The most common newbie questions are:

Should I try to use these free open source programs?  Why are we not trying GUI software for computational analysis? Should I use commercial bioinformatics programs/software?”

1. Let’s be open

We generally think free and cheap are useless. But this concept is not applicable when we discuss open source software. Mostly, the bioinformatics software is developed by highly competitive biological programmers who believe in open sharing of knowledge. They come under Open Bioinformatics Foundation or O|B|F which is a non-profit, volunteer run organization focused on supporting open source programming in bioinformatics. The best part about open source tools/software is that they’re free to download the source code and read exactly what the program does. If you are so inclined, you can view all of the parts of the program and see the logical flow of the pipeline. In addition, open source makes an excellent learning tool for any beginning bioinformatician. Moreover, you can modify existing open source programs to deal with cutting-edge problems or to customize your pipeline. Apart from your computational and analysis work, most of the reviewer also prefers the open source based results so that they can validate the results if validation required.

2. Code headache

As a bioinformatician you are supposed to know the basics of programming languages, and if you are not good at it, then please learn it as soon as possible because you are not a bio-analyst but biological programmers. The open source programs usually lack dedicated service and support teams (often because they were the product of an overworked doc/postdoc!) so you are responsible for troubleshooting your own errors most of the time. We commonly receive the HELP email to support and assist to setup the pipeline; you can also find this kind of request on any QA forum. I personally believe this coding horror brings the biggest downside of open-source programs; where you need some programming skills in order to implement the program in your pipeline. But, if you are not able to fix the pipeline and modify the open source code according to your requirements them you should re-think on your bioinformatician name tag!!!

3. Dive into the codes

Some of the biologist turn bioinformatician says “if you can do the same thing with commercial software then why to get migraine with weird codes”, well this statement looks to me that guys are keen to learn swimming but still don’t like to get wet. If you are still using paid software and doing your work by customer support and clicking some of the well-designed GUI button then perhaps you are not interested in learning and trying new and challenging bioinformatics works. You are missing the basic flavour of bioinformatics. Let’s dive into the coding world, I am sure your will enjoy it. I recommend your to swim freely in code’s sea, and enjoy the journey; do not merely watch it from the outside.  

4. Paid does not mean better

The bioinformatics company which are specializes in bioinformatics solutions develop well designed/packed, user friendly software by using a large number of specialised scientist, programmers and support staff. They also provide good services to accomplice your biological analysis work. This means that if you hit a ‘snag’ with your data, help is likely only a phone call away! These companies price their products competitively against the cost of a dedicated bioinformatician. You may be able to afford the program, but not the additional staff! Additionally, most of the functionality that you need in your analysis is already coded into the program. Need to plot a graph? Just click this button right here. It is that easy. But, as a bioinformatician this is not generally well encouraged approach in biological analysis work, because the software is not available to everyone and your data can’t be validated. Moreover, there is very less chances that anyone will repeat your work or love to do similar kind of research (because not all the labs in the world are rich like yours).

5. Take a caution

In biological analysis work, in which you deal GB/TB of data are having maximum chances of getting errors, so please be careful and always cross check your data before coming to any conclusion. Even an error in two line code can alter your entire analysis and display weird results. Some of the scientist blindly believes on commercial software, which is entirely wrong. Using proprietary tools does not absolve you of the need to actually read and research the type of analysis that you are doing. This is particularly true in the case of genome assembly and annotation.

At the end, I would like to tell only one think that open source solutions allows you to do more cutting edge analysis than the commercial tools. So let’s go for it.

Disclaimer:

This is my personal view. I have nothing to do with any company or open source community. The views expressed on these pages are mine alone and not those of my current/past employers. I do reserve the right to remove comments left by spammers or off-topic comments.

http://elements.eaglegenomics.com/

Uni Computing (formerly Uni BCCS) is a department of Uni Research, affiliated with the University of Bergen.

