In fact, there are huge demands for expert biological data analyst. It’s a fairly new  (not exactly) hot area, these bioinformatician are invaluable because they know and understand the significance of biological data for your research and how you can use it for better understanding of biological problems.

The bioinformatics can discover biological patterns and stories in genomic and proteomics data. They can develop the pipeline needed to properly collect, store and analyse it.

Once your research group is ready to make a larger investment and hire a bioinformatician to gain a competitive edge, there are several key traits to seek out in potential candidates. The best bioinformatician are:

1. Highly Skilled - programming skills, experience with the biological software and tools.

The biological data won’t illuminate much if the scientist analysing it doesn’t possess practical programming skills, experience with the biological software and tools and a thorough understanding of basic biological stuff. A solid background in mathematics and statistics is also an indispensable trait.

2. Insight - Real vision, robust understanding and deep insight.

In order to hire the best bioinformatics and computational biologist scientist for your needs, it is always recommended and mostly practiced by the recruiters, to ask each contender to write and develop a sample script/presentation based on a specific set of data you provide. Then, explore the approaches used to deal with data provided and pick up those candidates who convey real vision, robust understanding and deep insight.

3. Energetic – Curiosity to explore

Mostly natural curiosity and enthusiasm for solving big biological problems coupled with an ability to transform data into a scientific stories may place one candidate above the rest. In addition to achieve that, the bioinformatician should be agile enough to quickly modify their methods to suit changes within a particular research.

4. Researcher – Publications

Look for someone who has a keen sense and understanding of concern biological problems. You can judge it by looking at previously published papers and data. It is always recommended to have a look at GitHub and other repository for codes written by her/him.

5. Impressive communicator - Insight that can’t be expressed is worthless.

Good bioinformatics scientists are able to uncover biological patterns and are willing to explain those patterns in clear and helpful ways through thoughtful and open communication. In other words, they should must have good scientific writing skills. A computational biologis/bioinformatician  should know how to present the data and tell a scientific story through numbers/images.

Bioinformatics Textbook Track

Find more about Rosalind puzzle at http://rosalind.info/problems/list-view/?location=bioinformatics-textbook-track

I will provide solution of all the Rosalind problem with Perl for community.

Check out the right sidebar for more links ...

Bio-Linux is using an Ubuntu LTS version as its base

The fact that it uses Ubuntu LTS versions for the base is a good thing because it means its users won't have to worry about the support. Ubuntu 14.04 LTS is supported until 2019, so people who are using Bio-Linux shouldn't have a problem.

"An updated Bio-Linux 8 version is now on the website in ISO and OVA versions. As usual, there is no need to download this version if you are an existing user. All updates to existing packages will be applied to your system through the update manager and new packages are all available via apt-get or Synaptic," reads the announcement.

The changelog also states that a problem that was preventing the desktop to not start on VirtualBox has been fixed, the QIIME and Bowtie-Bio tools have been upgraded, the pandaseq paired end assembler has been added, and the beginners tutorial specific to Bio-Linux 8 has been improved.

Check out the official announcement for a complete list of changes and updates. You can download Bio-Linux 8.0.5 right now from Softpedia and give it a spin. It has the Unity desktop and now it runs very well in virtual environments.

Reference @ http://news.softpedia.com/news/Bioinformatics-Distro-Bio-Linux-8-0-5-Now-Available-for-Download-469867.shtml

Transcription is the process by which the information in DNA is copied into messenger RNA (mRNA) for protein production. Originally created for DNA Interactive ( http://www.dnai.org ). TRANSCRIPT: The Central Dogma of Molecular Biology: "DNA makes RNA makes protein" Here the process begins. Transcription factors assemble at a specific promoter region along the DNA. The length of DNA following the promoter is a gene and it contains the recipe for a protein. A mediator protein complex arrives carrying the enzyme RNA polymerase. It manoeuvres the RNA polymerase into place... inserting it with the help of other factors between the strands of the DNA double helix. The assembled collection of all these factors is referred to as the transcription initiation complex... and now it is ready to be activated. The initiation complex requires contact with activator proteins, which bind to specific sequences of DNA known as enhancer regions. These regions may be thousands of base pairs distant from the start of the gene. Contact between the activator proteins and the initiation-complex releases the copying mechanism. The RNA polymerase unzips a small portion of the DNA helix exposing the bases on each strand. Only one of the strands is copied. It acts as a template for the synthesis of an RNA molecule which is assembled one sub-unit at a time by matching the DNA letter code on the template strand. The sub-units can be seen here entering the enzyme through its intake hole and they are joined together to form the long messenger RNA chain snaking out of the top.

