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October 28, 2014
It is well known that reticulations in phylogenetic networks can reflect variation in data sets from many sources, not only gene flow during evolutionary history. These other sources are presumably unwanted in the analysis when they are due to estimation errors. Such errors include incorrect data, inappropriate sampling, and model mis-specification.
For molecular data, one of the more obvious sources of model mis-specification is an incorrect multiple sequence alignment. This reflects wrong assessments of primary homology among the characters, so that the wrong residues are aligned in the columns. This particular issue seems not to have been addressed in the network literature in any systematic way.
However, it is obviously rather important. After all, who needs a phylogenetic network that reflects mis-alignment rather than evolutionary history? One approach to this issue would be to have some sort of measurement of our confidence in the alignment columns, which could be taken into account when the network is constructed.
One practical problem with this approach is that there has been a veritable cottage industry developing such measurements, which would need to be assessed for their suitability. So, I thought that I might list some of them here, along with a brief description of what they measure. The list is comprehensive but not necessarily exhaustive — it consists of ones for which there was at some stage a computer program (there are others that have never been named). Most of the methods are designed specifically for amino-acid sequences, so that not all of them can be used for nucleotides.
There are basically two types of measurement: (1) quantitative scoring schemes, which provide a reliability score for each aligned position, and (2) selection schemes, which select a subset of the aligned positions as being reliably aligned. So, I have divided the list roughly into these two groups.
Dopazo J (1997) A new index to find regions showing an unexpected variability or conservation in sequence alignments. Computer Applications in the Biosciences 13: 313-317.
— evolutionary index is based on conservativeness of amino acid differences as predicted from nucleotide differences
Thompson JD, Gibson TJ, Plewniak F, Jeanmougin F, Higgins DG (1997) The CLUSTAL-X windows interface: flexible strategies for multiple sequence alignment aided by quality analysis tools. Nucleic Acids Research 25: 4876-4882.
— quality is based on conservativeness of amino acid differences
Notredame C, Holm L, Higgins DG (1998) COFFEE: an objective function for multiple sequence alignments. Bioinformatics 14: 407-422.
— score represents consistency among global and local alignments
Pei J, Grishin NV (2001) AL2CO: calculation of positional conservation in a protein sequence alignment. Bioinformatics 17: 700-712.
— conservation is based on weighted entropy
Redelings BD, Suchard MA (2005) Joint Bayesian estimation of alignment and phylogeny. Systematic Biology 54: 401-418.
— approximate probability that the letter is homologous to the ancestral residue in its column
Lassmann T, Sonnhammer EL (2005) Automatic assessment of alignment quality. Nucleic Acids Research 33: 7120-7128.
— consistency based on overlap of alignments from several programs
Landan G, Graur D (2007) Heads or tails: a simple reliability check for multiple sequence alignments. Molecular Biology and Evolution 24: 1380-1383.
— measures uncertainty due to co-optimal alignments
Bradley RK, Roberts A, Smoot M, Juvekar S, Do J, Dewey C, Holmes I, Pachter L (2009) Fast Statistical Alignment. PLoS Computational Biology 5: e1000392.
— several scores based on HMM consistency, certainty, expected accuracy, expected sensitivity, expected specificity
Penn O, Privman E, Landan G, Graur D, Pupko T (2010) An alignment confidence score capturing robustness to guide tree uncertainty. Molecular Biology and Evolution 27: 1759-1767.
— robustness to guide tree uncertainty
Kim J, Ma J (2011) PSAR: measuring multiple sequence alignment reliability by probabilistic sampling. Nucleic Acids Research 39: 6359-6368.
— agreement with probabilistic sampling of suboptimal alignments
Wu M, Chatterji S, Eisen JA (2012) Accounting for alignment uncertainty in phylogenomics. PLoS One 7: e30288.
— pair Hidden Markov Model to model the sequence evolution and uses the model to calculate the posterior probabilities that residues of a column are correctly aligned
Chang J-M, Di Tommaso P, Notredame C (2014) TCS: a new multiple sequence alignment reliability measure to estimate alignment accuracy and improve phylogenetic tree reconstruction. Molecular Biology and Evolution 31: 1625-1637.
