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

https://bioinformatics-core-shared-training.github.io/cruk-summer-school-2017/Day1/Session4-seqIntro.html

Address of the bookmark: https://bioinformatics-core-shared-training.github.io/cruk-summer-school-2017/Day1/Session4-seqIntro.html

No of post : 01
Essential qualifications: i) Ph. D degree in Bioinformatics/computers/Bio-technology. OR ii) Master’s Degree in Bioinformatics/computers/Bio-technology with 1st division or 60% marks or equivalent overall grade point average with at least two years of research experience as evidenced from fellowship/Associateship/training/other engagements.
Desirable qualifications: i) Working Knowledge and Published Research papers in Bio-informatics.
Monthly emoluments : Rs. 23,000/- + HRA . for M.Sc degree holder Rs. 24,000/- + HRA for Ph.D degree holder
Maximum Age limit : Research Associate – Males- 40 years & Women 45 years.
SELECTION PROCEDURE FOR CENTRAL POTATO RESEARCH INSTITUTE (CPRI) – RESEARCH ASSOCIATE POST:

Written Test on 20/03/2015.
Shortlisted candidates will undertake face to face interview.
Dates are yet to be announced for the final selection
WALK-IN PROCEDURE FOR RESEARCH ASSOCIATE VACANCY IN CENTRAL POTATO RESEARCH INSTITUTE (CPRI):

Interested/eligible candidates should submit their application along with the attested copies of educational qualification (provisional degree of Masters and Ph.D is mandatory )/experience certificates and one passport size photograph to the Asstt. Admn. Officer(E-I), CPRI, Shimla-171001 at 9.30 AM on the date of interview. The candidates appearing for interview must bring original certificate with them and only those candidates possessing essential qualification as per advertisement will be interviewed. The Director, CPRI, Shimla reserves the right either to fill up the post or cancel the interview without assigning any reasons thereof. Application form is available in the website ( website: http//cpri.ernet.in). No TA/DA will be given by the Institute to the candidates. The Institute is located at Bemloe which is about 2 Kms from Main Bus Stand(Old)/3 Kms. from the Railway Station and about 5 Kms. from ISBT (Tutikandi).

So far miniasm is in early development stage. It has only been tested on a dozen of PacBio and Oxford Nanopore (ONT) bacterial data sets. Including the mapping step, it takes about 3 minutes to assemble a bacterial genome. Under the default setting, miniasm assembles 9 out of 12 PacBio datasets and 3 out of 4 ONT datasets into a single contig. The 12 PacBio data sets are PacBio E. coli sample, ERS473430, ERS544009, ERS554120, ERS605484, ERS617393, ERS646601, ERS659581, ERS670327, ERS685285, ERS743109 and a deprecated PacBio E. coli data set. ONT data are acquired from the Loman Lab.

For a C. elegans PacBio data set (only 40X are used, not the whole dataset), miniasm finishes the assembly, including reads overlapping, in ~10 minutes with 16 CPUs. The total assembly size is 105Mb; the N50 is 1.94Mb. In comparison, the HGAP3produces a 104Mb assembly with N50 1.61Mb. This dotter plot gives a global view of the miniasm assembly (on the X axis) and the HGAP3 assembly (on Y). They are broadly comparable. Of course, the HGAP3 consensus sequences are much more accurate. In addition, on the whole data set (assembled in ~30 min), the miniasm N50 is reduced to 1.79Mb. Miniasm still needs improvements.

Miniasm confirms that at least for high-coverage bacterial genomes, it is possible to generate long contigs from raw PacBio or ONT reads without error correction. It also shows that minimap can be used as a read overlapper, even though it is probably not as sensitive as the more sophisticated overlapers such as MHAP and DALIGNER. Coupled with long-read error correctors and consensus tools, miniasm may also be useful to produce high-quality assemblies.

Minimap and miniasm are ultrafast tools for (i) mapping and (ii) assembly. Designed for long, noisy reads, they do not have a correction or consensus step, and therefore the resulting assemblies are contiguous (i.e. long) but very noisy (i.e. full of errors)

We start with an all against all comparison:

minimap -Sw5 -L100 -m0 -t8 reads.fq reads.fq | gzip -1 > reads.paf.gz

Then we can assemble

miniasm -f reads.fq reads.paf.gz > reads.gfa

Convert GFA to FASTA:

awk '/^S/{print ">"$2"\n"$3}' reads.gfa | fold > reads.fa

And then count how many contigs:

grep ">" reads.fa | wc -l

# Download sample PacBio from the PBcR website
wget -O- http://www.cbcb.umd.edu/software/PBcR/data/selfSampleData.tar.gz | tar zxf -
ln -s selfSampleData/pacbio_filtered.fastq reads.fq
# Install minimap and miniasm (requiring gcc and zlib)
git clone https://github.com/lh3/minimap && (cd minimap && make)
git clone https://github.com/lh3/miniasm && (cd miniasm && make)
# Overlap
minimap/minimap -Sw5 -L100 -m0 -t8 reads.fq reads.fq | gzip -1 > reads.paf.gz
# Layout
miniasm/miniasm -f reads.fq reads.paf.gz > reads.gfa

Address of the bookmark: https://github.com/lh3/miniasm

PRINCIPAL SCIENTIST Pay Band: Minimum pay of `43,000 in the PB-4 of `37400-67000/- + RGP of `10,000/-.

