BOL: Related items

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 :- R Syntax

Archana Malhotra — Fri, 20 Feb 2015 23:41:03 -0600

R is a functional based language, the inputs to a function, including options, are in brackets. Note that all dat and options are separated by a comma

Function(data, options)

Even quit is a function

So is help

help(read.table)

Provides the help page for the FUNCTION ‘read.table’

help.search(“t test”)

Searches for help pages that might relate to the phrase ‘t test’

NOTE: quotes are needed for search strings, they are not needed when referring to data objects or function names.

There is a short cut for help,

? shows the help page on a function name, same as help(function)

?read.table

?? searches for help pages on functions, same as help.search(‘phrase’)

??“t test”

Information is usually returned from a function, by default this is printed to screen

read.table(‘data.tsv’)

This can always be stored, we call what it is stored in an ‘object’

mydata

here mydata is an object of type dataframe

Reminder:

Vector: a list of numbers, equivalent to a column in a table
Data Frame = a collection of vectors. Equivalent to a table

Hint:

Up/Down arrow keys can be use to cycle through previous commands

BINC Sample Question Paper !!!

Jitendra Narayan — Thu, 16 Apr 2015 09:14:14 -0500

BINC sample question paper round TWO.

Perl One liner basics !!

Abhimanyu Singh — Sun, 24 May 2015 09:28:33 -0500

Perl has a ton of command line switches (see perldoc perlrun), but I'm just going to cover the ones you'll commonly need to debug code. The most important switch is -e, for execute (or maybe "engage" :) ). The -e switch takes a quoted string of Perl code and executes it. For example:

$ perl -e 'print "Hello, World!\n"'
Hello, World!

It's important that you use single-quotes to quote the code for -e. This usually means you can't use single-quotes within the one liner code. If you're using Windows cmd.exe or PowerShell, you must use double-quotes instead.

I'm always forgetting what Perl's predefined special variables do, and often test them at the command line with a one liner to see what they contain. For instance do you remember what $^O is?

$ perl -e 'print "$^O\n"'
linux

It's the operating system name. With that cleared up, let's see what else we can do. If you're using a relatively new Perl (5.10.0 or higher) you can use the -E switch instead of -e. This turns on some of Perl's newer features, like say, which prints a string and appends a newline to it. This saves typing and makes the code cleaner:

$ perl -E 'say "$^O"'
linux

Pretty handy! say is a nifty feature that you'll use again and again.

Ryan E. Mills Lab

Tue, 26 May 2015 09:29:24 -0500

Our research group is primarily focused on the analysis of whole genome sequence data to identify genetic variation (primarily structural variation) and examine their potential functional impact in disease phenotypes. We are particularly interested in analyzing complex regions of the genome that are not easily resolved through modern sequencing approaches and which may exhibit interesting mechanistic origins.

We are also interested in the large-scale integration of genomic, expression, methylation and proteomic data sets, as well as the application of whole genome sequence analysis in clinical diagnostics.

More at http://millslab.ccmb.med.umich.edu/index.html

Frequent words problem solution by Perl

Jit — Tue, 09 Jun 2015 23:38:44 -0500

Solved with perl http://rosalind.info/problems/1a/

#Find the most frequent k-mers in a string.
#Given: A DNA string Text and an integer k.
#Return: All most frequent k-mers in Text (in any order).

use strict;
use warnings;

my $string="ACGTTGCATGTCGCATGATGCATGAGAGCT";
my $kmer=4;
my %myHash;
my $max=0;

for (my $aa=0; $aa<=(length($string)-4); $aa++) {
   my $myStr=substr $string, $aa,$kmer;
   #print "$myStr\n";
   my $km=kmerMatch ($string, $myStr, $kmer);
   if ($km > $max) { $max = $km;}
   #print "$km\t$myStr\n";
   $myHash{$myStr}=$km;

}

#Print all key which have matching values
foreach my $name (keys %myHash){
    print "$name " if $myHash{$name} == $max;
}

sub kmerMatch { #Check the exact matching kmers with sliding window
my ($string, $myStr, $kmer)=@_;
my $count=0;
for (my $aa=0; $aa<=(length($string)-4); $aa++) {
   my $myWin=substr $string, $aa,$kmer;
   if ($myWin eq $myStr) {
       #print "$myWin eq $myStr\n";
       $count++;
   }
}
return $count;
}

Clump Finding Problem Solved with Perl

Jit — Wed, 10 Jun 2015 00:17:17 -0500

The question at http://rosalind.info/problems/1d/

Script are moved to http://bioinformaticsonline.com/snippets/view/34633/clump-finding-problem-solved-with-perl

At what age did you gain passion in Bioinformatics?

Jit — Tue, 23 Jun 2015 10:39:06 -0500

Most of the bioinformatician were biologist ( yeah ... not all ;), and at later stage they gain a passion in Bioinformatics and learn it. When did you get inclined towards computational analysis of biological data?

Raphael Lab

Sat, 04 Jul 2015 19:05:29 -0500

Raphael Lab research is focused on Bioinformatics and Computational Biology.

Current research interests include next-generation DNA sequencing, structural variation, genome rearrangements in cancer and evolution, and network analysis of somatic mutations in cancer. Earlier research included topics in comparative genomics, multiple sequence alignment, and motif finding.

More athttp://compbio.cs.brown.edu/

BioRG

Tue, 04 Aug 2015 20:52:52 -0500

This research group works on problems from the fields of Bioinformatics, Biotechnology, Data Mining, and Information Retrieval. The group's research projects includes Comparative Genomics of Bacterial genomes, Metagenomics, Genomic databases, Pattern Discovery in sequences and structures, micro-array data analysis, prediction of regulatory elements, primer design, probe design, phylogenetic analysis, medical image processing, image analysis, data integration, data mining, information retrieval, knowledge discovery in electronic medical records, and more.

More at http://biorg.cis.fiu.edu/