— transitive consistency score is an extended version of the Coffee scoring scheme
Martin MJ, Gonzâlez-Candelas F, Sobrino F, Dopazo J (1995) A method for determining the position and size of optimal sequence regions for phylogenetic analysis. Journal of Molecular Evolution 41: 1128-1138.
— locates the smallest blocks with similar pairwise genetic distances to the whole alignment
Castresana J (2000) Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Molecular Biology and Evolution 17: 540-552.
— selected blocks are based on conservation of identity
Löytynoja A, Milinkovitch MC (2001) SOAP, cleaning multiple alignments from unstable blocks. Bioinformatics 17: 573-574.
— stability is measured with respect to variation in the Clustal gap-opening and gap-extension penalties
Thompson JD, Plewniak F, Ripp R, Thierry J-C, Poch O (2001) Towards a reliable objective function for multiple sequence alignments. Journal of Molecular Biology 314: 937-951.
— normalized mean distance is based on pairwise distances
Cline M, Hughey R, Karplus K (2002) Predicting reliable regions in protein sequence alignments. Bioinformatics 18: 306-314.
— uses information from near-optimal alignments
Lawrence CJ, Zmasek CM, Dawe RK, Malmberg RL (2004) LumberJack: a heuristic tool for sequence alignment exploration and phylogenetic inference. Bioinformatics 20: 1977–1979.
— identifies blocks that have their phylogenetic tree being most similar to that of the whole alignment
Dress AW, Flamm C, Fritzsch G, Grünewald S, Kruspe M, Prohaska SJ, Stadler PF. (2008) Noisy: identification of problematic columns in multiple sequence alignments. Algorithms in Molecular Biology 3: 7.
— identification of phylogenetically uninformative homoplastic sites from compatibilities in a circular split system
Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T (2009) trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25: 1972-1973.
— proportion of sequences with a gap, level of amino acid similarity, level of consistency across different (user-provided) alignments
Blouin C, Perry S, Lavell A, Susko E, Roger AJ. (2009) Reproducing the manual annotation of multiple sequence alignments using a SVM classifier. Bioinformatics 25: 3093-3098.
— support vector machine reproduces manual annotations from other alignments
Criscuolo A, Gribaldo S (2010) BMGE (Block Mapping and Gathering with Entropy): a new software for selection of phylogenetic informative regions from multiple sequence alignments. BMC Evolutionary Biology 10: 210.
— calculates entropy-like scores weighted by similarity matrices
Kück P, Meusemann K, Dambach J, Thormann B, von Reumont BM, Wägele JW, Misof B (2010) Parametric and non-parametric masking of randomness in sequence alignments can be improved and leads to better resolved trees. Frontiers in Zoology 7: 10.
— consensus profiles identify dominating patterns of nonrandom similarity
Rajan V (2013) A method of alignment masking for refining the phylogenetic signal of multiple sequence alignments. Molecular Biology and Evolution 30: 689-712.
— compatible subsplits define clusters of sites which are then removed based on evolutionary rate
Wednesday, 12:00 PM at NESCent, Ninth Street and Main Street, Erwin Mill Building, 2024 W. Main Street, Suite A200. For more information, call 919-668-4551
Erick Matsen wrote:
@cwhidden and I would like to sample from the subtree-prune-regraft (SPR) random walk on rooted phylogenetic trees. Does anyone know an easy way to do this? Chris could roll his own, but I'll bet that there is an easy solution out there.
If we wanted to sample from the random walk on unrooted trees, we could sample from the MrBayes prior. BEAST is rooted, which is nice, but has non-uniform priors on topologies. @mlandis would this be easy with revBayes?