Age: The candidates must not have attained the age of 52 years as on 24.03.2015. There shall be no age limit for the Council’s employees.

ICAR-NATIONAL INSTITUTE OF BIOTIC STRESS MANAGEMENT, RAIPUR (CHHATTISGARH)

57. Principal Scientist (Agricultural Entomology) (Two post)

Qualifications Essential:

(i) Doctoral degree in Agricultural Entomology including relevant basic sciences.

(ii) 10 years experience in the relevant subject out of which at least 8 years should be as Scientist/ Lecturer/Extension Specialist or in an equivalent position in the Pay Band- 3 of `15600-39100 with Grade Pay of `5400/`6000/`7000/`8000 and 2 years as a Senior Scientist or in an equivalent position in the Pay Band- 4 of ` 37400-67000 with Grade Pay of ` 8700/ ` 9000.

(iii) The candidate should have made contribution to research/teaching/extension education as evidenced by published work/innovations and impact.

Desirable:

(i) Experience of using frontiers research tools in management of insect pests of crop plants.

(ii) Evidence of contributions to relevant field through publications/ patents/citation index to suggest a vision/perspective in biotic stress research.

61. Principal Scientist (Bioinformatics) (One post)

Qualifications Essential:

(i) Doctoral degree in Bioinformatics including relevant basic sciences. (ii) & (iii) As in item no. 57 above.

Desirable:

(i) Experience of using bioinformatics for advancement of knowledge and for research on biotic stress management.

(ii) Evidence of contributions to relevant field through publications/patents/citation index to suggest a vision/perspective in biotic stress research.

http://asrb.org.in/administrator/uploads_dir/1424859407english.pdf

https://github.com/ewijaya/slidesort-bpr

Address of the bookmark: https://code.google.com/archive/p/slidesort-bpr/

ICAR-NPTC: Fibre development in flax/linseed.

(Job # 1) Research Associate (one) (Bioinformatics)

Rs.24000+ 30% HRA) for Ph.D. and for M. Sc Rs.23000/‐ (+ 30% HRA)

Ph.D. Degree in Bioinformatics/Molecular Biology/Biotechnology/ Genetics/allied sciences; or M. Sc in Bioinformatics/ Biotechnology/Life Sciences/ allied sciences with 1st division or 60% marks or equivalent overall grade point average with at least two years of research experience as evidenced from Fellowship/ Associate ship. 2 years research experience in bioinformatic data analysis/molecular biology techniques, and high throughput DNA/RNA sequencing, and transcriptome data analysis. Research paper with IF>1 will be desirable

ICAR-NPTC: Shade avoidance/low-light tolerance in rice.

General Terms & Conditions applicable to all the positions:
Age Limit: 35 years max. (5 years relaxation for SC/ST/OBC and woman candidates as per ICAR rules).
The positions are purely temporary, on a contractual basis and are initially offered for one year.
Originals must be shown for verification. 7. Research experience (Experience certificate from previous employer to be attached): I hereby declare that the information provided above is true to the best of my knowledge. Date: Signature

Advertisement:

www.nrcpb.org/sites/default/files/ICAR-NPTC%20DBT%20RA%20SRF%20interview%2024th%20March.pdf

How to use TAREAN:

• Install a local instance of the pipeline using its source code available from bitbucket repository.
• Use  public Galaxy-based server at https://repeatexplorer-elixir.cerit-sc.cz/. The server is provided in frame of the Elixir CZ project and is maintained by CESNET and CERIT-SC. Simple registration is required to use this service.

Development of TAREAN was supported by ELIXIR CZ research infrastructure project (MEYS Grant No: LM2015047).

References

Novak, P., Avila Robledillo, L., Koblizkova, A., Vrbova, I., Neumann, P., Macas, J. (2017) – TAREAN: a computational tool for identification and characterization of satellite DNA from unassembled short reads. Nucleic Acids Res., doi:10.1093/nar/gkx257

Address of the bookmark: https://bitbucket.org/petrnovak/repex_tarean

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/.