Anne-Mieke Vandamme

IFN-β induces greater antiproliferative and proapoptotic effects and increased p53 signaling compared with IFN-α in PBMCs of Adult T-cell Leukemia/Lymphoma patients

Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq, to AB, JVW and AMV “PVE”), PRONEX (CNPq-FAPESB to AB and JVW and LF), FWO (Grant: ZKC1280-00-W10 and G0D6817N to AMV)

HIV-1 Infection in Cyprus, the Eastern Mediterranean European Frontier: A Densely Sampled Transmission Dynamics Analysis from 1986 to 2012

Since HIV-1 treatment is increasingly considered an effective preventionstrategy, it is important to study local HIV-1 epidemics to formulate tailored preventionpolicies. The prevalence of HIV-1 in Cyprus was historically low until 2005. To investigatethe shift in epidemiological trends, we studied the transmission dynamics of HIV-1 in Cyprususing a densely sampled Cypriot HIV-1 transmission cohort that included 85 percent ofHIV-1-infected individuals linked to clinical care between 1986 and 2012 based on detailedclinical, epidemiological, behavioral and HIV-1 genetic information. Subtyping andtransmission cluster reconstruction were performed using maximum likelihood and Bayesianmethods, and the transmission chain network was linked to the clinical, epidemiological andbehavioral data. The results reveal that for the main HIV-1 subtype A1 and B sub-epidemics,young and drug-naïve HIV-1-infected individuals in Cyprus are driving the dynamics of thelocal HIV-1 epidemic. The results of this study provide a better understanding of thedynamics of the HIV-1 infection in Cyprus, which may impact the development of preventionstrategies. Furthermore, this methodology for analyzing densely sampled transmissiondynamics is applicable to other geographic regions to implement effective HIV-1 preventionstrategies in local settings.

A36 Prevalence of HIV-1 subtypes in Slovenia with an emphasis on molecular and phylogenetic investigation of subtype A

formatics pipeline to identify and classify all known viruses present in a metagenomic sample. Viral NGS reads are identified using a protein-based alignment method, DIAMOND, which is substantially faster than the standard BLAST method, and more reliable for viruses. These reads are automatically assembled into contigs using SPAdes, a de novo assembler. The contigs are then used to classify the virus at species level using a pan-viral typing tool based on all available taxonomic reference sequences from the International Committee on Taxonomy of Viruses (ICTV) database. This bioinformatics pipeline is Java-encoded and will include an easy-to-use web interface that is fit-for-purpose for researchers or clinicians. This tool can assemble viral contigs from paired-end reads generated by an Illumina MiSeq sequencer. So far 1865 viruses can be identified at species level resolution and 10 viruses (chikungunya virus, dengue virus, HBV, HCV, HHV8, HIV-1, HPV, HTLV-1, YFV, and Zika virus) at the genotype level. A web version of the panviral typing tool is already available and a web version with extended NGS functionality is currently being evaluated. Eliminating the need for virus-specific laboratory techniques, or targeted sequence capture, means a virome can be profiled in the context of its non-viral microbiome. Preliminary findings suggest our tool offers greater functionality than existing alternatives, with greater sensitivity to known viruses (including bacteriophages), automatic assembly and good quality phylogenetic analyses. A systematic comparison is underway.

