We are looking for highly motivated and bright individuals interested in high-throughput cutting-edge data analyses methods in genomics (computational positions). Available positions:

Applications are invited from suitable candidates in both, the Max Planck India Partner Program and the CRP Wheat Program for openings at the levels:

Minimum qualification Salary scale (per month)

Project assistant Bachelor’s / Master’s Rs. 10000 / Rs. 14000

Project fellow (junior data analyst) Masters + research experience Rs. 16000

Research fellow (senior data analyst) Masters + adequate research experience/desirable skill sets Rs. 22000

Research Associated PhD (<1yr) / > 1 yr experience Rs. 28000 / Rs. 32000

Condition to satisfactory performance, availability of funds and requirements of the project, the positions could be available upto a period of ~2 years (or funding of the project).

Essential qualification: MSc/BTech/MTech/PhD (or other suitable qualification) in discipline related to bioinformatics, computational biology, computer application (or equivalent)/ ‘Advance PostGraduate Diploma in Bioinformatics’. Proficiency in one of the programming languages or statistics (proficient in R for example) is compulsory.

Desirable qualification:

1. Programming experiences in at least one low level language such as C/C++ and one scripting language such as Perl/Python/PHP and knowledge of SQL/MySQL.

2. Substantial experience in the linux or other unix environments.

Application process: Applications should contain CV along with brief description (maximum 1 page) of research conducted (highlighting skills and experience) till now. Applications should be sent by email to Shree P. Pandey, Department of Biological Sciences, IISER-Kolkata, Mohanpur Campus, West Bengal within 2 weeks (Feb 19th 2015). E-mail: sppiiserkol@gmail.com, sppandey@iiserkol.ac.in Brief description of the group: We are an interdisciplinary group focusing on small-RNA (miRNA, siRNA) mediated regulation of signaling and defense. Project equally involve bioinformatics and systems biology (specially microarrays and next-generation sequencing (NGS) data analysis and its use), along with plant molecular biology, genetic engineering, field biology, and analytical plant chemistry for understanding response of plants to biotic stresses. For more details visit: http://www.iiserkol.ac.in/~sppandey/

Advertisement:

www.iiserkol.ac.in/images/iiserk/advertisements/advertisement_7_spp_feb_2015.pdf

nf-core/mag v5 (https://github.com/nf-core/mag) addresses this challenge by extending its reproducible Nextflow-based workflow for modern genome-resolved metagenomics.

A key addition is support for long-read-only metagenomic assembly and bin refinement, enabling long-read data to be processed through an integrated workflow. The release also introduces five additional binning tools, providing complementary strategies for recovering genomes from complex microbial communities.

The workflow has also expanded beyond conventional bacterial and archaeal MAGs, with improved classification of viruses and eukaryotes, together with enhanced genome-quality assessment.

Conceptually, the workflow brings together:

Reads → QC → Assembly → Binning → Bin refinement → Taxonomic classification → MAG quality assessment

The real strength of nf-core/mag is not any single algorithm, but the integration and reproducibility of multiple tools within a standardized workflow. This is particularly important for large-scale metagenomic studies where software versions, parameters, databases and computational environments can strongly influence results.

After seven years of development involving multiple curator teams and the wider nf-core community, v5 demonstrates how community-driven workflow development can keep metagenomic analysis aligned with rapidly evolving sequencing technologies.

The future of genome-resolved metagenomics is not simply longer reads—it is better integration of assembly, binning, refinement and quality control within reproducible computational workflows.

Read the full paper in Bioinformatics https://academic.oup.com/bioinformatics/article/42/9/btag628/8770536?

Name of the Post: Research Associate
No. of the Post: 01 One
Age Limit: Max. 35 years
Salary: Rs. 22000/- per month plus HRA

Required Job Profile:
Candidate must possess M.Sc. in bioinformatics or biotechnology from recognized university or institute.
Desired Job Profile;
Candidate having Ph.D. or pursuing Ph.D. in the related subject or equivalent published work in reputed peer reviewed journals or advance PG dipoma in bioinformatics course.

How to apply:
Eligible and interested candidates should need to send the bio-data and bring all related documents in original and set of attested copies of the same in the time of interview.

