Smudgeplots are computed from raw or even better from trimmed reads and show the haplotype structure using heterozygous kmer pairs. For example:

Address of the bookmark: https://github.com/KamilSJaron/smudgeplot

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

Address of the bookmark: https://www.ncbi.nlm.nih.gov/nuccore/ON563414

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/

• Clustering module Predicts, clusters and annotates the genes of every input genome
• Lifestyle prediction Employs a machine learning model to forecast bacterial lifestyle or other specified metadata
• Analitical module (Shiny app) Results from the previous modules are embedded in a user-friendly interface for comprehensive and interactive comparative genomics.

You can find the complete wiki here [https://github.com/Carrion-lab/bacLIFE/wiki/bacLIFE-wiki]

Address of the bookmark: https://github.com/Carrion-lab/bacLIFE

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.

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!

In addition to providing a broad sample of the diversity in S. bicolor, the genotypes included in this study are known to display agronomically important traits including stay-green drought resistance, insect resistance, grain size and grain quality.

Find more at http://www.nature.com/ncomms/2013/130827/ncomms3320/full/ncomms3320.html

For knowledge about Linux and their importance amongst bioinformatician plesae read this article "An introduction to Linux for bioinformatics" by Paul Stothard.

Linux cheat sheet at http://bioinformaticsonline.com/file/view/87/linux-cheat-sheet

Please browse for futher useful linux pages on right hand side ...