Basic Galaxy Tutorial

• RNA-Seq tutorial based on Trapnell et al. (2012) Nature Protocols

In this tutorial we cover the concepts of RNA-Seq differential gene expression (DGE) analysis using a very small synthetic dataset from a well studied organism.

Advanced Galaxy Tutorial

• RNA-Seq (Advanced) Tutorial

In this tutorial we compare the performance of three statistically-based differential expression tools:

* CuffDiff

* EdgeR

* DESeq2

Advanced Command Line Tutorial

• Graphical Output with CummeRbund introduces some basic commands using the cummeRbund package of the R programming language

You will need to install R, RStudio and cummeRbund on your PC (explained in the Tutorial). You will learn how to produce graphical output from RNA-Seq analysis previously done using a Cuffdiff analysis.

Variant Detection

Basic Galaxy Tutorial

• Variant Detection tutorial

In this tutorial we cover the concepts of detecting small variants (SNVs and indels) in human genomic DNA using a small set of reads from chromosome 22.

Advanced Galaxy Tutorial

• Variant Detection (Advanced) Tutorial

In this tutorial we compare the performance of three statistically-based variant detection tools:

* SAMtools: Mpileup

* GATK: Unified Genotyper

* FreeBayes

Each of these tools takes as its input a BAM file of aligned reads and generates a list of likely variants in VCF format

Pipelines are for those who are comfortable with using the UNIX command line; and often allow more control over branching and iteration logic.

• WGS/exome GATK-based variant calling pipeline

This is a basic variant-calling and annotation pipeline developed at the Victorian Life Sciences Computation Initiative (VLSCI), University of Melbourne. It is based around BWA, GATK and ENSEMBL and was originally designed for human (or similar) data. The master branch is configured for WGS data; there is an exome branch configured for variant calling in exome data.

To run the pipeline you will need Rubra: https://github.com/bjpop/rubra. Rubra uses the python Ruffus library: http://www.ruffus.org.uk/.

Protocols

• Familial Variant Calling

In this protocol we discuss and outline the process of calling familial related mutations.

• Somatic Variant Calling

In this protocol we discuss and outline the process of identifying somatic variants or mutations.

Assembly

Basic Galaxy Tutorial

• Genome assembly tutorial

In this tutorial we carry out de novo assembly of a microbial genome. We have also written a De novo Genome Assembly for Illumina Data Protocol for a more generic description of the method.

Protocol

• De novo Genome Assembly for Illumina Data

In this protocol we discuss and outline the process of de novo assembly for small to medium sized genomes. Use our Genome assembly tutorial to learn a specific case of using Galaxy to carry out de novo assembly of a microbial genome.

Small RNAs

Basic Galaxy Tutorial

• Quality control for small RNA

This tutorial covers initial steps of the workflow for analysis of short RNA expression such as a quality control of the raw reads, processing of the raw reads for the subsequent analysis and initial quality assessment of the library.

ChIP Seq

Protocol

• ChIP-Seq

In this protocol we discuss ChIP-Seq: a method to analyze the interaction between proteins and DNA.

Amplicons

Protocol

• Amplicon Alignment

In this protocol we discuss and outline the process of aligning custom amplicons using primers for high precision.

Learn Galaxy

Introduction to Galaxy, for those who are very new to Galaxy.

Using Histories and Workflows, for those with some Galaxy knowledge.

The Galaxy project website has many tutorials and screencasts about using Galaxy and the tools, and developing new tools.

Address of the bookmark: https://genome.edu.au/wiki/Learn

More at http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000369

Address of the bookmark: http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000369

Perl one-liners are small and awesome Perl programs that fit in a single line of code and they do one thing really well. These things include changing line spacing, numbering lines, doing calculations, converting and substituting text, deleting and printing certain lines, parsing logs, editing files in-place, doing statistics, carrying out system administration tasks, updating a bunch of files at once, and many more. Perl one-liners will make you the shell warrior. Anything that took you minutes to solve, will now take you seconds!

perl -pe '$\="\n"'   
#double space a file

perl -pe '$_ .= "\n" unless /^$/'
#double space a file except blank lines

perl -pe '$_.="\n"x7'
#7 space in a line.

perl -ne 'print unless /^$/'
#remove all blank lines

perl -lne 'print if length($_) < 20'
#print all lines with length less than 20.

perl -00 -pe ''
#If there are multiple spaces, delete all leaving one(make the file a single spaced file).