Last date: 16.02.2015
Refer to http://www.nehu.ac.in/Advertisements/BIFTuraAdvt_221214.pdf

Summary
Employer Address: Dr.B.K. Mishra Coordinator BIF, RDAP Department, North Eastern Hill University, Tura Campus, Tura, Meghalaya
Email: drbkm1972@yahoo.co.in;birendramishra14@gmail.com
URL: http://www.nehu.ac.in/Advertisements/BIFTuraAdvt_221214.pdf
Phone: 03651-223107
Required Skills: not mentioned / required for this post
Required Experience: not mentioned / required for this job post
Required Education: M.Sc. in bioinformatics or biotechnology from recognized university or institute.
Job Location: Tura, Meghalaya, India

To address the challenges of describing and estimating virodiversity, a team of investigators from Center for Infection and Immunity (CII) and EcoHealth Alliance began in jungles of Bangladesh - home to the flying fox.

Reference:

http://economictimes.indiatimes.com/news/news-by-industry/et-cetera/mammals-harbour-at-least-320000-new-viruses/articleshow/22253268.cms

http://www.bbc.co.uk/news/science-environment-23932400

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

Now a day, geneticists have developed a language of their own. They are pouring lots of money and energy to read the entire genomic text and understand the gods own code ATGC. It is somewhat fascinating, not only for geneticist but also for non-biologist to know that there are several conserved blocks in genome which remain conserved over hundreds of millions of years. There have been several researches on conserved blocks and non-conserved regions to understand the mechanism and importance of all these regions (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2675965/). The finding indicates conservation and rearrangements of certain evolutionary important genes play an important role in evolution/adaptive changes (http://www.nature.com/nature/journal/v491/n7424/abs/nature11622.html https://academic.oup.com/gbe/article/8/8/2442/2198198/Novel-Insights-into-Chromosome-Evolution-in-Birds , http://science.sciencemag.org/content/346/6215/1311).

But the puzzle remains open, how to correctly define the synteny (presence of two or more genes on the same chromosome) and conserved synteny (presence of two or more genes on chromosome of each of the two species) on several genomes.

Figure: Image generated with Evolution Highway (EH) tool http://eh-demo.ncsa.illinois.edu/ 

Keeping the new approach to define conserved synteny in mind there have been various algorithms developed to identify the conserved homologous synteny blocks (HSB) amongst species. Some of them which were commonly used for synteny detections are:

SyntenyTracker ( http://www-app.igb.uiuc.edu/labs/lewin/donthu/Synteny_assign/html/),

SyntenyTracker was shown to be an efficient and accurate automated tool for defining HSBs using datasets that may contain minor errors resulting from limitations in map construction methodologies.

CoGe (http://genomevolution.org/CoGe/SynFind.pl )

Satsuma (http://evomics.org/learning/genomics/satsuma/)

Cinteny (http://cinteny.cchmc.org/) ,

Cinteny server can be used for finding regions syntenic across multiple genomes and measuring the extent of genome rearrangement using reversal distance as a measure.

OrthoCluster (http://krono.act.uji.es/noticias/orthocluster-a-new-tool-for-mining-syntenic-blocks)

A new tool for mining syntenic blocks in comparative genomics

SynMap (http://genomevolution.org/wiki/index.php/SynMap),

SyMAP (http://www.symapdb.org/)

SyMAP (Synteny Mapping and Analysis Program) v4.0 is an automated system for identifying and displaying genome synteny alignments. The genomes may be represented by sequenced chromosomes (pseudomolecules), by draft sequence contigs, or by FPC physical maps (with BAC-end or marker sequence).

http://genomevolution.org/CoGe/SynMap.pl

RegionMiner (http://www.genomatix.de/online_help/help_regionminer/orthologous.html)

SyntenyMiner is being developed as an application to visualize and interrogate comparisons among multiple complete genome sequences. http://syntenyminer.sourceforge.net/

AutoGRAPH ( http://autograph.genouest.org/),

AutoGRAPH is an integrated web server for multi-species comparative genomic analysis. It is designed for constructing and visualizing synteny maps between two or three species, determination and display of macrosynteny and microsynteny relationships among species, and for highlighting evolutionary breakpoints.

SynChro(http://www.lgm.upmc.fr/CHROnicle/SynChro.html)

SynChro is a tool designed to define conserved synteny blocks. It reconstructs synteny blocks between pairwise comparison of multiple genomes. The reconstructed synteny blocks may overlap each other, be included in one another or duplicated due to micro-rearrangements.

SyntenyView ( http://www.cbs.dtu.dk/dtucourse/cookbooks/nikob/exercises/gf1_output_5.html),

Ensembl 'SyntenyView' shows conservation of large-scale gene order between species pairs. A brief summary of the calculation method appears at the bottom of this help page.  The left of a 'SyntenyView' page displays a diagram of chromosomes with blocks of conserved synteny. The right of a page shows homology matches between individual genes within syntenic blocks.

