Muchas gracias to Bart’s Blog for this handy reference for svn branches in git. Apparently all I needed was to specify a remote branch when creating the git branch, e.g., git checkout -b git-topic-branch-foo foo
where foo is the name of the remote branch.
Welcome to the Handbook of Biological Statistics! This online textbook evolved from a set of notes for my Biological Data Analysis class at the University of Delaware. My main goal in that class is to teach biology students how to choose the appropriate statistical test for a particular experiment, then apply that test and interpret the results. I spend relatively little time on the mathematical basis of the tests; for most biologists, statistics is just a useful tool, like a microscope, and knowing the detailed mathematical basis of a statistical test is as unimportant to most biologists as knowing which kinds of glass were used to make a microscope lens. Biologists in very statistics-intensive fields, such as ecology, epidemiology, and systematics, may find this handbook to be a bit superficial for their needs, just as a microscopist using the latest techniques in 4-D, 3-photon confocal microscopy needs to know more about their microscope than someone who’s just counting the hairs on a fly’s back.
Sharing your codes in Biocoders.net is very easy, you have to choose the appropriate submitter either your are coding in R, perl, java or any programming langage you are familiar with. You will be guided through three easy steps before submitting your source codes, we have designed the submitter tool so that we could have as much information as possible on your codes. We welcome any suggestion to make these tools more useful and intuitive.
Complete Genomics has recently made several complete human genome data sets available. The genomes were sequenced at the Complete Genomics commercial genome sequencing center in Mountain View, California as part of our Complete Genomics Analysis Service (CGA™ Service). These data are largely consistent with the quality and attributes of other data provided to Complete Genomics customers.
CSAMA 2011: Computational Statistics for Genome Biology (Ninth Edition) This one week intensive course is intended to give insights into recent advances in statistical and computational aspects of the design and interpretation of microarray experiments. The topics will include all aspects of the data analysis of microarray experiments for transcript profiling and ChIP-chip. The course is intended mainly for researchers with a basic understanding of microarray technology and its statistical and computational challenges. The four practical sessions of the course will be most beneficial for participants that are able to converse in a programming language such as R.
What will it cover?
* Introduction to R and Bioconductor * RNA-Seq and ChiP-Seq data analysis * Microarray analysis * Statistics for differential expression * Sequence manipulation * Annotation of genes, genomic features and variants * Gene set enrichment analysis * Machine Learning * High-throughput image analysis