Real-time tracking and prediction of RNA virus evolution

Richard Neher
Biozentrum, University of Basel

slides at neherlab.org/201710_ASMNGS.html

Human seasonal influenza viruses

slide by Trevor Bedford

Influenza viruses evolve to avoid human immunity
Vaccines need frequent updates

nextflu.org

joint work with Trevor Bedford & his lab

code at github.com/blab/nextflu

Beyond tracking: can we predict?

Model of rapidly adapting virus populations

RN, Annual Reviews, 2013; Desai & Fisher; Brunet & Derrida; Kessler & Levine

Typical tree

Bolthausen-Sznitman Coalescent

RN, Hallatschek, PNAS, 2013; see also Brunet and Derrida, PRE, 2007; Desai, Walczak, Fisher, Genetics, 2013

Bursts in a tree ↔ high fitness genotypes

Predicting evolution

Given the branching pattern:

can we predict fitness?
pick the closest relative of the future?

RN, Russell, Shraiman, eLife, 2014

Fitness inference from trees

$$P(\mathbf{x}|T) = \frac{1}{Z(T)} p_0(x_0) \prod_{i=0}^{n_{int}} g(x_{i_1}, t_{i_1}| x_i, t_i)g(x_{i_2}, t_{i_2}| x_i, t_i)$$

RN, Russell, Shraiman, eLife, 2014

Prediction of the dominating H3N2 influenza strain

no influenza specific input
how can the model be improved? (see model by Luksza & Laessig)
what other context might this apply?

RN, Russell, Shraiman, eLife, 2014

Prediction of the dominating H3N2 influenza strain

nextstrain.org

joint work with Trevor Bedford & his lab

code at github.com/nextstrain

NextStrain architecture

Using treetime to rapidly compute timetrees

TreeTime: maximum likelihood phylodynamic analysis

Phylogenetic trees record history:

transmission
divergence times
population dynamics
ancestral geographic distribution/migrations

Typical approach: Bayesian parameter estimation

flexible
probabilistic → confidence intervals etc
but: computationally expensive

TreeTime by Pavel Sagulenko

probabilistic treatment of divergence times
dates trees with thousand sequences in a few minutes
linear time complexity
fixed tree topology
github.com/neherlab/treetime

West African Ebola virus outbreak

TreeTime: nuts and bolts

Attach sequences and dates

Reconstruct ancestral sequences

Propagate temporal constraints via convolutions

Integrate up-stream and down-stream constraints

Fit phylodynamic model → iterate

Molecular clock phylogenies of ~2000 A/H3N2 HA sequences -- a few minutes

What about bacteria?

vertical and horizontal transmission
genome rearrangements
much larger genomes
variation of divergence along the genome
NGS genomes tend to be fragmented
annotations of variable quality

panX by Wei Ding

pan-genome identification pipeline
phylogenetic analysis of each orthologous cluster
detect associations with phenotypes
fast: analyze hundreds of genomes in a few hours
github.com/neherlab/pan-genome-analysis

panX @ pangenome.de

S. pneumoniae data set by Croucher et al.

Pan-genome statistics and filters

Species trees and gene trees

Links between species trees and gene trees

Summary

Data set are growing rapidly
→ tools for interpretation and exploration are crucial
Breadth and depth
→ provide an overview, integrate, and go deep
Actionable outputs require (near)real-time analysis
→ fast analysis pipelines are essential
We are just scratching the surface...