The Museum of Paleontology and the Department of Earth and Environmental Sciences at the University of Michigan are searching for a tenure-track faculty candidate in the field of Paleontology. This is a university year appointment with an expected start date of September 1, 2015. We anticipate an appointment at the assistant professor/assistant curator level, but applications at other levels will be considered. The successful candidate is expected to establish a leading research program and contribute to both undergraduate and graduate teaching. The appointment in the Museum of Paleontology involves shared curatorial responsibility for a major research collection, now on the threshold of significant enhancement of its online footprint. To apply please use the "Apply Through Website", complete the online form, and upload the required application documents as a single PDF file. If you have any questions or comments, please send an email message to: Michiganfirstname.lastname@example.org.
Source: Systematics jobs at ResearchGate
Call for Participation (apologies for multiple copies) Synthetic and Systems Biology Summer School: Biology meets Engineering and Computer Science - 2nd Edition Taormina - Sicily, Italy, July 5-9, 2015 http://bit.ly/ZWPdub email@example.com ** Deadlines ** Student Application: February 15, 2015 Oral/Poster Submission: February 15, 2015 The Synthetic and Systems Biology Summer School (SSBSS) is a full-immersion course on cutting-edge advances in systems and synthetic biology with lectures delivered by world-renowned experts. The school provides a stimulating environment for doctoral students, early career researches and industry leaders. Participants will also have the chance to present their results (Oral presentations or Posters) and to interact with their peers. ** Topics ** Genetic Engineering Metabolic Engineering Genome Design Reading and Writing Genomes Pathway Design Synthetic Circuits and Cells Biological CAD Industrial Applications Artificial Tissues and Organs Genomically recoded Organisms Biological Design Automation ** List of Speakers ** * Adam Arkin, University of California Berkeley, USA * Jef Boeke, New York University, USA * Angela DePace, Harvard University, USA * Forbes Dewey, MIT, USA * Paul Freemont, Imperial College London, UK * Karmella Haynes, Arizona State University, USA * Richard Kitney, Imperial College London, UK * Timothy Lu, MIT, USA * Philip Maini, Oxford University, UK * Steve Oliver, Cambridge University, UK * Greg Stephanopoulos, MIT, USA - TBC * Nicola Zamboni, ETH, Switzerland Other speakers will be announced soon. ** Industrial Panel ** * Zach Serber, Zymergen, Inc. USA More speakers will be announced soon! School Directors Jef D. Boeke, New York University, USA Giuseppe Nicosia, University of Catania, Italy Mario Pavone, University of Catania, Italy Giovanni Stracquadanio, University of Oxford, UK ** Short Talk and Poster Submission ** Students may submit a research abstract for presentation. School directors will review the abstracts and will recommend for poster or short-oral presentation. Abstract should be submitted by February 15, 2015. The abstracts will be published on the electronic hands-out material of the summer school. http://bit.ly/1zD5yVt http://bit.ly/ZWPdub firstname.lastname@example.org Apologies for multiple copies. Please forward to anybody who might be interested. email@example.com via Gmail
Conference: Ecological and Evolutionary Genomics Gordon Research Conference July 12-17 2015 at University of New England, Biddeford, ME >From Genomes to Biomes: Using Biodiversity to Explore Biocomplexity. >From genomes to biomes, from microbes to plants and animals, the 2015 Gordon Research Conference on Ecological and Evolutionary Genomics will highlight how genome-enabled approaches are helping to rapidly advance our understanding of the complicated relationship between genotype, phenotype and the environment. Topic areas such as population genomics, adaptation & speciation, symbiosis and interacting organisms, biodiversity & phylogenomics, community & ecosystem genomics, genetic and ecological networks, methods & non-model organisms, genomics & animal behavior, and applications of ecological and evolutionary genomics, will highlight how biodiversity can be used to illuminate complex biological relationships and inform ecological and evolutionary processes and molecular mechanisms of adaptation to changing environments. The conference will also feature emerging approaches and technologies to aid further exploration of the genomes from organisms that span the tree of life. Gordon Conferences are famous for fostering in depth interactions that yield new insights in a collegial atmosphere. Co-chairs, Jack Werren (University of Rochester) and Michael Herman (Kansas State University) along with Vice-chairs Felicity Jones (Max Plank Institute, Tubingen) and Michael Pfrender (University of Notre Dame) invite you to join us on the ocean-side campus of the University of New England in Biddeford, Maine for a stimulating conference. We