The Phylogenetic Handbook: List of contributors

Part I. Introduction: 1. Basic concepts of molecular evolution Anne-Mieke Vandamme Part II. Data Preparation: 2. Sequence databases and database searching Guy Bottu, Marc Van Ranst and Philippe Lemey 3. Multiple sequence alignment Des Higgins and Philippe Lemey Part III. Phylogenetic Inference: 4. Nucleotide substitution models Korbinian Strimmer, Arndt von Haeseler and Marco Salemi 5. Phylogenetic inference based on distance methods Yves Van de Peer and Marco Salemi 6. Phylogenetic inference using maximum likelihood methods Heiko A. Schmidt and Arndt von Haeseler 7. Bayesian phylogenetic analysis using MRBAYES Fredrik Ronquist, Paul van der Mark and John P. Huelsenbeck 8. Phylogeny inference based on parsimony and other methods using PAUP* David L. Swofford and Jack Sullivan 9. Phylogenetic analysis using protein sequences Fred R. Opperdoes and Philippe Lemey Part IV. Testing Models and Trees: 10. Selecting models of evolution David Posada 11. Molecular clock analysis Philippe Lemey and David Posada 12. Testing tree topologies Heiko Schmidt Part V. Molecular Adaptation: 13. Natural selection and adaptation of molecular sequences Oliver G. Pybus and Beth Shapiro 14. Estimating selection pressures on alignments of coding sequences Sergei L. Kosakovsky Pond, Art F. Y. Poon, and Simon D. W. Frost Part VI. Recombination: 15. Introduction to recombination detection Philippe Lemey and David Posada 16. Detecting and characterizing individual recombination events Mika Salminen and Darren Martin Part VII. Population Genetics: 17. The coalescent: population genetic inference using genealogies Allen Rodrigo 18. Bayesian evolutionary analysis by sampling trees Alexei Drummond and Andrew Rambaut 19. LAMARC: estimating population genetic parameters from molecular data Mary K. Kuhner Part VIII. Additional Topics: 20. Assessing substitution saturation with DAMBE Xuhua Xia and Philippe Lemey 21. Split networks: a tool for exploring complex evolutionary relationships in molecular data Vincent Moulton and Katharina T. Huber.What is the probability that Sweden will win next year’s world championships in ice hockey? If you’re a hockey fan, you probably already have a good idea, but even if you couldn’t care less about the game, a quick perusal of the world championship medalists for the last 15 years (Table 7.1) would allow you to make an educated guess. Clearly, Sweden is one of only a small number of teams that compete successfully for the medals. Let’s assume that all seven medalists the last 15 years have the same chance of winning, and that the probability of an outsider winning is negligible. Then the odds of Sweden winning would be 1:7 or 0.14. We can also calculate the frequency of Swedish victories in the past. Two gold medals in 15 years would give us the number 2:15 or 0.13, very close to the previous estimate. The exact probability is difficult to determine but most people would probably agree that it is likely to be in the vicinity of these estimates. You can use this information to make sensible decisions. If somebody offered you to bet on Sweden winning the world championships at the odds 1:10, for instance, you might not be interested because the return on the bet would be close to your estimate of the probability. However, if you were offered the odds 1:100, you might be tempted to go for it, wouldn’t you? As the available information changes, you are likely to change your assessment of the probabilities. Let’s assume, for instance, that the Swedish team made it to

The Phylogenetic Handbook: Index

Part I. Introduction: 1. Basic concepts of molecular evolution Anne-Mieke Vandamme Part II. Data Preparation: 2. Sequence databases and database searching Guy Bottu, Marc Van Ranst and Philippe Lemey 3. Multiple sequence alignment Des Higgins and Philippe Lemey Part III. Phylogenetic Inference: 4. Nucleotide substitution models Korbinian Strimmer, Arndt von Haeseler and Marco Salemi 5. Phylogenetic inference based on distance methods Yves Van de Peer and Marco Salemi 6. Phylogenetic inference using maximum likelihood methods Heiko A. Schmidt and Arndt von Haeseler 7. Bayesian phylogenetic analysis using MRBAYES Fredrik Ronquist, Paul van der Mark and John P. Huelsenbeck 8. Phylogeny inference based on parsimony and other methods using PAUP* David L. Swofford and Jack Sullivan 9. Phylogenetic analysis using protein sequences Fred R. Opperdoes and Philippe Lemey Part IV. Testing Models and Trees: 10. Selecting models of evolution David Posada 11. Molecular clock analysis Philippe Lemey and David Posada 12. Testing tree topologies Heiko Schmidt Part V. Molecular Adaptation: 13. Natural selection and adaptation of molecular sequences Oliver G. Pybus and Beth Shapiro 14. Estimating selection pressures on alignments of coding sequences Sergei L. Kosakovsky Pond, Art F. Y. Poon, and Simon D. W. Frost Part VI. Recombination: 15. Introduction to recombination detection Philippe Lemey and David Posada 16. Detecting and characterizing individual recombination events Mika Salminen and Darren Martin Part VII. Population Genetics: 17. The coalescent: population genetic inference using genealogies Allen Rodrigo 18. Bayesian evolutionary analysis by sampling trees Alexei Drummond and Andrew Rambaut 19. LAMARC: estimating population genetic parameters from molecular data Mary K. Kuhner Part VIII. Additional Topics: 20. Assessing substitution saturation with DAMBE Xuhua Xia and Philippe Lemey 21. Split networks: a tool for exploring complex evolutionary relationships in molecular data Vincent Moulton and Katharina T. Huber.What is the probability that Sweden will win next year’s world championships in ice hockey? If you’re a hockey fan, you probably already have a good idea, but even if you couldn’t care less about the game, a quick perusal of the world championship medalists for the last 15 years (Table 7.1) would allow you to make an educated guess. Clearly, Sweden is one of only a small number of teams that compete successfully for the medals. Let’s assume that all seven medalists the last 15 years have the same chance of winning, and that the probability of an outsider winning is negligible. Then the odds of Sweden winning would be 1:7 or 0.14. We can also calculate the frequency of Swedish victories in the past. Two gold medals in 15 years would give us the number 2:15 or 0.13, very close to the previous estimate. The exact probability is difficult to determine but most people would probably agree that it is likely to be in the vicinity of these estimates. You can use this information to make sensible decisions. If somebody offered you to bet on Sweden winning the world championships at the odds 1:10, for instance, you might not be interested because the return on the bet would be close to your estimate of the probability. However, if you were offered the odds 1:100, you might be tempted to go for it, wouldn’t you? As the available information changes, you are likely to change your assessment of the probabilities. Let’s assume, for instance, that the Swedish team made it to

