To date there are several open-source applications available to extract GO terms corresponding to protein/nucleotide sequences (A detailed list can be accessed here, However, the best I have experienced for the De novo transcripts is InterProScan), and to perform enrichment analysis (A detailed list is here). Most of these enrichment tools work like a charm for model organisms, but only handful of them support the incorporation of custom annotations. One such tool is BiNGO (Biological Networks Gene Ontology tool), an open-source Java plug-in of Cytoscape. BiNGO can be used either on a list of genes, or interactively on subgraphs of biological networks visualized in Cytoscape. BiNGO maps the predominant functional themes of the tested gene set on the GO hierarchy.

In order to use BiNGO for novel organisms, one need to provide a custom annotation file (CAF). In principle, CAF contains the gene/transcript and GO relationship, with one relationship per line, eg.

XLOC_000001=0005515
XLOC_000001=0008270
XLOC_000001=0016491
XLOC_000002=0055114
XLOC_000003=0016491
.
.
XLOC_999999=9999999

The left value is the transcript name and right value is the GO category (without the prefix, ‘GO:’) obtained using InterProScan or synonymous tool.

The first line of GAF should always be:

(species=Custom_species)(type=Biological Process)(curator=GO)

You can choose to change species name from “Custom_species” to something else. Once the building of GAF (GAF.txt) is complete for all the annotated transcripts. It can be used in place of “Select organism/annotation” by choosing “Custom” option. (As shown in the figure below)

 

Additionally, one can also choose to switch to a newer ontology (obo) file downloaded from geneontology.org download page. After providing gene list of interest and choosing the appropriate options, hit the “Start BiNGO” button to start the analysis.

Cytoscape together with BiNGO offers several downstream network grooming options, which you may find useful. For more on this, visit BiNGO and Cytoscape user guides. Hope this helps in your endeavor.

1) Check disk/drive usages:

df

2) Create the space. This step is long if you select a large amount of space. In this example, 512MB is created under root (/), given that block size (bs) is 1024 bytes:

sudo dd if=/dev/zero of=/swapfile bs=1024 count=512k

3) Switch it on:

sudo mkswap /swapfile

*This will only last until you restart the operating system, so, useful as a temporary measure. To make the swapfile permanent, do the following:

4) Open the following file:

sudo nano /etc/fstab

Paste in the following:

/swapfile    none     swap     sw     0     0

Article by: Dr. Kevin Blighe

A very classic question in bioinformatics is, which programming language is the best for a bioinformatician? Discussions like this never end with a conclusive answer. Interestingly, people find this question as a piece of cake and jump at it with whatever they have in their hands! The result is, you get a nice rainbow of choices, right from “C” to “PHP”!

Each programming language has its own perks and disadvantages. For example, “C” has an incredible speed in execution but it is equally code-intensive in writing even a simple program. Python and Perl on other hand make the same program code-lite but with a mediocre speed of execution. Apart from these performance issues, every language is blessed with a varying degree of third party modules/libraries.

Python has provided interfaces to many system calls and libraries, giving direct access to the shell of an operating system (modules like os, subprocess let you call unix commands directly from the python terminal). Python is also usable as an extension language for applications written in other languages that need easy-to-use scripting or automation interfaces. More than 15 coding projects have started to establish a platform where python can be integrated with other programming languages like C, Java, Perl, PHP, R, Fortran etc.

These hybrid platforms are either available as python modules which can easily be imported, like we do for general (numpy, maths, random etc) modules or accessible from a parent language (i.e. Jython, python implemented in Java)

A detailed list of these hybrid platforms are accessible from here.

Some fascinating platforms I couldn’t resist to mention here are:

• elmer: Elmer allows developers to write code in Python and execute it in C or Tcl.
• JPype: JPype allows python programs to fully access java class libraries.
• PyPerlish: Allows the usage of perl idioms in python.
• RPy: Simple and efficient access to R from python.

It is interesting to note that every platform mentioned here was somebody’s dream. Since shifting to a new language might deliver new exciting features but at the same time it takes away what you loved the most about the previous one. Following are the words from the creator of PyPerlish,

“I’ve used perl for several years, and been very impressed with its ease of use. When you need to do something new, chances are there is an idiom which lets you do it in a few keystrokes. I didn’t want to lose that in moving to python. Somehow I wanted to get the benefits of perl’s idioms with the robust scalability and maintainability of python. So the idea is to emulate perl idioms, no matter how we implement the python code under the covers.”       — Harry George

Note: This page will update time to time! You might want to save this page for the current listings or Bookmark it for further updates in this catalogue.

I, recently, performed an extensive literature mining and considered the opinions of pioneering researchers and scientists in the field of bioinformatics on this topic.

Disclaimer:

1. If you are an established researcher and looking from a professional angle then this article is not for you (Although we are going to discuss the foundation here). I would be happy to redirect you to “10 Steps to Success in Bioinformatics“
2. If you are not from the category 1, then you are on the right page.

