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/

Address of the bookmark: http://biobits.org/samtools_primer.html

http://evomics.org/learning/assembly-and-alignment/velvet/

Address of the bookmark: http://evomics.org/learning/assembly-and-alignment/velvet/

http://sfg.stanford.edu/denovo.html

http://sfg.stanford.edu/sequencing.html

http://sfg.stanford.edu/BLAST.html

http://sfg.stanford.edu/denovo.html 

Address of the bookmark: http://sfg.stanford.edu/guide.html

The most used mappers of DNA-seq data are BWA and Bowtie for DNA-Seq data and Tophat, STAR or HISAT for RNA-Seq data. Mappers differ in which options they can take in, how fast and how accurate they are. Bowtie is faster than BWA, but looses some sensitivity (does not map an equal amount of reads to the correct position in the genome).

Address of the bookmark: http://wiki.bits.vib.be/index.php/Mapping_of_NGS_data

Essential Qualifications: Ph.D. (Bioinformatics/ Biophysics/ Biotechnology or any other stream of biological/ physical sciences) with a minimum of two publications in reputed peer reviewed journals in the area of structural bioinformatics or biophysics or biomolecular modeling/ simulation.

Job description: Development of bioinformatics tools and algorithms/software for structure based analysis of biomolecular systems. Programmatic access to major biomolecular databases using APIs Knowledge based prediction and analysis of biomolecular structure, function and interactions. Docking/simulations for inhibitor design.

Desirable Qualifications (Research Associate/s): i) Strong computer programming skills (in Python/PERL/PHP or C++ or object oriented database management systems like MySQL etc or scripting languages under LINUX/UNIX environment).

ii) Extensive experience in computational analysis of biomolecular structure/interactions and usage of advanced biomolecular simulation softwares. iii) Adequate knowledge of major databases, webservers and softwares in the area of biomolecular structure/function and drug design. iv) Familiarity with Parallel Programming environments and experience in usage of high-end HPC clusters.

The candidates must highlight their experience in above mentioned fields/topics in their CV. Initial appointment will be for a period of 1 year, subject to extension after review of performance.

Emoluments: As per DST, GOI norms and commensurate with experience.

More at https://www.iisc.ac.in/positions-open/

Research Interest and Activities: 1. Plant Genetic Transformations
2. Molecular Population Genetics 3. Detection of human and Animals diseases 4. Breast Cancer­specific protein marker 5. Phytochemical 6. Genomic map 7. Bioremediation 8. Tissue Culture.

Web site and links: www.geocity.cbge.com

Now my main interests are in gene regulation, where we work on promoter analysis; non-coding RNA, where miRNAs and structure prediction are the main areas; and protein structure, where the group is working on methods for structure prediction from sequence. To read more about these topics, please see the research pages.

Lab page @ http://wiki.binf.ku.dk/User:Krogh

The overarching goal of our research is to understand how to interpret complex and fascinating messages embedded in genomes.

https://www.lsugenomics.org/