Spontaneous mutation rate in the smallest photosynthetic eukaryotes

Mutation is the ultimate source of genetic variation, and knowledge of mutation rates is fundamental for our understanding of all evolutionary processes. High throughput sequencing of mutation accumulation lines has provided genome wide spontaneous mutation rates in a dozen model species, but estimates from nonmodel organisms from much of the diversity of life are very limited. Here, we report mutation rates in four haploid marine bacterial-sized photosynthetic eukaryotic algae; Bathycoccus prasinos, Ostreococcus tauri, Ostreococcus mediterraneus, and Micromonas pusilla. The spontaneous mutation rate between species varies from μ = 4.4 × 10−10 to 9.8 × 10−10 mutations per nucleotide per generation. Within genomes, there is a two-fold increase of the mutation rate in intergenic regions, consistent with an optimization of mismatch and transcription-coupled DNA repair in coding sequences. Additionally, we show that deviation from the equilibrium GC content increases the mutation rate by ∼2% to ∼12% because of a GC bias in coding sequences. More generally, the difference between the observed and equilibrium GC content of genomes explains some of the inter-specific variation in mutation rates

Statistical Analysis with Webstat, a Java applet for the World Wide Web

The Java programming language has added a new tool for delivering computing applications over the World Wide Web (WWW). WebStat is a new computing environment for basic statistical analysis which is delivered in the form of a Java applet. Anyone with WWW access and a Java capable browser can access this new analysis environment. Along with an overall introduction of the environment, the main features of this package are illustrated, and the prospect of using basic WebStat components for more advanced applications is discussed.

A Relational Event Approach to Modeling Behavioral Dynamics

This chapter provides an introduction to the analysis of relational event data (i.e., actions, interactions, or other events involving multiple actors that occur over time) within the R/statnet platform. We begin by reviewing the basics of relational event modeling, with an emphasis on models with piecewise constant hazards. We then discuss estimation for dyadic and more general relational event models using the relevent package, with an emphasis on hands-on applications of the methods and interpretation of results. Statnet is a collection of packages for the R statistical computing system that supports the representation, manipulation, visualization, modeling, simulation, and analysis of relational data. Statnet packages are contributed by a team of volunteer developers, and are made freely available under the GNU Public License. These packages are written for the R statistical computing environment, and can be used with any computing platform that supports R (including Windows, Linux, and Mac).

Computing and Visualizing Log-linear analysis interactively

The purpose of this paper is to describe a simple program for computing log-linear analysis based on a direct manipulation interface that emphasizes the use of plots for guiding the analysis and evaluating the results obtained. The program described here works as a plugin for ViSta (Young 1997) and receives the name of LoginViSta (for Log-linear analysis in ViSTa). ViSta is a statistical package based on Lisp-Stat. Lisp-Stat is a statistical programming environment developed by Luke Tierney (1990) that features an object-oriented approach for statistical computing and one that allows for The purpose of this paper is to describe a simple program for computing log-linear analysis based on a direct manipulation interface that emphasizes the use of plots for guiding the analysis and evaluating the results obtained. The program described here works as a plugin for ViSta (Young 1997) and receives the name of LoginViSta (for Log-linear analysis in ViSTa). ViSta is a statistical package based on Lisp-Stat. Lisp-Stat is a statistical programming environment developed by Luke Tierney (1990) that features an object-oriented approach for statistical computing and one that allows for Computing and Visualizing Pedro Valero-Mora and Forrest W. Young interactive and dynamic graphs

Rweb:Web-based Statistical Analysis

Rweb is a freely accessible statistical analysis environment that is delivered through the World Wide Web (WWW). It is based on R, a well known statistical analysis package. The only requirement to run the basic Rweb interface is a WWW browser that supports forms. If you want graphical output you must, of course, have a browser that supports graphics. The interface provides access to WWW accessible data sets, so you may run Rweb on your own data. Rweb can provide a four window statistical computing environment (code input, text output, graphical output, and error information) through browsers that support Javascript. There is also a set of point and click modules under development for use in introductory statistics courses.

Using R-based VOStat as a low resolution spectrum analysis tool

We describe here an online software suite VOStat written mainly for the Virtual Observatory, a novel structure in which astronomers share terabyte scale data. Written mostly in the public-domain statistical computing language and environment R, it can do a variety of statistical analysis on multidimensional, multi-epoch data with errors. Included are techniques which allow astronomers to start with multi-color data in the form of low-resolution spectra and select special kinds of sources in a variety of ways including color outliers. Here we describe the tool and demonstrate it with an example from Palomar-QUEST, a synoptic sky survey

Multi-dimensional Point Process Models in R

A software package for fitting and assessing multidimensional point process models using the R statistical computing environment is described. Methods of residual analysis based on random thinning are discussed and implemented. Features of the software are demonstrated using data on wildfire occurrences in Los Angeles County, California and earthquake occurrences in Northern California.