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/

It was first developed to transfer annotations between different genome assembly versions. However, it can also transfer annotations between strains and even different species, like Plasmodium chabaudi onto P. berghei, between different Leishmania species or Salmonella enterica onto other Salmonella serotypes. RATT is able to transfer any entries present on a reference sequence, such as the systematic id or an annotator's notes; such information would be lost in a de novo annotation.

More at http://ratt.sourceforge.net/

Address of the bookmark: http://ratt.sourceforge.net/

• Automatically improve draft assemblies
• Find variation among strains, including large event detection

Pilon requires as input a FASTA file of the genome along with one or more BAM files of reads aligned to the input FASTA file. Pilon uses read alignment analysis to identify inconsistencies between the input genome and the evidence in the reads. It then attempts to make improvements to the input genome, including:

• Single base differences
• Small indels
• Larger indel or block substitution events
• Gap filling
• Identification of local misassemblies, including optional opening of new gaps

More at https://github.com/broadinstitute/pilon/wiki

Address of the bookmark: https://github.com/broadinstitute/pilon/wiki

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 course Bioinformatics Algorithms (Part 1) by Pavel Pevzner, Phillip E. C. Compeau, and Nikolay Vyahhi from University of California, San Diego will be offered free of charge to everyone on the Coursera platform. Sign up at http://www.coursera.org/course/bioinformatics.

Lab page @ http://krishna.gs.washington.edu/index.html

(A) Regulatory System Analysis with respect to microRNAs

(B) Computational Epigenomics & Regulomics:

(C) Computational issues with Next Generation Sequencing:

Department of Biotechnology,
Institute of Himalyan Bioresources Technology
CSIR, Palampur(Himachal Pradesh), India.
Email: ravishihbt.res.in; ravish9gmail.com

More @ http://scbb.ihbt.res.in/SCBB_dept/Lab_Member.php

More @ http://www.bioinf.manchester.ac.uk/dbbrowser/index.php

1. Project Junior Research Fellow / Project Assistant

Last Date: 11th Nov 2013

Qualification B.Tech (Comp. Sci.), B.Tech/M.Tech (Bioinformatics), MCA, M.Sc. (Mathematics/Statistics)

Desirable Qualifications: Programming in FORTRAN/ C /PERL, Web application technologies

Upper Age limit 28

Rs.12000 / Rs.16000 (as sanctioned by the funding agency)

General terms and conditions:

Positions are purely temporary and co-terminus with the project.

HRDG (CSIR) prevailing guidelines are applicable these positions.

All categories of applicants are required to submit online application.

Enhancement of stipend to Project JRF to Project SRF will be with the due recommendation of Principal Investigator and approval of the Director on the evaluation of the 3 member Standing Committee consisting of Chairperson at the level of Chief Scientist, Coordinator of the JRFs/RAs/PDFs and the Principal Investigator of the Project.

The age relaxation as per HRDG (CSIR) norms: SC/ST/OBC/Women/Physically Handicapped persons – five years.

The Stipend normally be fixed at Rs.22000/- for Research Associates/Post Doc. Fellows. However, a selected RA/PDF may be placed in the higher start of stipend if there is ample justification and such recommendation is made by the Selection Committee. Based on the recommendation with justification by the PI and approval of the Director, person getting stipend at lower rate may be elevated to higher rate subject to availability of the funds in the project.

Recruitment will be based on initial screening based on qualifications and experience criteria and also based on suitability of the candidates to the nature of research project. This screening will be followed by written test followed / interview. After completing this process, candidates will be shortlisted and appointed in specific project subjects as and when appropriate positions become available. The pool of selected candidates will be valid for six months.

Remunerations indicate are maximum admissible and will depend upon the availability of funds and subject to conditions applicable to projects from different funding agencies at the time of recruitment.

Apply : http://www.ccmb.res.in/positions/projects/temp_positions.php

Form download : http://www.ccmb.res.in/positions/projects/oct-2013/pdf_download.php