Anisimova, Maria

ZHAW digitalcollection

7Newsletter TRANSFER | 3-2014 | www.lsfm.zhaw.ch
Dr. Maria Anisimova,  
Head of Applied Computational Genomics, 
maria.anisimova@zhaw.ch
Institut für Angewandte Simulation / Institute of Applied Simulations
Forschungsgruppe Predictive and Bio-Inspired Modelling
Joint alignment and phylogeny for large genomic data
The evolutionary history of molecules is 
described by a tree called phylogeny, es­
timated from genomic sequences. Phy­
logenies are used for testing biological 
hypotheses with applications from medi­
cine to ecology. Yet, phylogeny inference 
relies on an inferred alignment of homol­
ogous sequences, which – in turn – relies 
on a guide­tree reflecting their ancestral 
relationships. The aim is to address this 
apparent circularity so to improve the 
reliability of phylogenetic analyses. 
With the advent of new generation sequencing 
(NGS) bioinformatic methods must keep pace 
to provide robust scalable solutions to analyse 
large data. Usually the phylogenetic inference 
is simplified into two independent steps: align­
ment inference and tree inference (Fig. 1). Since 
the two steps are interdependent, errors 
committed at each step affect the reliability of 
the other, and are propagated to downstream 
analy ses. Ideally alignment and tree should be 
inferred jointly. Existing joint alignment­tree 
inference (JATI) algorithms use the Bayesian 
paradigm and rely on the classic evolutionary 
model of sequence changes based on an 
infinite­state continuous­time birth­death pro­
cess. While useful for small datasets, these 
methods do not scale to large modern­day 
data due to the exponential time complexity of 
the model and the need for intensive sampling 
of multiple parameters including unconven­
tional ones – alignment and tree.
Development of a new algorithm
To circumvent these problems, we will develop 
a new JATI algorithm in the maximum likelihood 
(ML) framework, building upon our methods
implemented in independent packages: Codon
the estimation of statistical support of inferred 
tree partitions and the ancestral reconstruction 
of molecular history. Our approach will enable 
analyses of huge genomic datasets. For exam­
ple, we have recently applied phylogenetic 
methods to study large NGS datasets of matu­
rating antibody sequences. The work was in 
collaboration with industry (MAB Discovery, 
Germany); the report is now in press in Mol Biol 
Evol. We expect that the new JATI method will 
be in demand not only in academic projects 
but also in pharma and biotech industry. 
PhyML for fast ML phylogeny inference for pro­
tein­coding genes, and ProGraphMSA for fast 
probabilistic graph­based alignment. Using the 
Poisson process to model indels will help to 
reduce the time complexity to linear. The arse­
nal of models in CodonPhyML (Fig. 2) will 
improve accuracy, eg, by describing the struc­
ture of the genetic code and selection for the 
protein­coding genes. Further, ProGraphMSA 
provides one of the fastest alignment heuristics, 
accounts for sequence divergence, correctly 
penalizes insertions and deletions, and is the 
only alignment method that includes sequence 
content heterogeneity, alternative splicing and 
repeats.
Analysing huge genomic datasets
Combining these features in one algorithm will 
result in vastly more efficient heuristics than the 
currently available to search the alignment­tree 
space. The new methodology will also allow 
Research project
Fast joint estimation of alignment and phylogeny from genomic sequences in a 
frequentist framework
Lead: Dr. Maria Anisimova
Duration: 3 years
Partner: University of Zurich
Funding: Swiss National Science Foundation
Project budget: CHF 454 000
Fig. 1: The dependency between alignment and phylogeny estimation calls for their simultaneous inference 
Fig. 2: A colour­coded matrix showing comparisons of 
28 substitution models and correspondent phylogenies 
for a one­gene multiple sequence alignment, as com­
puted by CodonPhyML: http://sourceforge.net/projects/
codonphyml


Joint alignmnet and phylogeny for large genomics data

ZHAW Zürcher Hochschule für Angewandte Wissenschaften

7Newsletter TRANSFER | 3-2014 | www.lsfm.zhaw.chDr. Maria Anisimova,  Head of Applied Computational Genomics, maria.anisimova@zhaw.chInstitut für Angewandte Simulation / Institute of Applied SimulationsForschungsgruppe Predictive and Bio-Inspired ModellingJoint alignment and phylogeny for large genomic dataThe evolutionary history of molecules is described by a tree called phylogeny, es­timated from genomic sequences. Phy­logenies are used for testing biological hypotheses with applications from medi­cine to ecology. Yet, phylogeny inference relies on an inferred alignment of homol­ogous sequences, which – in turn – relies on a guide­tree reflecting their ancestral relationships. The aim is to address this apparent circularity so to improve the reliability of phylogenetic analyses. With the advent of new generation sequencing (NGS) bioinformatic methods must keep pace to provide robust scalable solutions to analyse large data. Usually the phylogenetic inference is simplified into two independent steps: align­ment inference and tree inference (Fig. 1). Since the two steps are interdependent, errors committed at each step affect the reliability of the other, and are propagated to downstream analy ses. Ideally alignment and tree should be inferred jointly. Existing joint alignment­tree inference (JATI) algorithms use the Bayesian paradigm and rely on the classic evolutionary model of sequence changes based on an infinite­state continuous­time birth­death pro­cess. While useful for small datasets, these methods do not scale to large modern­day data due to the exponential time complexity of the model and the need for intensive sampling of multiple parameters including unconven­tional ones – alignment and tree.Development of a new algorithmTo circumvent these problems, we will develop a new JATI algorithm in the maximum likelihood (ML) framework, building upon our methodsimplemented in independent packages: Codonthe estimation of statistical support of inferred tree partitions and the ancestral reconstruction of molecular history. Our approach will enable analyses of huge genomic datasets. For exam­ple, we have recently applied phylogenetic methods to study large NGS datasets of matu­rating antibody sequences. The work was in collaboration with industry (MAB Discovery, Germany); the report is now in press in Mol Biol Evol. We expect that the new JATI method will be in demand not only in academic projects but also in pharma and biotech industry. PhyML for fast ML phylogeny inference for pro­tein­coding genes, and ProGraphMSA for fast probabilistic graph­based alignment. Using the Poisson process to model indels will help to reduce the time complexity to linear. The arse­nal of models in CodonPhyML (Fig. 2) will improve accuracy, eg, by describing the struc­ture of the genetic code and selection for the protein­coding genes. Further, ProGraphMSA provides one of the fastest alignment heuristics, accounts for sequence divergence, correctly penalizes insertions and deletions, and is the only alignment method that includes sequence content heterogeneity, alternative splicing and repeats.Analysing huge genomic datasetsCombining these features in one algorithm will result in vastly more efficient heuristics than the currently available to search the alignment­tree space. The new methodology will also allow Research projectFast joint estimation of alignment and phylogeny from genomic sequences in a frequentist frameworkLead: Dr. Maria AnisimovaDuration: 3 yearsPartner: University of ZurichFunding: Swiss National Science FoundationProject budget: CHF 454 000Fig. 1: The dependency between alignment and phylogeny estimation calls for their simultaneous inference Fig. 2: A colour­coded matrix showing comparisons of 28 substitution models and correspondent phylogenies for a one­gene multiple sequence alignment, as com­puted by CodonPhyML: http://sourceforge.net/projects/codonphyml

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Joint alignmnet and phylogeny for large genomics data

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