While mining I came across various raging discussions regarding, what bioinformaticians are meant to do? Some people were adamant on the notion that experts from various fields should collaborate together instead of one person doing all the things poorly! It is crucial to understand that a bioinformatician is different from mathematician/statistician and he/she is also different from a biologist in regards to their ability to establish an interface for research and also channel the requirements and findings from both the directions.

A disparity between a biologist, a mathematician and a bioinformatician!

One thing I observed is that the definition of bioinformatics is not universally agreed upon. Generally speaking, we define it as the development and application of information technology for understanding the problems in biology, most commonly molecular biology (but increasingly in other areas of biology as well). As such, it deals with methods for systematic storage, retrieval and analysis of biological data including but not limited to nucleic acid and protein sequences, their structures, functional roles, regulatory pathways and biophysical interactions.

Some researchers interpret bioinformatics more narrowly and include only those issues dealing with high throughput sequencing data along with the  integration and the analysis of OMICs. A few construe bioinformatics more broadly and include all areas of computational biology, including population modelling and molecular simulations. Others construe bioinformatics as the development of necessary tools and databases for the analysis of biological data to draw meaningful conclusions.

In spite of the diversity in the bioinformatics domains, the foundation of which can be summed up in three major pillars of education:

1. Biology
2. Mathematics and Statistics
3. Computer Science

It is ideal but at the same time ambitious to be able to grasp all the three pillars of bioinformatics. A good bioinformatician would possess a thorough knowledge of any two pillars but AT THE SAME time should also be aware of the fundamentals of the third one.

If you have trouble deciding, then let your INTEREST pick your two pillars, based on which you will develop a bioinformatics flavor, right from sequence analysis to molecular dynamic simulations.

These pillars in themselves are very diverse if we dive into them. For example, when we talk about biology, it comprises of molecular biology, biochemistry, evolution, ecology, behaviour etc. Similarly, in mathematics and statistics, you have a range of options right form probability theory to analytical combinatorics. In soft skills you have options from programming languages to learning systems.

You can browse through some of these streams and learn more about them here at “Your eLearning Directory of Bioinformatics essentials“.

One becomes a bioinformatics domain expert based on what slice he/she takes from the stack. You may chose molecular biology, differentials and integrals, and Matlab or equivalent, and be a good bioinformatician in the domain of molecular dynamics and simulations. Some people might argue that this is a very naive theory and one would require to know much more than that. Well naturally there is no upper limit. If you keep your knowledge stagnant for a long, you will soon realise that you have stopped growing. The pace at which advancements are happening in the field of biology and technology, it has become vital to upgrade our knowledge, skills and techniques but at the same time it is absolutely essential to stick to your stream, because success comes from experiences and experiences comes from dedicated efforts.

Your integrity is like a tip of an iceberg. The same tip, from outside may appear smaller than a frozen lake but when it comes to summer, the tip is all that remains since it has a strong foundation which no ordinary summer can melt.

For example:

Input file: hg19_genome.fa

    >Chr1
    ATCTGCTGCTCGGGCTGCTCTAT…
    >Chr2
    GTACGTCGTAGGACATGCATCG…
    >MT1
    TACGATCGATCAGCTCAGCATC…
    >MT2
    CGCCATGGATCAGCTACATGTA…

We would like to extract the sequence for Chr2 from hg19_genome.fa. Use the following command:

$ awk ‘BEGIN {RS=”>”} /Chr2/ {print “>”$0}’ hg19_genome.fa

Output:

    >Chr2
    GTACGTCGTAGGACATGCATCG…

Note that, the search keyword (here ‘Chr2’) doesn’t have to be an exact match. If you use ‘MT‘ instead, you will get the third and fourth entry, since ‘MT’ is a sub-string of the third and fourth fasta record.

Now lets break down the command so that we don’t have to mug it up or we could mold it and use it in variety of other places.

• awk — This is the main command (Or more of a very powerful programming language)

• ” — We write every bit of awk code inside these single quotes

• BEGIN — This tells the awk to execute the immediately following code in curly brackets at the beginning.
• {RS=”>”} — Record separator  (If we look at the file, we can observe every sequence starts with a “>” sign. This helps us to separate two fasta records)
• /Chr2/ — keyword to search in the entire record
• {print “>”$0} — Here $0 is the current record (From “Chr2” to the entire sequence till next “>”). We added “>” at the beginning just to get the standard identitifer which is not included in $0.
• hg19_genome.fa — This is the input multi-fasta file that we have used.

Now,
Suppose we are interested in more that one keyword then two possibilities arise:

You want BOTH the keywords present,
$ awk ‘BEGIN {RS=”>”} /Chr2/ && /MT/ {print “>”$0}’ hg19_genome.fa