Posición disponible para botanico sistematico en el IB-UNAM, Mexico

Convocatoria 

Instituto de Biología, Universidad Nacional Autónoma de México 

Botánico Sistemático 

El Instituto de Biología de la Universidad Nacional Autónoma de México (IBUNAM), a través de la Secretaría Académica convoca a los interesados en ocupar una posición como Investigador Asociado “C” de Tiempo Completo, en el área de Botánica Sistemática en el campus de Ciudad Universitaria, Ciudad de México, a someter su documentación bajo las siguientes especificaciones.

Requisitos. 1. Tener el grado de Doctor en Ciencias o equivalente (Ph.D.), preferentemente en las áreas de Botánica, Sistemática, Biología Evolutiva o una disciplina afín. 2. Poseer experiencia de investigación en sistemática de algún grupo de plantas embriofitas, demostrada mediante publicaciones originales y de calidad, de acuerdo a su edad y trayectoria académica. 3. Tener conocimiento de la diversidad florística de México y/o el Neotrópico, así como conocimientos en la curación de colecciones científicas, técnicas de recolección en campo, morfología, sistemática molecular, biología evolutiva y otros campos afines. 4. Tener el compromiso de participar en actividades complementarias a la investigación, incluyendo la formación de recursos humanos de alto nivel mediante docencia en los programas educativos de la UNAM y la dirección de tesis de licenciatura y posgrado, además de colaborar en actividades de difusión y participación institucional. 5. Mostrar disposición para integrarse de manera inmediata a las actividades académicas del IBUNAM, ejercer liderazgo en su línea de investigación y capacidad de formar e integrarse a grupos de investigación. 6. En el caso de investigadores extranjeros, poseer suficiencia de la lengua española.

Primera etapa: las solicitudes acompañadas de la documentación serán recibidas a partir del 02 de abril de 2018 hasta el cierre de la convocatoria, que será el 29 de junio de 2018 a las 19 hrs tiempo de la Ciudad de México.

Mas información en el sitio web www.ib.unam.mx.

Course on Metagenomics and Barcoding in Crete, March 19-23, 2018

Transmitting Science is offering a new course on its new venue: AN INTRODUCTION TO METAGENOMICS AND METABARCODING.

Dates: March 19-23, 2018.

Place: Heraklion (Crete, Greece).

Instructor: Dr. M. Lisandra Zepeda-Mendoza (Chr. Hansen – Bacterial Physiology & Improvement, Denmark).

Metagenomics is the study of the collection of genomes in an environment. Environments as diverse as Antarctic lakes, hot springs, or the human gut can be biologically characterized by extracting and sequencing DNA from samples taken from them. A characteristic of many of these samples is their complexity, posing difficulties to their analysis and characterization. However, metagenomics allows the taxonomic and functional characterization of samples. These two kinds of characterizations also enable the comparison of different habitats for biodiversity assessment.

In this course students will be introduced to the command line environment used to analyze high-throughput sequencing data (HTS). The initial cleaning steps that must be performed on every HTS dataset will be described and we will use the processed data for proper functional and taxonomical characterization of a metagenomic dataset. We will use methods such as mapping to whole genome databases, de novo assembly, gene annotation, building of non-redundant gene catalogue, and metagenomic species concept identification. Due to the wide usage of metabarcoding for the taxonomic characterization of an environment, we will also discuss amplicon sequencing strategies and data analysis. The course will be based on both theory and hands-on exercises.

More information: http://www.transmittingscience.org/courses/genetics-and-genomics/introduction-metagenomics-metabarcoding/ or writing to courses.crete@transmittingscience.org

El artículo en Cladistics que no leerán los que hacen verosimilitud y probs Bayesianas

"Is there any reason, other than esprit de corps, to explain why, in many journals, it became almost impossible to publish a paper using parsimony as the only method?"

Pablo A. Goloboff, Ambrosio Torres and J. Salvador Arias. 2017. Weighted parsimony outperforms other methods of phylogenetic inference under models appropriate for morphology. Cladistics

http://onlinelibrary.wiley.com/doi/10.1111/cla.12205/epdf

Aqui esta el resumen.

Abstract One of the lasting controversies in phylogenetic inference is the degree to which specific evolutionary models should influence the choice of methods. Model-based approaches to phylogenetic inference (likelihood, Bayesian) are defended on the premise that without explicit statistical models there is no science, and parsimony is defended on the grounds that it provides the best rationalization of the data, while refraining from assigning specific probabilities to trees or character-state reconstructions. Authors who favour model-based approaches often focus on the statistical properties of the methods and models themselves, but this is of only limited use in deciding the best method for phylogenetic inference—such decision also requires considering the conditions of evolution that prevail in nature. Another approach is to compare the performance of parsimony and model- based methods in simulations, which traditionally have been used to defend the use of models of evolution for DNA sequences. Some recent papers, however, have promoted the use of model-based approaches to phylogenetic inference for discrete morpho- logical data as well. These papers simulated data under models already known to be unfavourable to parsimony, and modelled morphological evolution as if it evolved just like DNA, with probabilities of change for all characters changing in concert along tree branches. The present paper discusses these issues, showing that under reasonable and less restrictive models of evolution for discrete characters, equally weighted parsimony performs as well or better than model-based methods, and that parsimony under implied weights clearly outperforms all other methods. © The Willi Hennig Society 2017.