BOL: Related items

Machine learning training and courses in bioinformatics !

Rahul Nayak — Tue, 31 Dec 2019 19:33:07 -0600

Machine learning techniques have been successful in analyzing biological data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. In this class, we will learn basics about probabilistic models and machine learning techniques. We will focus on probabilistic models (Markov models, Hidden Markov models, and Bayesian networks) for biological sequence analysis and systems biology. Other machine learning techniques, such as Naive bayes, neural networks and SVMs will only be covered briefly.

More at http://homes.sice.indiana.edu/yye/lab/teaching/spring2017-I529/

Pangolin tutorial !

Abhi — Fri, 10 Dec 2021 05:58:59 -0600

This is a tutorial for using the Pangolin Web Application. For information on using the command line tool, please visit the command line tool usage page.

https://cov-lineages.org/resources/pangolin/tutorial.html

Address of the bookmark: https://cov-lineages.org/resources/pangolin/tutorial.html

Ruby Language

Jitendra Narayan — Mon, 15 Jul 2013 01:34:26 -0500

Ruby was created by Yukihiro Matsumoto, who wished to create a new language that balanced functional programming with imperative programming

Ruby is a dynamic, reflective, general purpose object-oriented programming language that combines syntax inspired by Perl with Smalltalk-like features. Ruby originated in Japan during the mid-1990s and was initially developed and designed by Yukihiro "Matz" Matsumoto. It was influenced primarily by Perl, Smalltalk, Eiffel, and Lisp.

Ruby supports multiple programming paradigms, including functional, object oriented, imperative and reflective. It also has a dynamic typesystem and automatic memory management; it is therefore similar in varying respects to Python, Perl, Lisp, Dylan, Pike, and CLU.

The standard 1.8.7 implementation is written in C, as a single-pass interpreted language. There is currently no specification of the Ruby language, so the original implementation is considered to be the de facto reference. As of 2010, there are a number of complete or upcoming alternative implementations of the Ruby language, including YARV, JRuby, Rubinius, IronRuby, MacRuby and HotRuby, each of which takes a different approach, with IronRuby, JRuby and MacRuby providing just-in-time compilation and MacRuby also providing ahead-of-time compilation. The official 1.9 branch uses YARV, as will 2.0 (development), and will eventually supersede the slower Ruby MRI.

Ruby Quick Reference
http://www.zenspider.com/Languages/Ruby/QuickRef.html

Ruby Annotation
http://www.w3.org/TR/ruby/

Ruby in Linux Journals
http://www.linuxjournal.com/article/5915

Ruby Documentation: Programming Ruby
http://ruby-doc.org/docs/ProgrammingRuby/

The Top 10 Reasons The Ruby Programming Language Sucks

http://www.slideshare.net/vishnu/the-top-10-reasons-the-ruby-programming-language-sucks

Ruby : The Programmers best friends
http://www.ruby-lang.org/en/

For Ruby Beginners
http://www.squidoo.com/ruby-programming-beginner

Ruby Programming
http://en.wikibooks.org/wiki/Ruby_Programming

Ruby CookBook
http://en.wikibooks.org/wiki/Cookbook:Table_of_Contents

Ruby Programming Challenge for Newbies -
http://rubylearning.com/blog/ruby-programming-challenge-faq/

Common "issues" faced by Ruby Newbies by Chris Strom -
http://japhr.blogspot.com/2009/10/newbie-feedback.html

Books
http://www.sapphiresteel.com/The-Book-Of-Ruby

Free Online Ruby Programming along with many Ruby newbies here -
http://rubylearning.org/class/

Perl in a day !!

Jitendra Narayan — Sat, 10 Aug 2013 21:14:03 -0500

This pdf based tutorial in good resource to understand the basic of Perl in a day

http://ritg.med.harvard.edu/training/perl/RC_Perl_Intro.pdf

R and Bioconductor Tutorial

Jitendra Narayan — Fri, 23 Aug 2013 08:23:59 -0500

This tutorial is intended to introduce users quickly to the basics of R, focusing on a few common tasks that biologists need to perform some basic analysis: load a table, plot some graphs, and perform some basic statistics. More extensive tutorials can be found on the project website and via bioconductor (not covered here).

You can add more tutorial links in comments if found new pages.

Address of the bookmark: http://manuals.bioinformatics.ucr.edu/home/R_BioCondManual

Next generation sequencing(NGS) books

Abhimanyu Singh — Fri, 30 May 2014 04:48:04 -0500

Employing different technologies, the purpose of NGS platform is to decode the identity or modification on the nucleotides. NGS platforms evolve quickly and capture the main stream.

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

R for Microsoft Excel

Jitendra Narayan — Wed, 18 Feb 2015 00:43:27 -0600

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/

A guide for complete R beginners :- Installing R packages

Archana Malhotra — Tue, 24 Feb 2015 20:23:34 -0600

Part of the reason R has become so popular is the vast array of packages available at the cran and bioconductor repositories. In the last few years, the number of packages has grown exponentially!

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 .

Visual machine learning !!!

Jitendra Narayan — Wed, 29 Jul 2015 04:29:13 -0500

In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions.

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/

Genome Browser : GBrowse

Jit — Fri, 19 Feb 2016 09:22:43 -0600

Generic Genome Browser Version 2: A Tutorial for Administrators

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