The evolutionary connections between organisms are represented graphically through phylogenetic trees. Due to the fact that evolution takes place over long periods of time that cannot be observed directly, biologists must reconstruct phylogenies by inferring the evolutionary relationships among present-day organisms. 
Application of the techniques that make this possible can be seen in the very limited field of human genetics, such as the ever more popular use of genetic testing to determine a child's paternity, as well as the emergence of a new branch of criminal forensics focused on genetic evidence.
The effect on traditional scientific classification schemes in the biological sciences has been dramatic as well. Work that was once immensely labor- and materials-intensive can now be done quickly and easily, leading to yet another source of information becoming available for systematic and taxonomic appraisal. This particular kind of data has become so popular that taxonomical schemes based solely on molecular data may be encountered. Proponents even claim that taxonomy was previously based on morphology alone, which of course is utter fable.

For additional information on phylogenetics, see list of Phylogenetics Resources on the Internet.

Phylogeny and Reconstructing Phylogenetic Trees: http://aleph0.clarku.edu/~djoyce/java/Phyltree/cover.html
the CBRG and Department of Statistics Phylogeny tutorial: http://www.compbio.ox.ac.uk/tutorials/phylogeny/
TUTORIAL: PHYLOGENETIC ANALYSIS USING PARSIMONY:http://home.cc.umanitoba.ca/~psgendb/GDE/phylogeny/parsimony/phylip.parsimony.html

PHYLIP: http://www.umanitoba.ca/afs/plant_science/psgendb/doc/Phylip/main.html
An Introduction to Molecular Phylogeny: http://bibiserv.techfak.uni-bielefeld.de/gcb04/tutorials/hoef-emden/GCB04Tut.pdf

How to make a phylogenetic tree: http://www.hiv.lanl.gov/content/sequence/TUTORIALS/TREE_TUTORIAL/Treetutorial.html
Phylogenetic Trees: http://cnx.org/content/m11052/latest/
Phylogeny by Ron Shamir: http://www.cs.tau.ac.il/~rshamir/algmb/01/scribe08/lec08.pdf
Introduction to Phylogeny: http://www.utm.edu/departments/cens/biology/rirwin/391/391Phylog.htm
Lecturer notes on Phylogeny: http://www.sbc.su.se/~bens/course_material/phylocourse1/lecture2.pdf
Principles and Practice of Phylogenetic Systematics:http://www.faculty.biol.ttu.edu/Strauss/Phylogenetics/LectureNotes.htm

Inferring phylogenetic trees: http://www.cis.hut.fi/Opinnot/T-61.6070/slides2008/pres_6070.pdf

Lecture Notes

Chapter 1 - The Diversity, Classification, and Evolution of Vertebrates:http://academic.emporia.edu/mooredwi/nathist/chap1.htm

Algorithms for Phylogenetic Reconstructions:http://lectures.molgen.mpg.de/Algorithmische_Bioinformatik_WS0405/phylogeny_script.pdf

Phylogeny.fr is a free, simple to use web service dedicated to reconstructing and analysing phylogenetic relationships between molecular sequences. Phylogeny.fr runs and connects various bioinformatics programs to reconstruct a robust phylogenetic tree from a set of sequences. For more detail : http://www.phylogeny.fr/version2_cgi/index.cgi

A Brief Tutorial on Phylogenetics
http://bioss.ac.uk/~dirk/talks/tutorial_phylogenetics.pdf

A Brief Tutorial on Phylogenetics Human Rabbit Chicken
http://bioss.ac.uk/~dirk/talks/psnup_tutorial_phylogenetics.pdf

Phylogenetic Tree Computation Tutorial Overview
http://pga.lbl.gov/Workshop/April2002/lectures/Olken.pdf

MrBayes: A program for the Bayesian inference of phylogeny
http://golab.unl.edu/teaching/SBseminar/manual.pdf

Web sites providing software for the construction of phylogenetic trees

• BioEdit

• Coelocanth-Fish Out of Time

• Computational Biochemistry Research Group

• Digital Taxonomy

• Hennig 86

• Hyperclean from Bioinformatics Solutions, Inc.

• Memorial University of Newfoundland

• Mr. Bayes

• NONA

• Oxford University Evolutionary Biology Group

• Paleobiology Database

• PAUP

• Phylip Homepage

• Poy

• Sinauer Associates

• TNT-Tree Analysis Using New Technology

• Tree Base

• Treefinder

• Tree-Puzzle

• Tree View-Taxonomy and Systematics Group at Glasgow

• Washington University-List of Phylogeny Software

You can also add your favourite NGS educational material, or workshop tutorial by commenting on this bookmarks for user benefit. 

