No. CSE/14/1038

Walk in Interview for the post of JRF under TEQIP-II

SN Department – Qualification Post Graduation – Time

1 Bio-Informatics & Mathematics M.Tech Bio-informatics/M.Sc.* Maths 10.00 AM

2 Biological Sciences M.Sc.* in any branch of Biological Sciences 10.30 AM

3 Chemical Engineering M.Tech Chemical Engineering 11.00 AM

4 Chemistry M.Sc.* Chemistry 11.30 AM

5 Civil Engineering M.Tech Structure/GeoTech. /Water -Resources/Hydraulics/Environment/Transport 12.00 Noon

6 GIS M.Tech GIS/Civil 12.30 PM

7 Computer Science & Engineering M.Tech CSE/Information Security 01.00 PM

8 Electrical Engineering M.Tech Electrical Derives 01.30 PM

9 Electronics & Communication M.Tech Digital Communication 02.00 PM

10 MSME M.Tech Material Science/ Mechanical/Metallurgy 02.30 PM

11 Physics M.Sc.* Physics 03.00 PM

* M.Sc. with NET/GATE qualified

Resume along with one passport size photograph and relevant documents are required at the time of interview

Amount of Fellowship: Rs 18000/-month+ HRA

Duration: 31st Dec 2014 (End of TEQIP-II project)

Date of Interview: 7th February 2014

Venue Institute Committee Room

Advertisement:

http://www.manit.ac.in/manitbhopal/Year2014/Recruitment/Advertisement%20JRF.pdf

ABySS Explorer

Interactive Java application that uses a novel graph-based representation to display a sequence assembly and associated metadata

http://www.bcgsc.ca/platform/bioinfo/software/abyss-explorer

BamView

Genome browser and annotation tool that allows visualization of sequence features, next-generation sequencing (NGS) data and the results of analyses within the context of the sequence, and also its six-frame translation

http://www.sanger.ac.uk/resources/software/artemis/

DNannotator 

Annotation web toolkit for regional genomic sequences

http://bioapp.psych.uic.edu/DNannotator.htm

JVM 

Java Visual Mapping tool for NGS reads

http://www.springer.com/cda/content/document/cda_downloaddocument/9789401792448-c2.pdf?SGWID=0-0-45-1487072-p176815501

LookSeq 

Web-based visualization of sequences derived from multiple sequencing technologies. Low- or high-depth read pileups and easy visualization of putative single nucleotide and structural variation

http://lookseq.sourceforge.net

MagicViewer 

Visualization of short read alignment, identification of genetic variation and association with annotation information of a reference genome

http://bioinformatics.zj.cn/magicviewer/

MapView 

Alignments of huge-scale single-end and pair-end short reads

http://omictools.com/mapview-s1367.html

MultiPipMaker

Computes alignments of similar regions in two DNA sequences. The resulting alignments are summarized with a ‘percent identity plot’ (pip)

http://pipmaker.bx.psu.edu/pipmaker/

PileLineGUI 

Handling genome position files in NGS studies

http://sing.ei.uvigo.es/pileline/pilelinegui.html

SAMtools tview 

Simple and fast text alignment viewer; NGS compatible

http://www.htslib.org/

SEWAL

Uses a locality-sensitive hashing algorithm to enumerate all unique sequences in an entire Illumina sequencing run

http://www.sourceforge.net/projects/sewal

STAR 

A web-based integrated solution to management and visualization of sequencing data

http://wanglab.ucsd.edu/star/browser

SVA 

Software for annotating and visualizing sequenced human genomes

http://www.svaproject.org

Viewer (IGV) 

Visualization of large heterogeneous datasets, providing a smooth and intuitive user experience at all levels of genome resolution

https://www.broadinstitute.org/igv/

ZOOM Lite 

NGS data mapping and visualization software

http://bioinfor.com/zoom/lite/

Qualifications

PhD or Post-Graduated Master degree is required. Successful candidates will have some expertise in data analysis of NGS data by using methods running in R and UNIX environment. Familiarity with genome databases and browsers is required.

