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May 10, 2013
Practical DNA Training Program: A two-week (9 business days) intensive laboratory-based training program designed to teach participants the fundamentals of molecular techniques including DNA extraction, amplification (using PCR), sequencing and interpretation. This training program is offered at various times throughout the year and we will work with you to find a suitable time for training. Special Offer: Save the Tax! Save $455.00 by attending the Practical DNA Training Program June 17-27, 2013. For more information please contact us at 807-343-8877 or paleodna[at]lakeheadu.ca or visit our website www.ancientdna.com and click on 'Training Programs'. Thank you. Karen. -- Karen Maa Administrative Assistant Paleo-DNA Laboratory 1294 Balmoral Street, 3rd Floor Thunder Bay, Ontario P7B 5Z5 Telephone: 1-866-DNA-LABS or 1-807-343-8616 Karen Maa
University Assistant (= Research Associate/Senior Postdoc), 6 years in Evolutionary Modeling at the University of Vienna The mathematics and biosciences group (MaBS) at the University of Vienna is looking for a strong and highly motivated candidate for a university assistant position in evolutionary modeling. The research focus is flexible and includes work in population genetics or genomics, quantitative genetics, and evolutionary ecology. See the MaBS homepage (www.mabs.at) for further information on our research interests. In recent years, Vienna has developed into one of the leading centers in evolutionary biology (www.evolvienna.at). In addition to a stimulating scientific environment, Vienna also offers an extraordinarily high quality of life. Affordable housing, excellent public transport, great restaurants, a range of international schools, two operas, two music centers, many theaters and museums in combination with a pleasant climate make Vienna one of the most attractive cities in Europe. The successful candidate will have a record of high quality research in evolutionary modeling. S/he is expected to develop and maintain an independent research profile and to attract extramural funding. In addition to research, the candidate will contribute to teaching and supervise students. The position will be offered for 6 years and comes with a competitive salary. The starting date is October 2013 or later (negotiable). Formal requirement is a PhD and a strong background and interest in quantitative evolutionary research (analytical or computational modeling). Prior postdoc experience and the proven ability to attract funding are desirable. The working language is English, German skills are not essential. Applications should include: # Cover letter # CV with publication list and grants, # summary of past and future research plans, # teaching experience, # names and email addresses of three potential referees. Full applications (preferably as a single pdf) should be sent via the Job Center to the University of Vienna (http://jobcenter.univie.ac.at, email: jobcenter[at]univie.ac.at), with cc to Joachim Hermisson (joachim.hermisson[at]univie.ac.at) # no later than June 9th, 2013 # referenced to the identification number 3988. Informal inquiries (encouraged) should be sent to Joachim Hermisson. Joachim Hermisson Professor for Mathematics and Biosciences University of Vienna Department for Mathematics Nordbergstr. 15, 1090 Vienna, Austria and Max F.Perutz Laboratories Dr.-Bohrgasse 9, 1030 Vienna, Austria phone: +43 (0) 1 4277 50648 email: joachim.hermisson[at]univie.ac.at www.mabs.at joachim.hermisson[at]univie.ac.at
Post Doctoral Fellow - Population Modeling Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin PROJECT DESCRIPTION: The successful candidate will work on a NSF-funded project to develop a Bayesian population model that incorporates information on the spatial distribution of related individuals derived using genetic methods. The post-doc will be housed at Groningen University in the Netherlands, but will be a University of Wisconsin-Madison employee and collaborate with faculty at both universities. REQUIREMENTS: Applicants should have a doctoral degree in quantitative ecology, biostatistics, population genetics, or closely related discipline by the start date. A strong publishing record, programming experience (Python, Perl, and/or C), population genetics background, and population modeling skills are essential. SALARY AND CONDITIONS: The position will be available Sept 1, 2013 and the duration of the appointment is 13 months. Salary will be $44,000 per year plus benefits. APPLICATION/CONTACT INFORMATION: Applicants should send a cover letter, curriculum vitae, and contact information for three references in a single pdf-file to Dr. Zach Peery at mpeery[at]wisc.edu. The CV should contain a list of publications and information describing relevant skills and experience. Reviews of material will begin June 30, 2013 and continue until a suitable candidate is found. Zach Peery Graduate Research Assistant Department of Forest and Wildlife Ecology University of Wisconsin Russell Labs 1630 Linden Dr. Madison, WI 53706 rickaes[at]gmail.com
