Zurich Colloquium for Computational Molecular Evolution (2011)

One of the fundamental concepts behind Darwin's theory of evolution is “descent with modification”. From a common ancestor, populations and species diverge during the course of evolution, where natural selection is the main driving force. This process is traditionally envisioned through a phylogenetic tree. Since Darwin's ideas were published in his Origin of Species, statisticians, mathematicians, computer scientists, biologists, chemists and physicists have joined their forces developing both methodological and experimental aspects of the field.

Scope

The focus of this seminar series is on statistical and computational methodology to study how genomes of organisms evolve, the driving forces of change at both phenotypic and molecular levels. We aim to encourage and promote the innovation in statistical and computational approaches, and the interdisciplinary character of the field.

Organizers

Maria Anisimova and Christophe Dessimoz

Please contact one of us if you would like to join the mailing list for this colloquium

Funding

This seminar is funded by CBRG and the SIB.

Program

This is a preliminary program. It is still subject to change since we do not have confirmation from all speakers yet. Only the dates in bold have already been confirmed.

Previous years

The list of speakers/abstracts for previous years are available here: 2010, 2009, 2008, 2007.

Abstracts

Construction and exploitation of sequence alignments in modern evolutionary biology

Julie Thompson, IGBMC Strasbourg, France

Recent advances in high-throughput experimental techniques in biology are producing huge quantities of data that are complex, heterogeneous and noisy; but this accumulation of data is only a preliminary step towards understanding the fundamental principles and mechanisms of living organisms. Evolutionary approaches provide a unique conceptual framework for performing comparative analyses of genomic data and for studying complex biological systems. In this context, high-quality multiple sequence alignments (MSAs) are an essential prerequisite for reconstructing the detailed evolutionary histories of genes and genomes. I present a number of formalisms and tools that we are developing for the efficient construction and exploitation of the MSAs for large-scale evolutionary analyses. Example applications show how the resulting computational infrastructure can be used to study the behaviour and evolution of systems, such as macromolecular complexes or biological networks (metabolic, transcriptional, interaction as well as developmental and disease-related networks).

Deciphering the swine flu pandemics of 1918 and 2009

Richard Goldstein
National Institute for Medical Research, London, UK

The devastating Spanish flu of 1918 killed an estimated 50 million people worldwide, ranking it as the deadliest pandemic in recorded human history. It is generally believed that the virus transferred from birds directly to humans shortly before the start of the pandemic, subsequently jumping from humans to swine. A different picture emerges when we model how the viral sequences changes in human, avian, and swine hosts. These analyses show it that the Spanish flu of 1918, like the 2009 pandemic, was likely a 'swine-origin' influenza virus, having smouldered in swine for years prior to 1918. We can identify locations that seem to be under different selective constraints in humans and bird viruses, characterise the adaptation of the virus to the new host, and identify changes that may have facilitated the establishment of the 2009 swine-origin flu in humans.

Species delimitation using genomic sequence data

Ziheng Yang
Galton Laboratory, University College London

Traditionally, species have been delimited using morphological and ecological traits.  However, molecular genetic data can provide additional useful information about many processes related to species delimitation, such as species divergence, gene flow and hybridization.  Indeed multilocus sequence data can provide support for different species delimitations.  I will discuss a recent Bayesian method for species delimitation (Yang & Rannala 2010 PNAS 107:9264-9269), which uses the multi-species coalescent model to analyze genomic data to calculate Bayesian posterior probabilities for different species-delimitation models.  The method accommodates uncertainties in gene trees due to errors in phylogenetic inference or to lineage sorting.  I will also discuss some simulation results that examine the performance of the method, especially in presence of gene flow.

Reconstructing population histories from molecular marker data

Dr. Jukka Corander
University of Helsinki, Department of Mathematics and Statistics

We introduced recently a Bayesian model for reconstructing population histories from SNP data (Sirén et al. 2011, MBE). An approximation to the neutral Wright-Fisher diffusion was used to model random fluctuations in allele frequencies over time. The population histories were modelled as binary rooted trees that represent the historical order of divergence of the different populations. A combination of analytical, numerical and Monte Carlo integration techniques were utilized for the inferences. We will go through the details of this model and also present results from ongoing work where a generalization of the model to multiallelic data is developed, together with applications to inferring evolution of bacterial populations.