perl -00 -pe '$_.="\n"x4'
#Expand single blank lines into 4 consecutive blank lines

perl -pe '$_ = "$. $_"'
#Number all lines in a file

perl -pe '$_ = ++$a." $_" if /./'
#Number only non-empty lines in a file

perl -ne 'print ++$a." $_" if /./'
#Number and print only non-empty lines in a file

perl -pe '$_ = ++$a." $_" if /regex/'
#Number only lines that match a pattern

perl -ne 'print ++$a." $_" if /regex/'
#Number and print only lines that match a pattern

perl -ne 'printf "%-5d %s", $., $_ if /regex/'
#Left align lines with 5 white spaces if matches a pattern (perl -ne 'printf "%-5d %s", $., $_' : for all the lines)

perl -le 'print scalar(grep{/./}<>)'
#prints the total number of non-empty lines in a file

perl -lne '$a++ if /regex/; END {print $a+0}'
#print the total number of lines that matches the pattern

perl -alne 'print scalar @F'
#print the total number fields(words) in each line.

perl -alne '$t += @F; END { print $t}'
#Find total number of words in the file

perl -alne 'map { /regex/ && $t++ } @F; END { print $t }'
#find total number of fields that match the pattern

perl -lne '/regex/ && $t++; END { print $t }'
#Find total number of lines that match a pattern

perl -le '$n = 20; $m = 35; ($m,$n) = ($n,$m%$n) while $n; print $m'
#will calculate the GCD of two numbers.

perl -le '$a = $n = 20; $b = $m = 35; ($m,$n) = ($n,$m%$n) while $n; print $a*$b/$m'
#will calculate lcd of 20 and 35.

perl -le '$n=10; $min=5; $max=15; $, = " "; print map { int(rand($max-$min))+$min } 1..$n'
#Generates 10 random numbers between 5 and 15.

perl -le 'print map { ("a".."z",”0”..”9”)[rand 36] } 1..8'
#Generates a 8 character password from a to z and number 0 – 9.

perl -le 'print map { ("a",”t”,”g”,”c”)[rand 4] } 1..20'
#Generates a 20 nucleotide long random residue.

perl -le 'print "a"x50'
#generate a string of ‘x’ 50 character long

perl -le 'print join ", ", map { ord } split //, "hello world"'
#Will print the ascii value of the string hello world.

perl -le '@ascii = (99, 111, 100, 105, 110, 103); print pack("C*", @ascii)'
#converts ascii values into character strings.

perl -le '@odd = grep {$_ % 2 == 1} 1..100; print "@odd"'
#Generates an array of odd numbers.

perl -le '@even = grep {$_ % 2 == 0} 1..100; print "@even"'
#Generate an array of even numbers

perl -lpe 'y/A-Za-z/N-ZA-Mn-za-m/' file
#Convert the entire file into 13 characters offset(ROT13)

perl -nle 'print uc'
#Convert all text to uppercase:

perl -nle 'print lc'
#Convert text to lowercase:

perl -nle 'print ucfirst lc'
#Convert only first letter of first word to uppercas

perl -ple 'y/A-Za-z/a-zA-Z/'
#Convert upper case to lower case and vice versa

perl -ple 's/(\w+)/\u$1/g'
#Camel Casing

perl -pe 's|\n|\r\n|'
#Convert unix new lines into DOS new lines:

perl -pe 's|\r\n|\n|'
#Convert DOS newlines into unix new line

perl -pe 's|\n|\r|'
#Convert unix newlines into MAC newlines:

perl -pe '/regexp/ && s/foo/bar/'
#Substitute a foo with a bar in a line with a regexp.

Reference/Sources:

http://genomics-array.blogspot.in/2010/11/some-unixperl-oneliners-for.html

http://genomespot.blogspot.com/2013/08/a-selection-of-useful-bash-one-liners.html

http://biowize.wordpress.com/2012/06/15/command-line-magic-for-your-gene-annotations/

http://genomics-array.blogspot.com/2010/11/some-unixperl-oneliners-for.html

http://bioexpressblog.wordpress.com/2013/04/05/split-multi-fasta-sequence-file/

Reference

Cuccuru G1, Orsini M, Pinna A, Sbardellati A, Soranzo N, Travaglione A, Uva P, Zanetti G, Fotia G. (2014) Orione, a web-based framework for NGS analysis in microbiology. Bioinformatics [Epub ahead of print]. [article]

Address of the bookmark: http://orione.crs4.it/

Address of the bookmark: http://pevsnerlab.kennedykrieger.org/php/?q=software

http://rosalind.info/problems/list-view/

Address of the bookmark: http://rosalind.info/problems/list-view/

It is possible to create a vector by typing data directly into R using the combine function ‘c’

x

same as

x

creates the vector x with the numbers between 1 and 5.