ConGen 2013 Population Genomic Data Analysis Course http://www.popgen.net/congen2013/ Recent Approaches for Estimation of Population Structure, Gene-flow, Landscape Genomics, Selection Detection, and the Analysis of Next-Gen Sequence Data. 2-7 September 2013 Flathead Lake Biological Station, Montana, USA http://www2.umt.edu/flbs/ Applications for 2013 edition are now open! Objective: To provide training in conceptual and practical aspects of data analysis for the conservation genomics of natural and managed populations. Emphasis will be on next generation sequence data analysis (RADs, exon capture, and whole genome sequence analyses) and interpretation of output from recent novel statistical approaches and software programs. The course also will allow daily discussions among young researchers (students/participants) and leaders in conservation genomics to help develop the "next generation" of conservation geneticists. We will identify and discuss developments needed to improve data analysis approaches. This course will cover analysis methods including the coalescent, Bayesian, approximate Bayesian, and likelihood-based approaches. Who should apply: Ph.D. students, post-docs, and population biologists with a background of at least one semester university-level course in population genetics and a course in population ecology. Applicants must have a basic background in population genetic data analysis, including testing for Hardy-Weinberg proportions and gametic disequilibrium. Participation will be limited to 25-30 people allowing efficient instruction with hands-on computer exercises during the course. Priority will be given to persons with their own data to analyze (for example graduate students at the end of their degree program). Course/Workshop Format: For each subject, we typically provide 30-45 minutes of background, theory, discussion and introduction to concepts. Immediately following, we will conduct data analyses together for 30-60 minutes using relevant software programs and real data sets. Evening hands-on computer sessions and lodging together of the instructors and students in the same location (the beautiful Flathead Lake field station) will allow for extensive exchange and facilitate learning. Cost (before July 1st): $US 1,300 - which includes all lodging, meals (& coffee breaks etc.), transportation (to/from airport and Glacier National Park), a field trip to Glacier Park, and power point slide shows of all lectures (as well as audio-visual recordings of each lecture, including "question & answering sessions"). USD $1,500 if payment after July 1st. Deadline for application is 1 July, 2013 More information on http://www.popgen.net/congen2013/ tiagoantao[at]gmail.com
THREE-YEAR POST-DOCTORAL FELLOWSHIP IN COMPARATIVE GENOMIC INSIGHTS INTO PARASITE GENOME FUNCTION Applications are invited for a three-year post-doctoral fellowship held jointly at the Sanger Institute and European Bioinformatics Institute under these institutes' "ESPOD" fellowship programme (http://www.ebi.ac.uk/research/postdocs/espods). The above webpage includes a link to an abstract of this project (and others in the programme). Full project details are available at http://www.ebi.ac.uk/sites/ebi.ac.uk/files/groups/research_office/Goldman-Berriman.pdf The successful candidate will work in the Goldman Group at EMBL-European Bioinformatics Institute (http://www.ebi.ac.uk/research/goldman) and Matt Berriman's Parasite Genomics group at the Sanger Institute (http://www.sanger.ac.uk/research/projects/parasitegenomics). Applications should be sumitted by e-mail to the EBI Research Office by 26 July 2013. ----------------------------------------------------------------------- Nick Goldman tel: +44-(0)1223-492530 EMBL - European Bioinformatics Institute fax: +44-(0)1223-494468 Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
May 9, 2013
Background: Few studies on eurypterids have taken into account morphological changes that occur throughout postembryonic development. Here two species of eurypterid are described from the Pragian Beartooth Butte Formation of Cottonwood Canyon in Wyoming, and included in a phylogenetic analysis. Both species comprise individuals from a number of instars, and this allows for changes that occur throughout their ontogeny to be documented, and how ontogenetically variable characters can influence phylogenetic analysis to be tested. Results: The two species of eurypterid are described as Jaekelopterus howelli (Kjellesvig-Waering and St[latin small letter o with stroke]rmer, 1952) and Strobilopterus proteus sp. nov. Phylogenetic analysis places them within the Pterygotidae and Strobilopteridae respectively, both families within the Eurypterina. Jaekelopterus howelli shows positive allometry of the cheliceral denticles throughout ontogeny, while a number of characteristics including prosomal appendage length, carapace shape, lateral eye position, and relative breadth all vary during the growth of Strobilopterus proteus. Conclusions: The ontogeny of Strobilopterus proteus shares much in common with that of modern xiphosurans, however certain characteristics including apparent true direct development suggest a closer affinity to arachnids. The ontogenetic development of the genital appendage also supports the hypothesis that the structure is homologous to the endopods of the trunk limbs of other arthropods. Including earlier instars in the phylogenetic analysis is shown to destabilise the retrieved topology. Therefore, coding juveniles as individual taxa in an analysis is shown to be actively detrimental and alternative ways of coding ontogenetic data into phylogenetic analyses should be explored.
