1 nicholas mancuso department of computer science georgia state university joint work with bassam...

1

Nicholas MancusoDepartment of Computer Science

Georgia State University

Joint work with Bassam Tork, GSUPavel Skums, CDCIon Mӑndoiu, UConn Alex Zelikovsky, GSU

Viral Quasispecies Reconstruction from Amplicon 454 Pyrosequencing

Reads

CAME 2011, Atlanta Georgia

2

Viral Quasispecies and NGS• RNA Viruses

—HIV, HCV, SARS, Influenza—Higher (than DNA) mutation rates — quasispecies

—set of closely related variants rather than a single species

• Knowing quasispecies can help—Interferon HCV therapy effectiveness (Skums et al 2011)

• NGS allows to find individual quasispecies sequences—454 Life Sciences : 400-600 Mb with reads 300-800 bp long

• Sequencing is challenging—multiple quasispecies —qsps sequences are very similar

—different qsps may be indistinguishable for > 1kb (longer than reads)


3

Outline

• Shotgun vs Amplicon Sequencing• Viral Quasispecies Reconstruction Problem• Challenges and Approaches• Data Structure for Reads: Read Graph• Novel Methods for Solving QSR Problem• Observed vs True Read Frequencies• True Frequency Reconstruction• Simulations and Results


4

Shotgun versus Amplicon Sequencing

• Shotgun reads—starting positions

distributed uniformly

• Amplicon—each read has

predefined start/endcovering fixed overlappingwindows


5

Viral Quasispecies Spectrum Reconstruction Problem

• Given—collection of amplicon reads from a quasispecies

population with unknown variants and distribution

• Find—viral quasispecies sequences and their frequencies


6

Amplicon Sequencing Challenges• Collapse of quasispecies in amplicon

—distinct quasispecies may be indistinguishable in window• Collapse of quasispecies in overlap

—match reads from consecutive windows coming from the same qsp

• First approachProsperi et al (2011)—Guide Distribution

—choose a column—go right/left matching the

the closest in order neighbor


220 200 140 160 150200 140 130 150 14070 130 120 140 13010 20 110 130 1200 10 100 20 60

7

Approaches to QSP Reconstruction • Shotgun approaches

—estimates probability of consecutive reads coming from the same qsp (ViSpA, Astrovskaya et al 2011)

—parsimony (minimum number of distinct sequences covering all reads) (ShoRAH, Zagordi et al 2010)

• Why not use shotgun approaches for amplicons?—estimating probability in ViSpA relies on uniform distribution of reads—amplicon reads have fixed beginnings and ends

• Optimization approach—most parsimonious solution

— minimize number of distinct sequences covering all reads — too coarse: many different optimal solutions

—minimum information entropy (Shannon, 1948)— takes in account also frequency— fractional relaxation of pure parsimony


Min Entropy vs Parsimony• Parsimony and Min Entropy selects AC and BD if a = c, and b = d

8 CAME 2011, Atlanta Georgia

9

Data Structure for Reads: Read GraphK amplicons → K-staged read graph

—vertices → distinct reads—edges → reads with consistent overlap—vertices, edges have a count function


10

Read Graph• May transform graph into a 'forked' graph

—overlap is represented by fork vertex


11

Fork Resolving Problem• Minimum Entropy is NP-hard

—can solve it optimally for each small fork separately (future work)

• Greedy heuristic— ≤ a+b-1 are sufficient when resolving fork with a distinct reads on the

left and b on the right— that can be done greedily matching largest (greedy heuristic)— this does not guarantee minimum number of distinct qsps

• Better way = globally match the most frequent reads (max bandwidth)— find s-t path maximizing minimum read count— subtract the minimum count from each read in the path

— exhausts at least one read in the path


12

Greedy Method


13

Greedy Method


14

Greedy Method


15

Greedy Method


16

Greedy Method


17

Greedy Method


18

Greedy Method


19

Greedy Method


20

Greedy Method


21

Maximum Bandwidth Method


22



23



24



25



26



27



28



29



30



31



32



33



34



35



36



37



38

Observed vs Ideal Read Frequencies• Ideal frequency

—consistent frequency across forks

• Observed frequency (count)—inconsistent frequency across forks

• All methods perform better under ideal frequencies


39

Fork Balancing Problem

• Given—set of reads and respective frequencies

• Find—minimal frequency offsets balancing all forks

Simplest approach is to scale frequencies from left to right


40

Least Squares Approach

• Quadratic Program for read offsets• q – fork, oi – observed frequency, xi – frequency offset


41

Flowchart


42

Data Sets and Metrics• Simulated error-free HCV (1734 long fragment)– quasispecies from uniform, geometric, and skewed distribution– shift → delta of starting position

• Sensitivity– percentage of correctly assembled true quasispecies

• PPV– percentage of true quasispecies among all assembled

• Jensen-Shannon Divergence

43

Sensitivity Results


44

PPV Results


45

Divergence Results


46

ViSpA Comparison


47

Conclusion

• Two novel methods for solving QSR problem—Outperform Prosperi et al. on average—Outperform ViSpA approach on average

• Maximum Bandwidth approach worked best

• Future work: exact local solution for minimum entropy


48

Thanks


1 nicholas mancuso department of computer science georgia state university joint work with bassam...

Documents

atlanta georgia slide

amplicon distinct quasispecies

reads shorah

viral quasispecies sequences

quasispecies population

graph vertices distinct

amplicon sequencing

amplicon sequencing