Ability to detect, quantify, and visualize complex and de-novo splicing variations from RNASeq.

MAJIQ’s accuracy compares favorably to other algorithms.

MAJIQ 2 is *way* faster, more memory and I/O efficient

New visualization (VOILA 2.0) Ability to analyze hundreds and thousands of samples Why so negative? (Support for a confident negative set)

Finally, a major reason we are excited about MAJIQ 2.0 is that it sets the code base for many new exciting algorithmic and visualization improvements, with application to new research questions so stay tuned!

More at https://biociphers.wordpress.com/2019/04/01/majiq-2-is-out/

Address of the bookmark: https://majiq.biociphers.org/

Address of the bookmark: https://github.com/fenderglass/Flye

Address of the bookmark: https://github.com/fenderglass/Flye

Share your favourite video tutorial or lectures on BOL at http://bioinformaticsonline.com/videolist/all . You can also add video in you groups.

Note: Other than bioinformatics video material/tutorial will be deleted without any prior warning.

BioRuby paper link : http://bioinformatics.oxfordjournals.org/content/26/20/2617.abstract

Address of the bookmark: http://www.codeschool.com/paths/ruby

This bookmark is created to provide NGS online books links.

Address of the bookmark: http://en.wikibooks.org/wiki/Next_Generation_Sequencing_%28NGS%29/Print_version

If you currently use a spreadsheet like Microsoft Excel for data analysis, you might be interested in taking a look at this tutorial on how to transition from Excel to R by Tony Ojeda. The tutorial explains how to use R functions in place of Excel formulas, including tools like =AVERAGE and =VLOOKUP. For the most part, it uses modern R packages to keep the R code clear and concise.

You'll likely still be using Excel as a data source, though, so you'll also want to check out this guide to importing data from Excel to R from MilanoR.

Reference http://www.r-bloggers.com/an-r-tutorial-for-microsoft-excel-users/

This is a short post giving steps on how to actually install R packages. Let’s suppose you want to install the ggplot2 package. Well nothing could be easier. We just fire up an R shell and type:

> install.packages("ggplot2")

In theory the package should just install, however:

• if you are using Linux and don’t have root access, this command won’t work.
• you will be asked to select your local mirror, i.e. which server should you use to download the package.

Installing packages without root access

First, you need to designate a directory where you will store the downloaded packages. On my machine, I use the directory /data/Rpackages/ After creating a package directory, to install a package we use the command:

> install.packages("ggplot2", lib="/data/Rpackages/")
> library(ggplot2, lib.loc="/data/Rpackages/")


It’s a bit of a pain having to type /data/Rpackages/ all the time. To avoid this burden,  we create a file .Renviron in our home area, and add the line R_LIBS=/data/Rpackages/ to it. This means that whenever you start R, the directory /data/Rpackages/ is added to the list of places to look for R packages and so:

> install.packages("ggplot2")
> library(ggplot2)

just works!

Setting the repository

Every time you install a R package, you are asked which repository R should use. To set the repository and avoid having to specify this at every package install, simply:

• create a file .Rprofile in your home area.
• Add the following piece of code to it:


cat(".Rprofile: Setting UK repositoryn")
r = getOption("repos") # hard code the UK repo for CRAN
r["CRAN"] = "http://cran.uk.r-project.org"
options(repos = r)
rm(r)


I found this tip in a stackoverflow answer .

More at http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

Address of the bookmark: http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

This is an extensive tutorial to take you through the main features and gotchas of configuring GBrowse as a server. This tutorial assumes that you have successfully set up Perl, GD, BioPerl and the other GBrowse dependencies. If you haven't, please see the GBrowse HOWTO During most of the tutorial, we will be using the "in-memory" GBrowse database (no relational database required!) Later we will show how to set up a genome size database using the berkeleydb and MySQL adaptors.

More at http://elp.ucdavis.edu/tutorial/tutorial.html

Address of the bookmark: http://elp.ucdavis.edu/tutorial/tutorial.html