are assembling a diverse group of established and early career investigators to discuss their latest work. Discussion leaders and symposium speakers will also be chosen from among the applicants. The organizers are actively seeking funds to assist students and others attend the meeting. Applications for attendance will be accepted until the meeting is full, don’t delay! Applications to attend are now open and information can be found at http://bit.ly/1zD0ccE (click the “For Attendees” link). Please plan on joining us in Biddeford in 2015! Symposim Topic Areas & Speakers as of 27 October 2014 Population Genomics, Adaptation & Speciation (Andy Clark, Josephine Pemberton, Elodie Ghedin) Symbiosis & Interacting Organisms (Angela Douglas, Siv Anderrson, Takema Fukatsu, Todd Schlenke) Behavioral Ecology Meets Genomics (Laurent Keller, Wayne Potts, Amy Toth, TBA) Networks: From Genes to Ecosystems (Patricia Wittkopp, Cedric Feschotte, Alvaro Sanchez, Karoline Faust) Applications of Ecological & Evolutionary Genomics (Sherry Flint-Garcia, Joe Shaw, John Colbourne) Advances in Genomic Approaches in Non-Model Organisms (Steven Salzberg, Wes Warren, TBA) Biodiversity & Phylogenomics (Holly Bik, Casey Dunn, TBA) Community & Ecosystem Genomics (Jack Gilbert, Blake Matthews, Jen Schweitzer) John (Jack) Werren Nathaniel & Helen Wisch Professor of Biology University of Rochester Rochester, NY 14627 Email: firstname.lastname@example.org Web: http://bit.ly/1zD0aBA “Werren, Jack” via Gmail
Genetics Graduate Program Now Accepting Applications for Fall 2015 The Graduate Program in Genetics is currently accepting applications for M.S. and Ph.D. students for the Fall, 2014 semester. This program was established in 1952, and is one of the longest running genetics graduate programs in the USA. The graduate training faculty are a highly interactive group performing research in all aspects of genetics from molecules to populations. Our research encompasses behavioral genetics, biomedical genetics, computational genetics and bioinformatics, evolutionary, population and quantitative genetics, and molecular, cellular and developmental genetics. Our faculty utilize a wide range of traditional and non-traditional model systems in their research. We consider graduate students to be professionals in training, and provide a well-rounded program of academic, research and professional training. Students are intimately involved in program activities have a strong voice in shaping the program. We provide broad and comprehensive graduate training in genetics and also flexible academic programs tailored to meet the background and career goals of the individual student. For more information go to *genetics.sciences.ncsu.edu * or email Trudy Mackay (email@example.com) or Melissa Robbins ( firstname.lastname@example.org). *Melissa Robbins* *Genetics Graduate Program Coordinator* Department of Biological Sciences Genetics Program 3510 Thomas Hall Campus Box 7614 College of Sciences NC State University Tel: 919-515-2291 Email: email@example.com Melissa Robbins via Gmail
Dear all, next semester i will be teaching an introductory course on evolution for 4th year elementary science education students. we have only 40 hours, so we cannot go too much in depth. the students’ background in biology is also limited to a few biology courses. i’m therefore looking for a brief, simple undergraduate textbook on evolution. would anyone have any suggestions? thank you very much in advance! mehmet Mehmet Somel METU Dept. Biology / ODT Biyoloji Blm 06800 Ankara, Turkey Tel: +90-543-9799060; Office: +90-312-2106460 Email: firstname.lastname@example.org Web: http://bit.ly/1FRNCqD email@example.com via Gmail
The asidine darkling beetles (Coleoptera: Tenebrionidae: Asidini) are a diverse tribe of flightless tenebrionids found in many arid and sub-arid habitats around the world. The 263 currently described North American species are contained in ten genera, all of which are restricted to the western half of the continent. The Asidini, like all members of the subfamily Pimeliinae, lack defensive glands. Instead, several phenotypic traits occur within the tribe that may help limit predation. These include the contrasting defensive strategies of crypsis, through either background matching or pattern disruption, and Batesian mimicry of the chemically defended genus Eleodes. Dorsal elytral morphology was assessed between 53 North American asidine species and 13 common Eleodes model species using multiple methodologies to assess similarities between species in the two groups that might indicate mimetic relationships. A phylogeny of the North American asidines is used to map the occurrence of differing defensive strategies within the tribe. Crypsis is reconstructed as the ancestral state, with two origins for Batesian mimicry and multiple reversals. The combination of strongly to weakly cryptic species and varying levels of mimetic fidelity to Eleodes model species make the asidines a promising lineage upon which to further explore the evolution of defensive phenotypes.