5 groups in this lab works on computational resources, methods, algorithms, and software.

Following two bioinformatics groups are:

The Computational Biology Unit (CBU) provides education and research in bioinformatics focused on functional genomics.

The Computational Ecology Unit (CEU) is basically deal with population fluctuations, behavioural patterns and the ways life cycles emerge.

PNAS papers:
http://www.pnas.org/search?author1=Janet+Kelso&sortspec=date&submit=Submit

Jobs:
http://www.eva.mpg.de/genetics/bioinformatics/jobs.html

Contact:
Kelso Group
Department of Evolutionary Genetics
Max Planck Institute for Evolutionary Anthropology
Deutscher Platz 6
04103 Leipzig
Germany
Email: kelso@eva.mpg.de

Foreign nationals intending to have certification are eligible to appear for BINC examination.
Minimum qualification includes a degree from a recognized university/institute in the areas listed in FAQ.
Formal training in the area of Bioinformatics is not a prerequisite.
Note that the foreign students will only be certified by DBT and are not eligible for the cash award as well as junior research fellowship.

Address of the bookmark: http://binc.scisjnu.ernet.in/

All applications sent before 2nd of February 2015.

http://www.mind-project.eu/career

Address of the bookmark: http://www.rdatamining.com/

Awk is a programming language that is specifically designed for quickly manipulating space delimited data. Although you can achieve all its functionality with Perl, awk is simpler in many practical cases.

Why awk? You can replace a pipeline of 'stuff | grep | sed | cut...' with a single call to awk. For a simple script, most of the timelag is in loading these apps into memory, and it's much faster to do it all with one. This is ideal for something like an openbox pipe menu where you want to generate something on the fly. You can use awk to make a neat one-liner for some quick job in the terminal, or build an awk section into a shell script. You can find a lot of online tutorials, but here I will only show a few examples which cover most of bioinformatician daily uses of awk.

choose rows where column 3 is larger than column 5:

awk '$3>$5' input.txt > output.txt

extract column 2,4,5:

awk '{print $2,$4,$5}' input.txt > output.txt

awk 'BEGIN{OFS="\t"}{print $2,$4,$5}' input.txt

show rows between 20th and 80th:

awk 'NR>=20&&NR<=80' input.txt > output.txt

calculate the average of column 2:

awk '{x+=$2}END{print x/NR}' input.txt

regex (egrep):

awk '/^test[0-9]+/' input.txt

calculate the sum of column 2 and 3 and put it at the end of a row or replace the first column:

awk '{print $0,$2+$3}' input.txt

awk '{$1=$2+$3;print}' input.txt

join two files on column 1:

awk 'BEGIN{while((getline<"file1.txt")>0)l[$1]=$0}$1 in l{print $0"\t"l[$1]}' file2.txt > output.txt

count number of occurrence of column 2 (uniq -c):

awk '{l[$2]++}END{for (x in l) print x,l[x]}' input.txt

apply "uniq" on column 2, only printing the first occurrence (uniq):

awk '!($2 in l){print;l[$2]=1}' input.txt

count different words (wc):

awk '{for(i=1;i!=NF;++i)c[$i]++}END{for (x in c) print x,c[x]}' input.txt

deal with simple CSV:

awk -F, '{print $1,$2}'

substitution (sed is simpler in this case):

awk '{sub(/test/, "no", $0);print}' input.txt

OK now here's where to read this stuff properly explained. roll

Two thorough tutorials:

http://www.gnu.org/software/gawk/manual/gawk.html

http://www.grymoire.com/Unix/Awk.html

A famous list of useful one-liners - though they're short, many are quite tricky:

http://www.pement.org/awk/awk1line.txt

And some nice explanations of those one-liners. After reading this you'll have a pretty good grasp!

http://www.catonmat.net/blog/awk-one-li … -part-one/

http://www.catonmat.net/blog/ten-awk-ti … -pitfalls/