Application

Candidates should send a CV and a brief personal statement focusing on their skills and interests related to the research project.

Contacts

Start date: 1° April 2014
Salary on grant: 25,000 euros per year.
Contact Person (Referent): Mario Capasso
Ref. Email: mario.capasso@unina.it and achille.iolascon@unina.it
Tel: +39 081 3737889

Porechop also supports demultiplexing of Nanopore reads that were barcoded with the Native Barcoding Kit, PCR Barcoding Kit or Rapid Barcoding Kit.

Address of the bookmark: https://github.com/rrwick/Porechop

If you have a license key for Modeller 8 or 9, there is no need to reregister for Modeller 9.13 - the same license key will work. (It won't do any harm to reregister if you want to, though!)

9.13 is primarily a bugfix release relative to the last public release(9.12). Major user-visible changes include:

# Modeller now includes a variety of SOAP (statistically optimized atomic potential) scores for assessing proteins, loops, and interfaces.

# The Lennard-Jones interaction energy is now artificially truncated at very short distance; this makes simulations with poor starting conditions much less likely to 'blow up'.

# model.get_insertions(), model.get_deletions() and model.loops() now have an include_termini option; if False, residue ranges that include chain termini are excluded from the output.

See the Modeller manual for a full change log: http://salilab.org/modeller/9.13/manual/node39.html

If you encounter bugs in Modeller 9.13, please see http://salilab.org/modeller/9.13/manual/node10.html for information on how to report them.

Reference:

http://salilab.org/modeller/

• hist: Create an histogram of k-mer occurrences from a sequence file. Adds metadata in output for easy plotting.
• gcp: K-mer GC Processor. Creates a matrix of the number of K-mers found given a GC count and a K-mer count.
• comp: K-mer comparison tool. Creates a matrix of shared K-mers between two (or three) sequence files or hashes.
• sect: SEquence Coverage estimator Tool. Estimates the coverage of each sequence in a file using K-mers from another sequence file.
• blob: Given, reads and an assembly, calculates both the read and assembly K-mer coverage along with GC% for each sequence in the assembly.SEquence Coverage estimator Tool.
• filter: Filtering tools. Contains tools for filtering k-mer hashes and FastQ/A files:
  • kmer: Produces a k-mer hash containing only k-mers within specified coverage and GC tolerances.
  • seq: Filters a sequence file based on whether or not the sequences contain k-mers within a provided hash.
• plot: Plotting tools. Contains several plotting tools to visualise K-mer and compare distributions. The following plot tools are available:
  • density: Creates a density plot from a matrix created with the "comp" tool. Typically this is used to compare two K-mer hashes produced by different NGS reads.
  • profile: Creates a K-mer coverage plot for a single sequence. Takes in fasta coverage output coverage from the "sect" tool
  • spectra-cn: Creates a stacked histogram using a matrix created with the "comp" tool. Typically this is used to compare a jellyfish hash produced from a read set to a jellyfish hash produced from an assembly. The plot shows the amount of distinct K-mers absent, as well as the copy number variation present within the assembly.
  • spectra-hist: Creates a K-mer spectra plot for a set of K-mer histograms produced either by jellyfish-histo or kat-histo.
  • spectra-mx: Creates a K-mer spectra plot for a set of K-mer histograms that are derived from selected rows or columns in a matrix produced by the "comp".

In addition, KAT contains a python script for analysing the mathematical distributions present in the K-mer spectra in order to determine how much content is present in each peak.

This README only contains some brief details of how to install and use KAT. For more extensive documentation please visit: https://kat.readthedocs.org/en/latest/

https://academic.oup.com/bioinformatics/article/33/4/574/2664339 

Address of the bookmark: https://github.com/TGAC/KAT

Bioinformatician are not anti-social; We are just genome friendly.