Parsimony analysis of unaligned sequence data: maximization of homology and minimization of homoplasy, not minimization of operationally defined total cost or minimization of equally weighted transformations
Wheeler (2012) stated that minimization of ad hoc hypotheses as emphasized by Farris (1983) always leads to a preference for trivial optimizations when analysing unaligned sequence data, leaving no basis for tree choice. That is not correct. Farris's framework can be expressed as maximization of homology, a formulation that has been used to overcome the problems with inapplicables (it leads to the notion of subcharacters as a quantity to be co-minimized in parsimony analysis) and that is known not to lead to a preference for trivial optimizations when analysing unaligned sequence data. Maximization of homology, in turn, can be formulated as a minimization of ad hoc hypotheses of homoplasy in the sense of Farris, as shown here. These issues are not just theoretical but have empirical relevance. It is therefore also discussed how maximization of homology can be approximated under various weighting schemes in heuristic tree alignment programs, such as POY, that do not take into account subcharacters. Empirical analyses that use the so-called 3221 cost set (gap opening cost three, transversion and transition costs two, and gap extension cost one), the cost set that is known to be an optimal approximation under equally weighted homology in POY, are briefly reviewed. From a theoretical point of view, maximization of homology provides the general framework to understand such cost sets in terms that are biologically relevant and meaningful. Whether or not embedded in a sensitivity analysis, this is not the case for minimization of a cost that is defined in operational terms only. Neither is it the case for minimization of equally weighted transformations, a known problem that is not addressed by Kluge and Grant's (2006) proposal to invoke the anti-superfluity principle as a rationale for this minimization.
Although these positions are for ecologists, we think this cluster hire would be of interest to the readers of evoldir. Cluster Hire in Geographic Ecology: three positions at the rank of Assistant, Associate, or Full Professor http://GE.ou.edu The Department of Biology at the University of Oklahoma invites applications for three tenured/tenure-track faculty positions at any rank, beginning in fall 2015. We are searching for creative, collaborative thinkers who use integrative approaches to address fundamental ecological questions at regional to global scales. Our ultimate goal is to enhance our expertise in geographical and aquatic ecology toward predicting ecological and evolutionary responses to global change. The search is open to theoretical, lab, and field biologists working on any taxa. In this cluster hire, we seek: * A Geographical Ecologist who studies phenomena at multiple spatial scales toward understanding large-scale patterns and processes. Innovators in biogeography, macroecology, bioinformatics, and global ecology are especially encouraged to apply. * An Aquatic Ecologist who studies freshwater ecosystems toward predicting the role of changing water supplies on ecosystem services. Innovators in biogeochemistry, ecological networks, ecological genomics, river-reservoir systems and land-water interactions are especially encouraged to apply. * A Physiological Ecologist who studies the origin and maintenance of ecological traits and their ultimate role in the dynamics of population and ecosystem responses to a changing environment. Innovators studying traits involved in metabolic, stoichiometric, thermal and water-related variation and adaptation are especially encouraged to apply. We are especially interested in candidates who use or combine some of the following three approaches in their work. The first is development and/or testing of models and theory that connect phenomena at scales from local to global. The second is an integrative use of data-from gene