The evolution of mammalian tissue transcriptomes

Dr. Henrik Kaessmann
University of Lausanne and Swiss Bioinfomratics Institute

Evolutionary changes in gene regulation have long been thought to underlie most phenotypic differences between species. However, evolutionary analyses of gene expression variation were long hampered by the limitations of microarrays for between-species comparisons. The recent development of RNA sequencing now affords essentially unbiased transcriptome comparisons at unprecedented resolution. We have generated detailed qualitative and quantitative transcriptome data using next generation sequencing technologies for a unique collection of germline and somatic tissues from representatives of all major mammalian lineages: placental mammals (humans, apes, monkeys, rodents), marsupials (gray short-tailed opossum), and the egg–laying monotremes (platypus). Our comparative and evolutionary analyses of these unprecedented data provide detailed insights into patterns of gene expression change in mammals and the underlying evolutionary forces. The data also allow us to explore, for the first time, the relevance of observed gene expression changes for the evolution of mammalian phenotypes.

Evolutionary plasticity of cell-cycle regulation

Dr. Lars Juhl Jensen

Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Denmark

The regulation of the eukaryotic cell cycle has been a topic of intense research for decades, and it is of both fundamental scientific and medical importance. In recent years, microarrays have been utilized to simultaneously monitor the expression of all genes during the mitotic cell cycle of humans, budding yeast, fission yeast, and the plant Arabidopsis thaliana. Reanalysis the available microarray data for each species, which combined with sequence-based orthology detection, provided insight into the evolution of cell-cycle regulation. Surprisingly, the transcriptional regulation of cell-cycle genes is very poorly conserved between orthologous genes. By mapping the expression data onto protein interaction networks and well-described protein complexes, we discovered that the assembly, and hence the activity, of protein complexes is typically controlled through only a few subunits. We were able to show that although the identity of the periodically expressed subunits of a given complex varies greatly between species, the regulated subunits in each species are expressed shortly before the complex is known to act. Moreover, comparing the results from microarray expression studies to sets of substrates of cyclin-dependent kinases, which had either been shown experimentally or predicted based on sequence motifs, revealed that transcriptional and post-translational regulation has co-evolved independently in multiple lineages; the subunits that control the dynamic assembly of protein complexes during the cell cycle are thus tightly controlled at multiple levels. Our results indicate that many solutions have evolved for assembling the same molecular machines at the right time during the cell cycle, which raises the question of how fast regulation evolves and how closely related two organisms have to be for regulatory details to be transferable.

Effect of adding homologs in phylogenetic analysis

Dr. Kazutaka Katoh

Computational Biology Research Center, AIST, Japan

The inclusion of additional homologous sequences is generally believed to improve the accuracy of phylogenetic inference and multiple sequence alignment (MSA). Building upon a phylogeny-based benchmarking approach introduced recently, we quantitatively examined the validity of such analysis by performing an evaluation of MSA and phylogenetic tree inference.

In order to clarify the effects of homologs at the MSA step and at the tree inference step separately, two types of tests, (1) Enriched and (2) Impoverished, were performed.  In the (1) Enriched test, the entire MSA, containing additional homologs, was used to infer a tree. Its result reflects the total effect of homologs on the MSA step and the tree inference step.  In the (2) Impoverished test, the additional homologs were included in the MSA step but excluded from the tree inference step.  Its result is expected to reflect the effect of homologs specifically on the MSA calculation.  In addition, the effect of homologs specifically on the tree inference step was assessed by using (3) the difference between the results of Enriched and
Impoverished. 

We examined several combinations of different MSA methods and tree inference methods.  The results suggest that additional homologs do not improve the quality of MSA in general, but improve the resulting tree in most cases.  This benchmark also provides practical guidelines, for example, an appropriate similarity level of homologs to be included into a phylogenetic analysis.

QTL Mapping in the Evolutionary Context

Pjotr Prins

Laboratory of Nematology, Wageningen University and the Groningen Bioinformatics Cente, The Netherlands

QTL mapping, both with genome-wide association studies (GWAS) and linkage studies in experimental populations, is a proven statistical method for disentangling complex traits. Especially now, with the deluge of data and information generated by, for example, RNA-seq, a well designed QTL experiment promises a chance of gaining enough statistical power to say something about multi-gene regulation and interaction. Here we discuss elucidating host-pathogen protein-protein interactions through linkage studies, where evidence of positive selection pressure acts as a prior to QTL analysis.

From short reads to causal variants: analytical tools for Illumina short read data

Dr Gerton Lunter

The Wellcome Trust Centre for Human Genetics, University of Oxford, UK

In clinical settings the interest is in finding a novel or rare variant that is responsible for a disease.  With Illumina's HiSeq2000 sequencing machines, it is becoming possible to sequence whole genomes of patients, sometimes including their parents, to look these novel or rare variants.  However, since the genome is so large, this puts extreme demands on the analysis pipeline, both in terms of sensitivity and specificity.  In this talk I will describe a pair of tools, Stampy and Platypus, which were designed both for sensitivity and specificity, and to work well for SNPs and indels, both of which are important for disease.  If there is time, I will also include some work on the analysis of indels in the 1000 Genomes project, highlighting the biological importance of indels in shaping phenotypic variation.