Understanding the basics of genome sequencing:

Tutorial by Luke Jostins.

http://www.genetic-inference.co.uk/blog/2009/04/basics-sequencing-dna-part-1/

http://www.genetic-inference.co.uk/blog/2009/08/basics-sequencing-dna-part-2/

A window into third-generation sequencing

http://hmg.oxfordjournals.org/content/19/R2/R227.full.pdf

==============================================

NGS data analysis pipelines

• Detecting and annotating genetic variations using the HugeSeq pipeline  DOI: 10.1038/nbt.2134
• NARWHAL, a primary analysis pipeline for NGS data http://bioinformatics.oxfordjournals.org/cgi/content/abstract/28/2/284?etoc
• RseqFlow: Workflows for RNA-Seq data analysis  DOI: 10.1093/bioinformatics/btr441
• ngs_backbone: a pipeline for read cleaning, mapping and SNP calling using Next Generation Sequence  10.1186/1471-2164-12-285
• A framework for variation discovery and genotyping using next-generation DNA sequencing data  PubMed: 21478889
• SNiPlay: a web-based tool for detection, management and analysis of SNPs. Application to grapevine diversity projects  DOI: 10.1186/1471-2105-12-134 Abstract: http://www.biomedcentral.com/1471-2105/12/134/abstract
• WEP: a high-performance analysis pipeline for whole-exome data http://www.biomedcentral.com/1471-2105/14/S7/S11
• DDBJ read annotation pipeline: a cloud computing-based pipeline for high-throughput analysis of next-generation sequencing data. http://www.ncbi.nlm.nih.gov/pubmed/23657089
• GATK: a Toolkit for Genome Analysis http://www.broadinstitute.org/gatk/
• Metagenomics:http://www.nbic.nl/education/nbic-phd-school/course-schedule/ngsmetagenomics/
• RNASeq:http://www.nbic.nl/education/nbic-phd-school/course-schedule/ngsrnaseq/
• Bioinformatics and Seq courses: http://www.isb-sib.ch/training/training-activities-schedule/archive-2013.html
• Variant Detection (Model organism) Advanced tutorial https://docs.google.com/document/pub?id=1CuKkKylVDb03tnN7RSWl5EUzleetn0ctjmvaidPKLxM
• Variant Detection Introductory tutorial https://docs.google.com/document/pub?id=1ZRzrjjOCvtAu3m-IKL-rbJ1f4On60dDL_IEwG7oejdI
• Microbial de novo Assembly for Illumina Data Introductory tutorial https://docs.google.com/document/pub?id=1N3AB9ptISUu4zULqe1kXpVF0BDyGb5f5yzxWSJd_WNM
• RNAseq Differential Gene Expression Introductory tutorial https://docs.google.com/document/pub?id=1KbTiBHtvHLfPRZ39AY3uriazrINA8TJzgjjwn1zPP7Y

" Please add your favourite NGS link below in comment section for the benefit of bioinformatics community ". 

Address of the bookmark: http://chagall.med.cornell.edu/NGScourse/

The tutorial is divided in three main parts:

• In the Sequence part, you will see how to look efficiently for a particular protein sequence, how to blast it against the database of your choice to find homologues, how to perform a multiple alignment of the homologues you've selected and how to edit this alignment.
• The Structure part is about molecular visualization, homology modeling and structural domain prediction.
• In the Function part, you will be introduced to you 3 useful servers to investigate the function of a protein. i.e. finding interactors, co-expressed genes, see a phylogenetic profile, easily access papers citing your gene etc ...

During all the three parts, we will use the S. cerevisiae VPS36 protein as an example.

Address of the bookmark: http://www.mrc-lmb.cam.ac.uk/rlw/text/bioinfo_tuto/introduction.html

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

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/

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

Before you get started be sure to check out:

• devtools Google Group – https://groups.google.com/forum/#!forum/rdevtools
• book on “Advanced R programming” – http://adv-r.had.co.nz/
• GitHub repository – https://github.com/hadley/devtools

Address of the bookmark: https://www.rstudio.com/products/rpackages/devtools/

Address of the bookmark: http://www.homolog.us/Tutorials/index.php