frequencies to biogeochemistry, species distributions to climate past and future, functional traits to landscapes-to advance theory and identify novel patterns and processes. The third is a desire to apply this research to ameliorating outstanding ecological problems, including climate change, biodiversity loss, dwindling water supplies, and the degradation of ecosystem services. The University of Oklahoma is committed to building an international center of excellence exploring the geographical ecology of our evolving biosphere. Successful candidates will join colleagues across campus, including cluster hires in the EPSCoR initiative Adapting socio-ecological systems to increased climate variability. Our shared goal is to build theoretical and empirical bridges across the sciences, to predict the interplay between biotic and climatic changes, and to better steward our natural resources and services. Join us. How to Apply Successful candidates will have a Ph.D. degree and a record of outstanding achievement as evidenced by publications. Preferred candidates will have a promising (assistant) or externally funded (associate/full) research program and the ability to lead interdisciplinary, multi-investigator projects across a range of geographic scales. Each individual will be expected to provide excellent training for graduate students and postdocs, and contribute to undergraduate and graduate teaching (one course per semester) in the department. Applicants should submit a cover letter, complete curriculum vitae, research and teaching statements, and selected reprints/preprints as PDF files to Chair, Geographical Ecology Search Committee, at firstname.lastname@example.org. Applicants should also arrange to have three signed letters of reference sent to email@example.com or Department of Biology, 730 Van Vleet Oval, University of Oklahoma, Norman, OK 73019, USA. Applicants at the rank of Associate Professor or Professor may submit names and contact information for three references in lieu of letters. Visit us at http://bit.ly/1sVS5Ut. Screening of candidates will begin 3 December 2014 and will continue until the positions are filled. The University of Oklahoma is an Affirmative Action/Equal Opportunity employer and encourages diversity in the workplace. Protected veterans and individuals with disabilities are encouraged to apply. Rosemary Knapp Professor and Director of Graduate Studies Department of Biology 730 Van Vleet Oval University of Oklahoma Norman, OK 73019 “Knapp, Rosemary” via Gmail
October 26, 2014
Charles Darwin and Alfred Russel Wallace are usually credited with independently developing the idea that natural selection could be the important process by which new species arise, although history has apportioned most of the fame to Darwin alone.
In the first edition of his most famous book Darwin (1859) cited no sources, and credited no-one except Thomas Malthus as a source of ideas. He was criticized for this, and from the third edition onwards he provided a historical essay mentioning a few more names.
The basic issue is that the idea of natural election had been "in the air" for more than half a century, but only with respect to within-species variation. It was Darwin and Wallace who took the leap to consider between-species variation, on the basis that there is no historical boundary defining species — all individuals trace their ancestry back through a whole series of ancestors, including those who existed before the origin of their current species. That is, phylogenies trace back to the origin of life not just to the origin of each species.
So, who were the people who published, however briefly, a comment noting the idea of within-species natural selection? Joachim Dagg, of the Natural History Apostils blog, has recently been writing a series of posts discussing many of those publications that contain a clear description of selection. Here I have provided a convenient overview, in time order, with links to Joachim's blog for those of you who want more information.
