Research Focus – My research addresses three key evolutionary and ecological questions: 1) do larger effective population sizes improve the effectiveness of selection, 2) can ecological specialization promote larger effective population sizes, and 3) does improved selection efficacy increase the rate of adaptive evolution? The answers to these questions will produce a better understanding of the factors that facilitate adaptation and will also shed light on population divergence and the formation of new species. I predominately work in two systems, butterflies of the genus Papilio and the pathogenic, tick-vectored bacterium, Anaplasma phagocytophilum. Within Papilio, I am investigating the factors that differ between a host-generalist species, Papilio glaucus (the Eastern Tiger Swallowtail), and a host-specialist species, Papilio troilus (the Spicebush Swallowtail). These species are similar in most life history characteristics and geographic ranges, yet differ greatly in the number of larval host species they can feed upon. Within A. phagocytophilum, I have discovered that different strains vary greatly in the number of mammalian hosts they infect (host-range). I am now investigating what consequences this difference has for the evolutionary trajectory of this bacterium. In both systems, I utilized next-generation sequencing technologies and high performance computing to carry out comparative genomic analyses. These analyses allow me to assess sizes, gene flow and selection within populations and species. Other computational projects I am currently working on include thermal adaptation in clown fish using, population structure in South Africa elephants and population diversity in a rodent malaria species.
Biography - Matthew Aardema completed his B.S. in Zoology at Michigan State University (MSU) in 2008. He then went on to earn a M.S. degree at MSU in Ecology, Evolutionary Biology and Behavior. From there he went to Princeton University to earn his Ph.D. in the Department of Ecology and Evolutionary Biology under the guidance of Prof. Peter Andolfatto. Upon completion of his PhD, Matthew joined the American Museum of Natural History as a Gerstner Scholar in Bioinformatics and Computational Biology
Research Focus – The goal of my research is to broaden our understanding of how genomes change over time by developing tools and models to study the genomes of non-model organism systems and overlooked genome regions. The focus of my work is genome structure evolution, particularly regions where it has been historically difficult to map variation or even to assemble the DNA sequence, including: gene families, low-complexity sequences, subtelomeric regions, and small regions of extreme sequence divergence. Although this “dark matter” of genome structure is often the most difficult to work with, particularly in non-model organisms, it can yield the most interesting results relevant to phenotypic change and understanding evolutionary rates. Towards this end, I have been using malaria parasites as my study system because of the large number of open questions about multiple aspects of structural change in their relatively large, but low-complexity, genomes.
My recent work in comparative genomics is grounded in my earlier work in systematics and evolutionary genetics from my master’s research at the American Museum of Natural History and my PhD at Harvard University. The techniques of sequence alignment, tree building, and statistical models of evolution remain at the core of all my research. I have been able to use these methods to examine the evolution of low-complexity regions in coding sequence (DePristo and Zilversmit et al., 2006; Zilversmit et al. 2010), gene family evolution (Bethke et al. 2006; Ferreira, Zilversmit, and Wunderlich 2007; Zilversmit et al. 2013), and allelic and non-allelic homologous recombination (Zilversmit et al. 2010; Zilversmit et al. 2013). Analyzing all these phenomena both within and between species, in a phylogenetic framework for comparisons in the context of ancestry and evolutionary relationships, has allowed me to explore the neutral versus adaptive aspects of their evolution.
A large part of my research program now focuses on developing tools and technologies that allow for advances in comparative and evolutionary genomics. Recent developments in innovative and lower-cost sequencing technologies, and the wider use of parallel computer clusters, mean that scientists longer need to rely on the big sequencing centers for whole-genome level data, and from just a few model organisms. Since we can sequence, assemble, and annotate our own genomes, this can vastly increase the diversity of available genome data for comparative studies, which are at the core of evolutionary genomics. Currently, I am adapting both molecular and computational genomic methods to work with non-model organism systems to examine genome diversity and divergence using paired-end short read and long-read data. For this work, I have designed interworking genome analysis pipelines, using UNIX scripting and Python programs, for genome mapping (resequencing) with annotations, de novo genome assembly and annotation, and high-accuracy variant calling and in silico validation. Included in the mapping pipeline is a set of novel programs, one of which identifies regions of rapid evolutionary change that are not detectable by standard methods, and two others that show intersection and complement of variant data from multiple individuals. I have applied these techniques successfully to inter- and intraspecific studies, to identify loci associated with the evolution of drug resistance and increased disease virulence. In addition, I have been able to construct fine-scale genome maps of markers for population-level studies and quantitative trait locus analysis.
I am now applying these methods and pipelines to broader questions in malaria parasite evolution, including the origin of hemoglobin metabolism and thus the creation of the doorway to colonizing red blood cells as their ecological setting. My research also focuses on understanding other rapidly evolving gene families such as those that code for venom proteins.