R-language: http://www.r-project.org

BioConductor: http://www.bioconductor.org

Advantages of R

• Free!
• Powerful, many libraries have been created to perform application specific tasks. e.g. analysis of microarray experiments and Next-Gen sequencing (bioconductor: including Bioseq group).
• Presentation quality graphics
  • Save as a png, pdf or svg
• History
  • What you do can be saved for the next time you use R.
  • Ability to turn it into an automated script to perform again and again on different data

Disadvantages

• Lack of a comprehensive graphical user interface, but two do exist: However some do exist: R commander: http://socserv.mcmaster.ca/jfox/Misc/Rcmdr/ and Limma-gui (microarrays) : http://bioinf.wehi.edu.au/limmaGUI/

Preparation

• (Optional) Download and save the tutorial data set from
  • http://bioinformatics.knowledgeblog.org/wp-content/uploads/bioinf/kerr/data.tsv
  • Start R (type R on a Linux or Mac terminal, or find the starting link from PC)

Getting More Help

• Project Home page
  • http://www.r-project.org/
  • Check out the ‘introduction to R’, which is a much more in depth guide .
  • Also R has a built-in help system (see later)

Working directory

This is the directory used to store your data and results. It is useful if it is also the directory where your input data is stored.

• Mac/Linux: this is the directory where you typed in R
• PC: Change using the change working directory option

http://www.thestreet.com/story/13151062/1/affymetrix-acquires-assets-of-eureka-genomics-corporation-to-provide-high-throughput-and-economical-crop-and-animal-genotyping.html

http://www.stratosgenomics.com/stratos-genomics-technology

While your PhD or PostDoc application, it is more common that you got rejected by many professors. Don't disappoint reply it calmly.

In grad school, I shared a house with three Bioinformatics PhD students. One, when he applied to a particular professor, received a letter that said, essentially, "If you are applying because you want to enrich yourself, great. If you are applying because you want a job, you should know that you won't get one." I am trying to tell you this is because if you, with a good background in Bioinformatics, are passing up opportunities, you must be a strong candidate in many areas. Enrich yourself.

So, my suggestion is take a deep breath, forgot about all. Don’t take it personally. It's been usual processes while hunting for a good lab and professor. Take is positive, I am not sure why they reject, but don't worry perhaps the lab don't deserve you. Always remember there are billions of reasons not to hire someone for projects, especially in a research sector.

My suggestion, please do not whine about how you were a great research candidate for the post, and you just can't understand why they were so stupid as to have rejected you! This feeling will not win you any points in research, community. Especially, when in todays socially connected era everyone is linked. Remember, a nice E-mail saying, "I really wished to working with you on this project and I hope we cross paths again," is all you need to send to the professor. Send a thank you note to the professor. Thank them for the time they spend to judge you. In the future, If you and the professor (of your dream) are attending a bioinformatics conference, invite him/her to lunch (please remember to pay the bill). In today evolving scientific ere, always remember to build your solid network in order to get a job of interest. Join all possible networking sites like LinkedIn, ResearchGate, Acamedia, FB for the same reason. You as a researcher always build a bridge with student/researcher/colleague/professor who have the research potential to lead in research and hire you. Just because you didn't get this project, doesn't mean there isn't another that will open up in couple of month.

Mostly, jobs that are hard to get are hard to get. Only you can decide if the continued sacrifices are worth the expected payout. If it is, keep on plowing. Build relationships. Attend conferences.

Image ref @ JaSonYa

More at https://github.com/dentearl/n50PlottingTools

Address of the bookmark: https://github.com/dentearl/n50PlottingTools