computer Science, 10;1-12

Motivation


-examine with other spatial data



Motivation

• Spatial data



Motivation

• Spatial data



VEGETATION

Motivation

• Spatial data



POPULATION

Motivation

• Spatial data



CLIMATE CHANGE

• Motivation

• Major Questions

• Data




Outline

Major questions

• Location and degree of host jumping

• Underlying structure (small world, power law..)

• Subtype independence

• Re-assortment

• Geographic routes



• Motivation

• Major Questions

• Data




Outline

Data

• http://www.ncbi.nlm.nih.gov/genomes/FLU/FLU.html



Data

• ≈ 4000 sequences• 1999-2009• Global regions (i.e. China, U.S., Africa, India...)• All subtypes (i.e. H5N1, H1N1, ..)• All hosts species (Domestic Avian, Wild Avian, etc..)



Data

• ≈ 374 per year



Data

• Multiple host types



Data

• Multiple sub types



• Motivation

• Major Questions

• Data




Outline

Genome Comparisons

• Similarity matrix, N sequences:

N(N-1)/2 comparisons



-0.40.10.970.10.82N

--0.30.60.70.9.

---0.30.50.02.

----0.020.01.

-----0.932

------1

N. .. 21

- 1 N

-- 1.

--- .

---- .

-----12

------1

N. .. 21

Romanova,J (2006) The fight against new types of influenza virus. Biotechnology J,1:1381-1392

Genome Comparisons

• 8 segments



- 1 N

-- 1.

--- .

---- .

-----12

------1

N. .. 21

- 1 N

-- 1.

--- .

---- .

-----12

------1

N. .. 21

- 1 N

-- 1.

--- .

---- .

-----12

------1

N. .. 21

- 1 N

-- 1.

--- .

---- .

-----12

------1

N. .. 21

- 1 N

-- 1.

--- .

---- .

-----12

------1

N. .. 21

- 1 N

-- 1.

--- .

---- .

-----12

------1

N. .. 21

- 1 N

-- 1.

--- .

---- .

-----12

------1

N. .. 21

- 1 N

-- 1.

--- .

---- .

-----12

------1

N. .. 21

HA ≈ 1750bp

NS ≈ 900bp

M ≈ 1000bp NA ≈ 1300bp NP ≈ 1500bp

PA ≈ 2100bp PB1 ≈ 2200bp PB2 ≈ 2300bp

Genome Comparisons

• 8 segments



Genome Comparisons



• Alignment, O(n2), n = max sequence length

.....AAAACTTGAACC.....

.....GGACTTGACCT.....

Genome Comparisons



• Alignment-free k-mers, O(n)

∑ = {A,C,G,T/U}

4k possible k-mers, k≥0

TT

TG

.

.

.

AG

AC

AA

frequencyk-word

Genome Comparisons


• Feature Frequency Profiles (FFP)

Ck = <c1,...,c4k>

Fk = <c1/∑,...,c4k/∑> = <f1,...,f4k>

Sims GE, Jun SR, Wu GA, Kim SH (2009) Alignment-free genome comparison with feature frequency profiles (FFP) and optimal resolutions. Proc Natl Acad Sci U S A. ,106(8):2677-82 .

Genome Comparisons

• Jensen-Shannon Divergence (JS)

compare(s1,s2)


Pk = FFP(s1), Qk = FFP(s2), Mk = (Pk + Mk)/2

JS(Pk,Mk) = 1/2KL(Pk,Mk) + 1/2KL(Qk,Mk)

KL =

k

i ik

ikik m

pp

4

1 ,

,2,

log

Genome Comparisons


• k=?

Genome Comparisons


• k=?

k s.t. N(k) ≥ N(k+1)

k ≈ 4

Genome Comparisons


• Actual & Predicted times

• Questions/Comments?

• Thanks

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