Biography -Martine Zilversmit received her MS in Biology from NYU working with Rob DeSalle on high-throughput genome sequencing techniques for studies in molecular evolution and her PhD in Biology from Harvard University in the Department of Organismic and Evolutionary Biology working with Daniel Hartl on the genome structure evolution and population genetics of malaria parasites.
Research Focus — Evolutionary genetics has long been plagued by a paucity of data. A great body of mathematical theory in the form of population genetics was developed well before data became available to test most of its predictions. Now, with the maturation of next-generation sequencing (NGS) technologies over the past decade, many previously intractable questions are within reach. In particular, the sequencing of large numbers of individuals or else pooled samples consisting of many individuals (pooled NGS) allow allele frequencies to be measured with remarkable resolution, and important population genetic parameters to be estimated. My own research aims to develop computational tools (e.g., SNPGenie) for automating such analyses, allowing researchers to draw evolutionary inferences from NGS variant (primarily single nucleotide polymorphism, or SNP) data. So far, my efforts have mainly involved data from within-host virus populations, including both natural isolates (e.g., arteriviruses infecting red colobus monkeys) and serial infection experiments (e.g., influenza viruses infecting ferrets).
My research at the American Museum of Natural History focuses mainly on (1) within- and between-host viral evolution, especially of human papillomaviruses (HPVs); (2) determining orthology within a parsimony framework; and (3) the patterns of variation among and geographic distribution of human immune alleles. I rely heavily on my Sackler Institute for Comparative Genomics mentor, Apurva Narechania, in addition to generous collaborators at the National Cancer Institute and elsewhere. Our multidisciplinary work involves the collection of samples from natural populations around the world, genome sequencing, variant calling, bioinformatic processing, and evolutionary analysis. With respect to viral evolution, specific questions to be addressed include the host and viral genetic determinants of carcinogenicity in HPV, the influence of host vs. pathogen co-evolutionary history, and the importance of Muller’s Ratchet-like processes for viral fitness. Work on orthology aims to update and expand the BigPlant OrthologID tool to incorporate more eukaryotic genomes and non-protein-coding DNA information.
Finally, human immune allele research aims to detect correlations between spatial and genetic distance, as well as to identify specific variants important in evolutionary history and immunology. It is hoped that this work will elucidate methods for drawing upon quantitative theory to make concrete predictions that will have relevance for questions both therapeutic (e.g., immune epitope identification for vaccine design) and theoretical (e.g., the importance of mutation accumulation and the relative contributions of selection and drift in molecular evolution).
Biography — Chase W. Nelson received his B.A. in Biology from Oberlin College in 2010, where he performed honors research on mutation accumulation and gene expression in Arabidopsis thaliana under Angela J. Roles. During this time, he also undertook research experiences in unsupervised motif discovery at Ohio University and the molecular biology and phylogenetics of maize at the University of Wyoming. While he completed his Ph.D. in Biological Sciences at the University of South Carolina, studying evolutionary bioinformatics under Austin L. Hughes from 2011 to 2016, he also participated in next-generation sequencing research under Wen-Hsiung Li at Academia Sinica (中央研究院) in Taipei, Taiwan. In his free time, Nelson pursues vocal and dance studies, writing, and learning Mandarin.
Research Focus - Understanding what drives the distribution of species is fundamental to the study of ecology, taxonomy, and systematics. The physiology of a given plant species restricts its distribution to a specific set of climate, soils, and biotic interactions.
I am broadly interested in elucidating spatial patterns in global plant biodiversity through computational methods, particularly across temporal and climatic gradients. As a result, I work with large online databases of aggregated primary biodiversity data like the Global Biodiversity Information Facility (GBIF) to develop quantitative methods for the characterization of geographic distributions in terms of environmental factors, mainly climate. The unifying goal of my research is to be able to apply standard, quantitative methods to primary biodiversity data to reliably characterize environmental niches in a likelihood modeling framework for all extant taxa.
The main product of my Ph.D. work was a likelihood modeling framework that estimates climate parameters given a local vegetation community composition and distribution data for all of those species. The Climate Reconstruction Analysis using Coexistence Likelihood Estimation (CRACLE) framework has been extensively tested on modern vegetation surveys. Relative to other related methods, CRACLE is distinctly Gleasonian in its view of vegetation assembly. That is, all species are treated as independent units in the model when parameterizing climate niche occupancy. Now, CRACLE is being turned towards paleoclimate modeling using Late Quaternary plant macrofossil communities.
Biography - Rob completed his B.S. in Biology at Roanoke College in 2011. From there, he went on to Cornell University to work on Ph.D. in the School of Integrated Plant Science and the Section of Plant Biology with a focus on Plant Systematics and Evolution with Dr. Kevin C. Nixon. Upon completion of his Ph.D. in July 2016 he joined the American Museum of Natural History as a Gerstner Scholar in Bioinformatics and Computational Biology.