Source: BMC Evolutionary Biology
Exaptation of Transposable Elements into Novel Cis-Regulatory Elements: Is the Evidence Always Strong?
Transposable elements (TEs) are mobile genetic sequences that can jump around the genome from one location to another, behaving as genomic parasites. TEs have been particularly effective in colonizing mammalian genomes, and such heavy TE load is expected to have conditioned genome evolution. Indeed, studies conducted both at the gene and genome levels have uncovered TE insertions that seem to have been co-opted—or exapted—by providing transcription factor binding sites (TFBSs) that serve as promoters and enhancers, leading to the hypothesis that TE exaptation is a major factor in the evolution of gene regulation. Here, we critically review the evidence for exaptation of TE-derived sequences as TFBSs, promoters, enhancers, and silencers/insulators both at the gene and genome levels. We classify the functional impact attributed to TE insertions into four categories of increasing complexity and argue that so far very few studies have conclusively demonstrated exaptation of TEs as transcriptional regulatory regions. We also contend that many genome-wide studies dealing with TE exaptation in recent lineages of mammals are still inconclusive and that the hypothesis of rapid transcriptional regulatory rewiring mediated by TE mobilization must be taken with caution. Finally, we suggest experimental approaches that may help attributing higher-order functions to candidate exapted TEs.
Computational predictions have become indispensable for evaluating the disease-related impact of nonsynonymous single-nucleotide variants discovered in exome sequencing. Many such methods have their roots in molecular evolution, as they use information derived from multiple sequence alignments. We show that the performance of current methods (e.g., PolyPhen-2 and SIFT) is improved significantly by optimizing their statistical models on evolutionarily balanced training data, where equal numbers of positive and negative controls within each evolutionary conservation class are used. Evolutionary balancing significantly reduces the false-positive rates for variants observed at highly conserved sites and false-negative rates for variants observed at fast evolving sites. Use of these improved methods enables more accurate forecasting when concordant diagnosis from multiple methods is regarded as a more reliable indicator of the prediction. Applied to a large exome variation data set, we find that the current methods produce concordant predictions for less than half of the population variants. These advances are implemented in a web resource for use in practical applications (www.mypeg.info, last accessed March 13, 2013).
Large-scale, genome-level molecular phylogenetic analyses present both opportunities and challenges for bacterial evolutionary and ecological studies. We constructed a phylum-level bacterial phylogenetic marker database by surveying all complete bacterial genomes and identifying single-copy genes that were widely distributed in each of the 20 bacterial phyla. We showed that phylum trees made using these markers were highly resolved and were more robust than the bacterial genome tree based on 31 universal bacterial marker genes. In addition, using the Global Ocean Sampling data set as an example, we demonstrated that the expanded marker database greatly increased the power of metagenomic phylotyping. We incorporated the database into an automated phylogenomic inference application (Phyla-AMPHORA) and made it publicly available. We believe that this centralized resource should have broad applicability in bacterial systematics, phylogenetics, and metagenomic studies.