You can see what is in an object at any time by typing its name;

x

will produce the output ‘[1] 1 2 3 4 5′

Note that names need to be quoted

daysofweek ← c(‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’);

Usually however you want to input from a file. We have touched on the ‘read.table’ function already.

mydata

Now mydata is a data frame with multiple vectors

each vector can be identified by the default syntax

#if any of these are typed it will print to screen

mydata$V1 mydata$V2 mydata$V3

By default the function assumes certain things from the file

• The file is a plain text file (there are function to read excel files: not covered here)
• columns are separated by any number of tabs or spaces
• there is the same number of data points in each column
• there is no header row (labels for the columns)
• there is no column with names for the rows** [I’ll explain].

If any of these are false, we need to tell that to the function

If it has a header column

mydata header=T also works

Note that there is a comma between different parts of the functions arguments

If there is one less column in the header row, then R assumes that the 1^st column of data after the header are the row names

Now the vectors (columns) are identified by their name

#if any of these are typed it will print to screen

mydata$A mydata$B mydata$C

# Summary about the whole data frame

summary(mydata)

# Summary information of column A

summary(mydata$A)

We can shortcut having to type the data frame each time by attaching it

attach(mydata)

# summary of column B as ‘mydata’ is attached

summary(B)

Two other important options for read.table

If is is separated only by tabs and has a header

mydata

Really useful if you have spaces in the contents of some columns, so R does not mess up reading the columns . However if the columns or of an uneven length it will tell you.

If you know that the file has uneven columns

mydata

This causes R to fill empty spaces in a columns with ‘NA’ .

The last two examples will still work with our file and give the same result as with only headers=T

Graphs

to get an idea of what R is capable of type

demo(graphics)

steps through the examples, and the code is printed to the screen

We will work with simpler examples that have immediate use to biologists.

Remember to get more information about the options to a function type ‘?function’

Histogram of A

hist(mydata$A)

If there was more data we could increase the number of vertical columns with the option, breaks=50 (or another relevant number).

boxplot(mydata)

We can get rid of the need to type the data frame each time by using the attach function

# if not already done so

attach(mydata)

boxplot(mydata$A, mydata$B, name=c(“Value A”, “Value B”) , ylab=“Count of Something”)

same as

boxplot(A, B, name=c(“Value A”, “Value B”) , ylab=“Count of Something”)

Scatter plot

# if not already done so

attach(mydata)

plot(A,B) # or plot(mydata$A, mydata$B)

SAVING an image

Windows users (Rgui) RIGHT click on image and select which you want.

These instructions work for everyone.

You need to create a new device of the type of file you need, then send the data to that device

to save as a png file (easy to load into the likes of powerpoint, also great for web applications.

png(‘filename’)

boxplot(A, B, name=c(“Value A”, “Value B”) , ylab=“Count of Something”)

or to save as a pdf

pdf(‘filename’)

boxplot(A, B, name=c(“Value A”, “Value B”) , ylab=“Count of Something”)

Note

• Nothing will appear on screen, the output is going to the file
• Also it may not be saved immediately but will once the device (or R) is turned quit.

To quit R type

q() # If you save your session, next time you start R, you will have your data preloaded.

Or if you want to remain in R

dev.off() #turns of the png (or pdf etc) device, thus forces the data to save

More at http://www.bx.psu.edu/~rsharris/lastz/

Thesis: http://www.bx.psu.edu/~rsharris/rsharris_phd_thesis_2007.pdf

Address of the bookmark: http://www.bx.psu.edu/~rsharris/lastz/

The OpenCPU JavaScript client library provides the most seamless integration of R and JavaScript available today.

OpenCPU uses standard R packaging to develop, ship and deploy web applications. Several open source example apps are available from Github.

Installing your own OpenCPU server is super easy and only takes a few minutes.

More at https://www.opencpu.org/

The Ensembl comparative genomics resources are one such reference set that facilitates comprehensive and reproducible analysis of chordate genome data. Ensembl computes pairwise and multiple whole-genome alignments from which large-scale synteny, per-base conservation scores and constrained elements are obtained. Gene alignments are used to define Ensembl Protein Families, GeneTrees and homologies for both protein-coding and non-coding RNA genes. These resources are updated frequently and have a consistent informatics infrastructure and data presentation across all supported species. Specialized web-based visualizations are also available including synteny displays, collapsible gene tree plots, a gene family locator and different alignment views. The Ensembl comparative genomics infrastructure is extensively reused for the analysis of non-vertebrate species by other projects including Ensembl Genomes and Gramene and much of the information here is relevant to these projects. The consistency of the annotation across species and the focus on vertebrates makes Ensembl an ideal system to perform and support vertebrate comparative genomic analyses. We use robust software and pipelines to produce reference comparative data and make it freely available.

Database URL: http://www.ensembl.org.

Address of the bookmark: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4761110/