1. Remuneration : Rs. 12000/- per month (consolidated)

2. Qualification : M.Sc./M.Tech. Bioinformatics (preference will be given to the candidates with BINC / NET / GATE / Bioinformatics DBT-Traineeship / Studentship and teaching experience)

3. Place of work : Department of Bioinformatics, SVIMS, Tirupati

4. No. of Position : One

Terms and conditions:

1. Candidates are required to submit the Biodata, attested copies of relevant certificates in support of their age, educational qualification, experience etc., before the interview committee, SVIMS University, Tirupati.
2. No TA / DA will be paid for attending the interview.
3. Interim enquiries will not be entertained.
4. The maximum age limit for Teaching Assistant is 34 years as on 8th August, 2015.
5. The tenure of will be further extended subject to performance of the incumbent.

Advertisement: http://svimsbic.org/Teaching_Assistant_B.I._2015.pdf

Applications with complete bio-data from candidates possessing the required qualifications are invited for the posts of JRF (2) and Project Assistant (1) in

DBT, GOI-funded research project “Next Generation Sequencing (NGS)- based de novo assembly of expressed transcripts and genome information of Orchids in North-East India” in DBT’s Twinning programme for NE as per DBT sanction order and norms.

(i) JRF(2 nos.):

Qualifications: M.Tech/M.Sc in Life Sciences/ Botany/ Zoology/Biochemistry/ Biotechnology/ Bioinformatics; Desirable: Aptitude for Bioinformatics and Computer Programming/ Next generation sequencing data analysis

(ii) Project Assistant (1 no.):

Qualifications: Graduation in Science, Desirable: Experience of working in a Life Science/Plant Biotechnology lab. and familiarity with computers and field work viz. collection of samples.

The applications through email bicnehu@gmail.com or post must reach the undersigned
within 15 days from the date of publication of this advertisement. The advertised posts are purely temporary for the duration of the project and subject to availability of the funds from DBT. The appointment does not confer any entitlement or right over the posts for absorption in the University service.

Advertisement: www.nehu.ac.in/Advertisements/BICAdvtPV_200115.pdf

Organiser: Christine Carrington (University of the West Indies - UWI, St. Augustine, Trinidad and Tobago)
Co-organisers: Anne-Mieke Vandamme, Philippe Lemey (Katholieke Universiteit Leuven, Belgium), Marco Salemi, Mattia Prosperi (University of Florida, Gainesville, USA) and Karen E. Nelson (J. Craig Venter Institute, Rockville, USA)

Requests for information directly to:
Christine Carrington
Department of Preclinical Sciences
Faculty of Medical Sciences
University of the West Indies (UWI)
St. Augustine
Trinidad and Tobago
Telephone: +1-868-6452640 ext. 5009, +1-868-6848803
Fax: +1-868-6621873
E-mail: veme2015@sta.uwi.edu

Deadline for receipt of applications by local organiser: 15 March 2015
CALL FOR APPLICATIONS NOW OPEN
http://www.icgeb.org/course-application-trinidad-and-tobago-2015.html

http://rega.kuleuven.be/cev/veme-workshop/2015

Qualification required: PhD in Life Sciences/ Molecular Biology/ Bioinformatics or related discipline.

Desirable Qualification:

1. Candidates having experience in molecular biology techniques and must be well versed with bioinformatics tool or expert in relevant fields will be preferred.
2. The candidate should have good communication skill, knowledge about designing experiments and analyzing data.
3. Sense of initiative, autonomy, organization and thoroughness, good relation qualities.
Remuneration: 2000USD per month.

Last date of submitting the complete applications furnishing Bio-data is 10th February, 2015.

Interested candidates should send their resume including all personal as well as academic details to the principal investigator Prof Seung-Won Park (email:microsw@cu.ac.kr)

The shortlisted candidates will be intimated within February 2015.

Installation

To download the development version of pacman:

Download the zip ball or tar ball, decompress and run R CMD INSTALL on it, or use the devtools package to install the development version:

## Make sure your current packages are up to date
update.packages()
## devtools is required
devtools::install_github("trinker/pacman")

Note: Windows users need Rtools and devtools to install this way.

More at https://github.com/trinker/pacman

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

A postdoctoral training position is currently available in the Computational and Statistical Genomics Branch (CSGB) of the National Human Genome Research Institute (NHGRI). The position is located in the laboratory of Andy Baxevanis, Ph.D., whose research group uses comparative genomics approaches to better-understand the molecular innovations that drove the surge of diversity in early animal evolution. The overarching theme of Dr. Baxevanis’ research program is focused on how non-traditional animal models convey critical insights into human disease research.

Candidates should have or be close to obtaining a Ph.D. or equivalent degree in bioinformatics, computational biology, computer science, molecular biology, or a closely related field. Candidates with a background in evolutionary biology are particularly encouraged to apply. Programming skills and experience in the application of computational methods to genomic data are highly desirable. Applicants must possess good communication skills and be fluent in both spoken and written English. The ability to learn how to use new software and quickly become expert in its use, critical thinking, problem-solving abilities, and the ability to work semi-independently are required.
How to Apply

Interested applicants should submit a curriculum vitae, a detailed letter of interest, and the names of three potential referees to Dr. Baxevanis at andy@mail.nih.gov.
About Our Organization

The NIH Intramural Research Program is on the Bethesda, Maryland campus and offers a wide array of training opportunities for scientists early in their careers. The funding for this position is stable and offers the trainee wide latitude in the design and pursuit of their research project. The successful candidate will have access to NHGRI’s established and robust bioinformatics infrastructure, as well as resources made available through NIH’s Center for Information Technology (CIT) and the National Center for Biotechnology Information (NCBI).

For more information on CSGB and NHGRI’s Intramural Research Program, please see http://genome.gov/DIR/.