Gene duplication generates genetic novelty and redundancy and is a major mechanism of evolutionary change in bacteria and eukaryotes. To date, however, gene duplication has been reported only rarely in RNA viruses. Using a conservative BLAST approach we systematically screened for the presence of duplicated (i.e., paralogous) proteins in all RNA viruses for which full genome sequences are publicly available. Strikingly, we found only nine significantly supported cases of gene duplication, two of which are newly described here—in the 25 and 26 kDa proteins of Beet necrotic yellow vein virus (genus Benyvirus) and in the U1 and U2 proteins of Wongabel virus (family Rhabdoviridae). Hence, gene duplication has occurred at a far lower frequency in the recent evolutionary history of RNA viruses than in other organisms. Although the rapidity of RNA virus evolution means that older gene duplication events will be difficult to detect through sequence-based analyses alone, it is likely that specific features of RNA virus biology, and particularly intrinsic constraints on genome size, reduce the likelihood of the fixation and maintenance of duplicated genes.
Markov models of codon substitution naturally incorporate the structure of the genetic code and the selection intensity at the protein level, providing a more realistic representation of protein-coding sequences compared with nucleotide or amino acid models. Thus, for protein-coding genes, phylogenetic inference is expected to be more accurate under codon models. So far, phylogeny reconstruction under codon models has been elusive due to computational difficulties of dealing with high dimension matrices. Here, we present a fast maximum likelihood (ML) package for phylogenetic inference, CodonPhyML offering hundreds of different codon models, the largest variety to date, for phylogeny inference by ML. CodonPhyML is tested on simulated and real data and is shown to offer excellent speed and convergence properties. In addition, CodonPhyML includes most recent fast methods for estimating phylogenetic branch supports and provides an integral framework for models selection, including amino acid and DNA models.
The Candida Gene Order Browser (CGOB) was developed as a tool to visualize and analyze synteny relationships in multiple Candida species, and to provide an accurate, manually curated set of orthologous Candida genes for evolutionary analyses. Here, we describe major improvements to CGOB. The underlying structure of the database has been changed significantly. Genomic features are now based directly on genome annotations rather than on protein sequences, which allows non-protein features such as centromere locations in Candida albicans and tRNA genes in all species to be included. The data set has been expanded to 13 species, including genomes of pathogens (C. albicans, C. parapsilosis, C. tropicalis, and C. orthopsilosis), and those of xylose-degrading species with important biotechnological applications (C. tenuis, Scheffersomyces stipitis, and Spathaspora passalidarum). Updated annotations of C. parapsilosis, C. dubliniensis, and Debaryomyces hansenii have been incorporated. We discovered more than 1,500 previously unannotated genes among the 13 genomes, ranging in size from 29 to 3,850 amino acids. Poorly conserved and rapidly evolving genes were also identified. Re-analysis of the mating type loci of the xylose degraders suggests that C. tenuis is heterothallic, whereas both Spa. passalidarum and S. stipitis are homothallic. As well as hosting the browser, the CGOB website (http://cgob.ucd.ie) gives direct access to all the underlying genome annotations, sequences, and curated orthology data.
It is currently unclear whether the amino acid substitutions that occur during protein evolution are primarily driven by adaptation, or reflect the random accumulation of neutral changes. When estimated from genomic data, the proportion of adaptive amino acid substitutions, called α, was found to vary greatly across species, from nearly zero in humans to above 0.5 in Drosophila. These variations have been interpreted as reflecting differences in effective population size, adaptation being supposedly more efficient in large populations. Here, we investigate the influence of effective population size and other biological parameters on the rate of adaptive evolution by simulating the evolution of a coding sequence under Fisher’s geometric formalism. We explicitly model recurrent environmental changes and the subsequent adaptive walks, followed by periods of stasis during which purifying selection dominates. We show that, under a variety of conditions, the effective population size has only a moderate influence on α, and an even weaker influence on the per generation rate of selective sweeps, modifying the prevalent view in current literature. The rate of environmental change and, interestingly, the dimensionality of the phenotypic space (organismal complexity) affect the adaptive rate more deeply than does the effective population size. We discuss the reasons why verbal arguments have been misleading on that subject and revisit the empirical evidence. Our results question the relevance of the "α" parameter as an indicator of the efficiency of molecular adaptation.
The Barcode of Life
The Genealogical World of Phylogenetic Networks
BMC Evolutionary Biology