Link 1 - Link 2
William Charles Wells
Link 1 - Link 2
Link 1 - Link 2 - Link 3
Link 1 - Link 2 - Link 3
John C. Loudon
Link 1 - Link 2
* Full title: The Magazine of Natural History and Journal of Zoology, Botany, Mineralogy, Geology, and Meteorology
Dear list members, Registration is open for the course “INTRODUCTION TO NETWORK TOOLS IN BIOSCIENCES - 2nd Edition”. Course Webpage: http://bit.ly/19yfo98 INSTRUCTORS: Dr. Diego Rasskin-Gutman (Institut Cavanilles de Biodiversitat i Biologia Evolutiva, Spain) and Dr. Borja Esteve-Altava (Institut Cavanilles de Biodiversitat i Biologia Evolutiva, Spain). DATES: April, 20-24, 2014. 34 teaching hours. PLACE: Facilities of the Centre de Restauraci i Interpretaci de Els Hostalets de Pierola, Els hostalets de Pierola, Barcelona (Spain). PROGRAM: - Complex Biological Systems: Modelling Relations: Historical and conceptual introduction. Basic concepts and representations. - Hands on Computers: Introduction to R: Presentation of the R environment and language. Basic operations in R (useful for network modelling). Packages installation. - Hands on Computers: Introduction to igraph and Network Modelling: Presentation of the package igraph. Modelling deterministic networks. Manipulating network attributes. Modelling networks from loaded data. - Complex Biological Systems: Applied Network Theory: Nodes, links and types of networks. Basic network parameters. Network architecture and null network models. - Work Example: Analysing parameters and architecture in tetrapod skull networks. - Hands on Computers: Analysing Networks: Quantifying basic network parameters. Identifying network architecture. - Work Example: Null network models of skull development to study evolution. - Hands on Computers: Modelling Network Null Models: Regular and random models. Small-world and scale-free models. Geometric models. - Complex Biological Systems: Network Properties: Robustness and the concept of secondary extinction. Modularity. - Work Example: Modularity in skull networks. - Hands on Computers: Identifying Modules: Optimization methods. Heuristic methods. Quantifying the strength of modularity. - Participants Project Preparation. Bringing your own data is not required for this part, but you are welcome to do so if you have it. Organized by: Transmitting Science, the Institut Catal de Paleontologia Miquel Crusafont and the Council of Hostalets de Pierola. Please feel free to distribute this information between your colleagues if you consider it appropriate. With best regards Soledad De Esteban Trivigno, PhD. Course Director Transmitting Science firstname.lastname@example.org via Gmail
October 25, 2014
The University of Washington has post-doctoral position open in their “Genetic Approaches to Aging” Training Grant (see below). The position is available immediately. Applicants must be US citizens or Green Card holders. The successful applicant will have the choice of numerous labs (information on links below) with diverse training options, including training in the Evolutionary Genetics of Aging (e.g., http://bit.ly/13eK9N6). The T32 Genetic Approaches to Aging Training Grant has one Post-doctoral slot open for a 9-month appointment The goal of our program is to train new independent investigators who will utilize contemporary molecular and genetic techniques to investigate the underlying mechanisms of aging. Applications are scored by consideration of the qualifications of the applicant and the mentoring environment, as well as how the research specifically relates to the biology of aging. Funding is at NIH stipend levels. Deadlines Applications are considered on a rolling basis, we encourage applicants to make their submissions as soon as possible. Applications for the slot will be accepted until the position is filled. For more Information: For more information on the Genetic Approaches to Aging Training Program visit http://bit.ly/1zurT7B For Application instructions visit http://bit.ly/1uXLbdE grant/application For questions regarding the application process, please contact Rachel Wilsey at email@example.com or 206-616-4135 Daniel Promislow Department of Pathology and Department of Biology University of Washington 1959 NE Pacific Street Box 357705, Room K-078 Seattle, WA 98195 ph: 206 616-6994 e: firstname.lastname@example.org Daniel Promislow via Gmail
The role of gene duplication in generating new genes and novel functions is well recognized and is exemplified by the digestion-related protein lysozyme. In ruminants, duplicated chicken-type lysozymes facilitate the degradation of symbiotic bacteria in the foregut. Chicken-type lysozyme has also been reported to show chitinase-like activity, yet no study has examined the molecular evolution of lysozymes in species that specialize on eating insects. Insectivorous bats number over 900 species, and lysozyme expression in the mouths of some of these species is associated with the ingestion of insect cuticle, suggesting a chitinase role. Here, we show that chicken-type lysozyme has undergone multiple duplication events in a major family of insect-eating bats (Vespertilionidae) and that new duplicates have undergone molecular adaptation. Examination of duplicates from two insectivorous bats—Pipistrellus abramus and Scotophilus kuhlii—indicated that the new copy was highly expressed in the tongue, whereas the other one was less tissue-specific. Functional assays applied to pipistrelle lysozymes confirmed that, of the two copies, the tongue duplicate was more efficient at breaking down glycol chitin, a chitin derivative. These results suggest that the evolution of lysozymes in vespertilionid bats has likely been driven in part by natural selection for insectivory.
The Genealogical World of Phylogenetic Networks
BMC Evolutionary Biology
Molecular Biology and Evolution