SynBrowse ( http://www.synbrowse.org/),

SynBrowse (Synteny Browser) is a generic sequence comparison tool for visualizing genome alignments both within and between species. It is intended to help scientists study and analyze synteny, homologous genes and other conserved elements between sequences. This software is useful in studying genome duplication and evolution. It can also aid in identifying uncharacterized genes, putative regulatory elements and novel structural features of study species by comparing to a well annotated reference sequence, thus enabling genome curators to refine and edit annotations of species that have incomplete genome annotations.

Sibelia (http://arxiv.org/abs/1307.7941).

A comparative genomic tool: It assists biologists in analysing the genomic variations that correlate with pathogens, or the genomic changes that help microorganisms adapt in different environments. Sibelia will also be helpful for the evolutionary and genome rearrangement studies for multiple strains of microorganisms.

GSV (http://cas-bioinfo.cas.unt.edu/gsv/homepage.php)

Genome Synteny Viewer allows users to upload files which contain synteny regions between two or more genomes and interactively visualize the synteny between them. GSV also allows users to upload annotation files to visualize annotated regions in addition to synteny regions.

MicroSyn (http://www.lgm.upmc.fr/CHROnicle/SynChro.html)

MicroSyn software as a means of detecting microsynteny in adjacent genomic regions surrounding genes in gene families. MicroSyn searches for conserved, flanking colinear homologous gene pairs between two genomic fragments to determine the relationship between two members in a gene family.

SynOrth (http://synorth.genereg.net/)

Synorth [s n ôrth], named in combination of "synteny" and "ortholog", is designed for the study of evolutionary changes of genomic regulatory blocks (GRBs) in vertebrate genomes, and especially the changes following the whole-genome duplication in teleost fish, by tracing the ortholog genes gain and loss in ancient synteny blocks.

SyDiG (http://www.ncbi.nlm.nih.gov/pubmed/21441096)

Uncovering Synteny in Distant Genomes.

MapSynteny  (http://www.automatizacionysistemas.com/download.html)

MapSynteny is a macro in MS Excel® able to create images to show the relationship between genetic maps and large sequences (scaffolds, chromosomes, BACs, etc.). Based on tab – delimited BLAST results and some formulas, a suitable image of syntenic relationships or physical mapping can be obtained. http://www.automatizacionysistemas.com/Poster_MapSynteny.pdf

One of the best synteny tutorial for beginer @ http://www.nature.com/scitable/topicpage/synteny-inferring-ancestral-genomes-44022

Reference:

http://www.nature.com/scitable/topicpage/synteny-inferring-ancestral-genomes-44022

http://www.nature.com/nature/journal/v491/n7424/full/nature11622.html

http://en.wikipedia.org/wiki/Synteny

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2675965/

Young Professional II (Computer Science/Applications)
Master degree in Computer Science/Computer Applications/B.Tech (Computer Science) or equivalent.
Desirable: 1. Knowledge of Statistical and Computational Genomics/ Proteomics/ Bioinformatics/Data mining tools. 2. Experience in handling HPC, programming languages and database management packages.
A consolidated salary of Rs.25,000/- per month.
21 to 45 year

Young Professional II (Biotechnology/ Bioinformatics)
Master degree in Bioinformatics/ Biotechnology/ B. Tech(Biotech) or equivalent. Desirable: 1. Knowledge of Computational Genomics/Proteomics/Bioinformatics. 2. Expertise in NGS data analysis and knowledge of allied software and tools.
A consolidated salary of Rs.25,000/- per month.

Senior Research Fellow
1. Bachelors degree with Zoology, Fisheries and 2. Master's degree in Fishery science/ Zoology with Fisheries/ Biotechnology/ Life Sciences with specialization in Fisheries/ Molecular Biology. 3. 1 st Division or 60% marks or equivalent overall grade point average.
Desirable: Work experience in Fisheries, molecular research techniques, bioinformatics and Computer skills. NET qualified
Note: The project involves extensive exploration tours and sampling from water bodies all over India
Rs.16,000/- p.m. for 1st & 2nd year and `18,000/- p.m. for 3rd and subsequent years +HRA (as per rules) 35 years for male and 40 years for female candidate

How to apply

A walk-in-interview will be held on 04th March, 2015 at 10:00 hrs at National Bureau of Fish Genetic Resources, Lucknow. Eligible and desirous candidates fulfilling all the requirements may appear for the interview with duly filled in application giving full details of academic records and experience(s) along with attested photocopy as well as original copy of the relevant documents and a passport size photograph on the attached proforma.

http://www.nbfgr.res.in/

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

Please report new whole genome alignment tools under comment sections.

Address of the bookmark: http://www.cs.utoronto.ca/~brudno/721.full.pdf