Hasil untuk "Evolution"

P. Markov, M. Ghafari, M. Beer et al.

Michael Perryman

These lecture notes are intended for an advanced astrophysics course on Stellar Structure and Evolution given at Utrecht University (NS-AP434M). Their goal is to provide an overview of the physics of stellar interiors and its application to the theory of stellar structure and evolution, at a level appropriate for a third-year Bachelor student or beginning Master student in astronomy. To a large extent these notes draw on the classical textbook by Kippenhahn & Weigert (1990; see below), but leaving out unnecessary detail while incorporating recent astrophysical insights and up-to-date results. At the same time I have aimed to concentrate on physical insight rather than rigorous derivations, and to present the material in a logical order, following in part the very lucid but somewhat more basic textbook by Prialnik (2000). Finally, I have borrowed some ideas from the textbooks by Hansen, Kawaler & Trimble (2004), Salaris & Cassissi (2005) and the recent book by Maeder (2009). These lecture notes are evolving and I try to keep them up to date. If you find any errors or inconsistencies, I would be grateful if you could notify me by email ( O.R.Pols@uu.nl ).

J. Hadfield, Colin Megill, Sidney M. Bell et al.

Summary Understanding the spread and evolution of pathogens is important for effective public health measures and surveillance. Nextstrain consists of a database of viral genomes, a bioinformatics pipeline for phylodynamics analysis, and an interactive visualisation platform. Together these present a real-time view into the evolution and spread of a range of viral pathogens of high public health importance. The visualization integrates sequence data with other data types such as geographic information, serology, or host species. Nextstrain compiles our current understanding into a single accessible location, publicly available for use by health professionals, epidemiologists, virologists and the public alike. Availability and implementation All code (predominantly JavaScript and Python) is freely available from github.com/nextstrain and the web-application is available at nextstrain.org.

Swagatam Das, P. Suganthan

Jingqiao Zhang, A. Sanderson

M. Price, Paramvir S. Dehal, A. Arkin

Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement Neighbor-Joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N2) space and O(N2L) time, but FastTree requires just O(NLa + N) memory and O(Nlog (N)La) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 h and 2.4 GB of memory. Just computing pairwise Jukes–Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 h and 50 GB of memory. In simulations, FastTree was slightly more accurate than Neighbor-Joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.

A. K. Qin, V. L. Huang, P. Suganthan

A. Oskooi

S. Vargo, R. Lusch

D. Maddison, W. Maddison

E. Salpeter

M. Pagel

N. Hansen, A. Ostermeier

D. Gillespie

M. Nowak

T. Jukes

J. M. Smith

Dr. Susumu Ohno

V. Springel, S. White, A. Jenkins et al.

C. Baskin, J. Baskin