Bioinformatician would love to change the biological world, but they won't give us the genetic code :P

If at first you don't succeed; call it version 1.0

The glass is neither half-full nor half-empty: it's actually have several genomes.

I'm BioGeek.

Fedup with LIPS, try God script.

Idiot, Go ahead, make my data!

Thank god, my genome just compiled.

Error message: "Out of space on genome drive:"

Shut up mobile elements, or i'll flush you out.

Never underestimate the internet bandwidth, u gotta incomplete.

Applied fuzzy logic to understand God's logic?

Warning! Overflow, delete chromosome !

Be nice to the BioGeek, for all you know they might be the next curator!

Beware of computational biologist they screw genes and protein.

Warning! Your genome is full of garbage, delete it !

Bad or missing mouse genome. Spank the cat? (Y/N)

Genome make very fast, very accurate mistakes.

Let's BLAST it.

Some genome never has transposons. It just develops random features.

Go watch CINEMA and have BLAST.

https://f1000research.com/articles/6-100/

https://github.com/jason-weirather/AlignQC

Address of the bookmark: https://www.healthcare.uiowa.edu/labs/au/AlignQC/

1. Update new things continuously
As per my personal experience, it’s not always easy to work as a bioinformatician!  There are couple of reasons to say that; First computational part of biology make our life’s a little harder compared to other professional categories. The fact - for instance - that the technology cycle in the bioinformatics world is very short, the actual knowledge becomes outdated in a few months or years. Therefore, we need to learn continuously - new things get important. Second, to stay on top of things we really need the strong will to be good at our job. That's probably the most important characteristic to bioinformatician. They are usually an excellent knowledge worker with great technical abilities, and have the will to be that over decades!

2. Avoid the sentence "I did not know what to do!"
In our computational biology lab, we generally face lots of technical problems. But as you know, it's impossible to know everything to do the computational biology jobs ( Yup.. because you need diverse and multidisciplinary knowledge to understand biological problems and resolve their respective solutions), therefore it's absolutely necessary that a bioinformatician finds its way through a new topic. How I typically do that is I use google and I talk to other experts in our laboratory or online biostar community to find out what they think. "I did not know what to do!" should not be an argument for us.

3. To make oneself useful
Several time it does happen, you finished our task earlier than expected; in such cases if you have some time left then: Take a coffee and play chess; reversi, etc. In my case I take a rest. Afterwards I think about what I could do that helps the team to achieve its targets, 'cause some of my team mates probably didn't finish! (at least if I didn't met them at coffee bar !!)

4. Care for all
During my rigorous research duration; I attended several workshop organized by my University departments. I had a discussion with other research fellow, professors; I generally ask … what it really takes to make a team successful or to be a successful research leader. They always said: "Well, you need some caring people!" I think there is a lot truth in that statement. If we do not care about quality, timelines, good team culture, respectful communication (!!), clean code, if all this doesn’t matter to us, then I believe the probability is higher that we fail in research and analysis.

5. Be good with people
Because bioinformatician and computational biologist jobs typically involves to work in a (most wanted J cross-departmental!) team, therefore it's important that we're (more or less) good in dealing with other individuals. Everyone have their own strengths and weaknesses, just like us. It's important to treat all the research team mates with respect, regardless of their technical competence or contributions. Of course, sometimes people deserve a clear statement (!!!), but try to do these things one-on-one. Make sure nobody loses his face. Attend the meetings at the coffee bar; be good at table top soccer and go out once in a while to have a beer with your team. You know what I'm talking about.

At the end of a week I look back and I ask myself what I have produced. This could be paperwork, community days or (best!!) programming code. Always remember there is always a solution to a problem. Most of the times there are at least three solutions. So, don’t just blame, suggest a solution.

That's it. I am looking forward to your thoughts and comments!

Paper: http://science.sciencemag.org/content/early/2018/10/10/science.aau4832

More at https://www.wired.com/story/genome-hackers-show-no-ones-dna-is-anonymous-anymore/