This guide contains three different sets of instructions.  If you use RStudio, you can follow the “Ba-sic Instructions” in Section 2 which involve using RStudio’s interface. 

Address of the bookmark: http://web.mit.edu/insong/www/pdf/rpackage_instructions.pdf

install.packages("devtools")
#It may be necessary to install required as not all package dependencies are installed by devtools:
install.packages(c("dplyr","tidyr","ggplot2","MASS","meta","ggrepel","DT"))
devtools::install_github("PheWAS/PheWAS")
library(PheWAS)

Address of the bookmark: https://github.com/PheWAS/PheWAS

Address of the bookmark: http://www.phytools.org/Cordoba2017/ex/2/Intro-to-phylogenies.html

Address of the bookmark: https://rpubs.com/Devashish13/PCAGenomics

http://funprogramming.org/

#Find all occurrences of a pattern in a string.
#Given: Strings Pattern and Genome.
#Return: All starting positions in Genome where Pattern appears as a substring. Use 0-based indexing.

use strict;
use warnings;

my $string="GATATATGCATATACTT";
my $subStr="ATAT";
my $kmer=length($subStr);

kmerMatch ($string, $subStr, $kmer);

sub kmerMatch { #Check the exact matching kmers with sliding window
my ($string, $myStr, $kmer)=@_;
for (my $aa=0; $aa<=(length($string)-$kmer); $aa++) {
    my $myWin=substr  $string, $aa,$kmer;
    if ($myWin eq $myStr) {
        #print "$myWin eq $myStr\n";
        print $aa;
    }
}
}

With InsideDNA, you can upload and store your own genomic/genetic datasets in a limitless cloud space, and instantly analyze it with a powerful compute instance, without any tool installation or set up hassle.

More at https://insidedna.me/

Address of the bookmark: https://insidedna.me/

Address of the bookmark: http://engr.case.edu/li_jing/papers/00798gpattern.pdf

In this tutorial series article, I will explore features and packages of python which are widely used in the big data, NGS, and bioinformatics. I will also walk through a real biological example which shows NGS data processing with the help of python packages and programming.

Python has a couple of points to recommend it to biologists and scientists specifically:

• It's widely used in the scientific community
• It has a couple of very well designed libraries for doing complex scientific computing (although we won't encounter them in this book)
• It lend itself well to being integrated with other, existing tools
• It has features which make it easy to manipulate strings of characters (for example, strings of DNA bases and protein amino acid residues, which we as biologists are particularly fond of)

In general, following are some of the important features of python which makes it a perfect fit for rapid application development.

• Python is interpreted language so the program does not need to be compiled. Interpreter parses the program code and generates the output.
• Python is dynamically typed, so the variables types are defined automatically.
• Python is strongly typed. So the developers need to cast the type manually.
• Less code and more use makes it more acceptable.
• Python is portable, extendable and scalable.

There are two major Python versions, Python 2 and Python 3. Python 2 and 3 are quite different. This tutorial uses Python 3, because it more semantically correct and supports newer features.

I will post tutorial on daily basis on this page. Check the sub-pages on right side.

BioCircos.js provides SNP, CNV, HEATMAP, LINK, LINE, SCATTER, ARC, TEXT, and HISTGRAMmodules to display genome-wide genetic variations (SNPs, CNVs and chromosome rearrangement), gene expression and biomolecule interactions. BioCircos.js also provides BACKGROUND module to display background and axis circles. Tooltips showing detailed information of SVG elements are also provided.

Demo

Address of the bookmark: http://bioinfo.ibp.ac.cn/biocircos/document/index.html