The Phylogenetic Handbook: Molecular adaptation

Part I. Introduction: 1. Basic concepts of molecular evolution Anne-Mieke Vandamme Part II. Data Preparation: 2. Sequence databases and database searching Guy Bottu, Marc Van Ranst and Philippe Lemey 3. Multiple sequence alignment Des Higgins and Philippe Lemey Part III. Phylogenetic Inference: 4. Nucleotide substitution models Korbinian Strimmer, Arndt von Haeseler and Marco Salemi 5. Phylogenetic inference based on distance methods Yves Van de Peer and Marco Salemi 6. Phylogenetic inference using maximum likelihood methods Heiko A. Schmidt and Arndt von Haeseler 7. Bayesian phylogenetic analysis using MRBAYES Fredrik Ronquist, Paul van der Mark and John P. Huelsenbeck 8. Phylogeny inference based on parsimony and other methods using PAUP* David L. Swofford and Jack Sullivan 9. Phylogenetic analysis using protein sequences Fred R. Opperdoes and Philippe Lemey Part IV. Testing Models and Trees: 10. Selecting models of evolution David Posada 11. Molecular clock analysis Philippe Lemey and David Posada 12. Testing tree topologies Heiko Schmidt Part V. Molecular Adaptation: 13. Natural selection and adaptation of molecular sequences Oliver G. Pybus and Beth Shapiro 14. Estimating selection pressures on alignments of coding sequences Sergei L. Kosakovsky Pond, Art F. Y. Poon, and Simon D. W. Frost Part VI. Recombination: 15. Introduction to recombination detection Philippe Lemey and David Posada 16. Detecting and characterizing individual recombination events Mika Salminen and Darren Martin Part VII. Population Genetics: 17. The coalescent: population genetic inference using genealogies Allen Rodrigo 18. Bayesian evolutionary analysis by sampling trees Alexei Drummond and Andrew Rambaut 19. LAMARC: estimating population genetic parameters from molecular data Mary K. Kuhner Part VIII. Additional Topics: 20. Assessing substitution saturation with DAMBE Xuhua Xia and Philippe Lemey 21. Split networks: a tool for exploring complex evolutionary relationships in molecular data Vincent Moulton and Katharina T. Huber.What is the probability that Sweden will win next year’s world championships in ice hockey? If you’re a hockey fan, you probably already have a good idea, but even if you couldn’t care less about the game, a quick perusal of the world championship medalists for the last 15 years (Table 7.1) would allow you to make an educated guess. Clearly, Sweden is one of only a small number of teams that compete successfully for the medals. Let’s assume that all seven medalists the last 15 years have the same chance of winning, and that the probability of an outsider winning is negligible. Then the odds of Sweden winning would be 1:7 or 0.14. We can also calculate the frequency of Swedish victories in the past. Two gold medals in 15 years would give us the number 2:15 or 0.13, very close to the previous estimate. The exact probability is difficult to determine but most people would probably agree that it is likely to be in the vicinity of these estimates. You can use this information to make sensible decisions. If somebody offered you to bet on Sweden winning the world championships at the odds 1:10, for instance, you might not be interested because the return on the bet would be close to your estimate of the probability. However, if you were offered the odds 1:100, you might be tempted to go for it, wouldn’t you? As the available information changes, you are likely to change your assessment of the probabilities. Let’s assume, for instance, that the Swedish team made it to

The Phylogenetic Handbook: Additional topics

Part I. Introduction: 1. Basic concepts of molecular evolution Anne-Mieke Vandamme Part II. Data Preparation: 2. Sequence databases and database searching Guy Bottu, Marc Van Ranst and Philippe Lemey 3. Multiple sequence alignment Des Higgins and Philippe Lemey Part III. Phylogenetic Inference: 4. Nucleotide substitution models Korbinian Strimmer, Arndt von Haeseler and Marco Salemi 5. Phylogenetic inference based on distance methods Yves Van de Peer and Marco Salemi 6. Phylogenetic inference using maximum likelihood methods Heiko A. Schmidt and Arndt von Haeseler 7. Bayesian phylogenetic analysis using MRBAYES Fredrik Ronquist, Paul van der Mark and John P. Huelsenbeck 8. Phylogeny inference based on parsimony and other methods using PAUP* David L. Swofford and Jack Sullivan 9. Phylogenetic analysis using protein sequences Fred R. Opperdoes and Philippe Lemey Part IV. Testing Models and Trees: 10. Selecting models of evolution David Posada 11. Molecular clock analysis Philippe Lemey and David Posada 12. Testing tree topologies Heiko Schmidt Part V. Molecular Adaptation: 13. Natural selection and adaptation of molecular sequences Oliver G. Pybus and Beth Shapiro 14. Estimating selection pressures on alignments of coding sequences Sergei L. Kosakovsky Pond, Art F. Y. Poon, and Simon D. W. Frost Part VI. Recombination: 15. Introduction to recombination detection Philippe Lemey and David Posada 16. Detecting and characterizing individual recombination events Mika Salminen and Darren Martin Part VII. Population Genetics: 17. The coalescent: population genetic inference using genealogies Allen Rodrigo 18. Bayesian evolutionary analysis by sampling trees Alexei Drummond and Andrew Rambaut 19. LAMARC: estimating population genetic parameters from molecular data Mary K. Kuhner Part VIII. Additional Topics: 20. Assessing substitution saturation with DAMBE Xuhua Xia and Philippe Lemey 21. Split networks: a tool for exploring complex evolutionary relationships in molecular data Vincent Moulton and Katharina T. Huber.What is the probability that Sweden will win next year’s world championships in ice hockey? If you’re a hockey fan, you probably already have a good idea, but even if you couldn’t care less about the game, a quick perusal of the world championship medalists for the last 15 years (Table 7.1) would allow you to make an educated guess. Clearly, Sweden is one of only a small number of teams that compete successfully for the medals. Let’s assume that all seven medalists the last 15 years have the same chance of winning, and that the probability of an outsider winning is negligible. Then the odds of Sweden winning would be 1:7 or 0.14. We can also calculate the frequency of Swedish victories in the past. Two gold medals in 15 years would give us the number 2:15 or 0.13, very close to the previous estimate. The exact probability is difficult to determine but most people would probably agree that it is likely to be in the vicinity of these estimates. You can use this information to make sensible decisions. If somebody offered you to bet on Sweden winning the world championships at the odds 1:10, for instance, you might not be interested because the return on the bet would be close to your estimate of the probability. However, if you were offered the odds 1:100, you might be tempted to go for it, wouldn’t you? As the available information changes, you are likely to change your assessment of the probabilities. Let’s assume, for instance, that the Swedish team made it to