A Brief Bioinformatics Tutorial

Quickly Exploring Your Gene In The Literature: The iHop Server

iHOP means information Hyperlinked Over Proteins. The iHop server is a very efficient tool allowing finding all the papers in which a particular gene name is cited. Let's try it:
1/ Go to the Web site
2/ Type a gene name (e.g. vps36) or an accession number (e.g. UniProt or NCBI Gene, etc.) into the search field in the menu bar.

3/ In many cases your search term will correspond to various genes or proteins. Click on the "paper" icon on the right-hand side to select the corresponding gene. When you mouse goes over it, it should be written "interaction information for this gene"

4/ The "interaction information" contains all sentences found in the literature for the main gene of a page (gene A) in red, and other genes (gene B) in Blue. Gene symbols within sentences are hyperlinked. Therefore, clicking on a gene symbol (e.g. gene B) will bring you to the page of gene B.

5/ A nice option is the Enhanced PubMed/Google query. Each gene page contains at the top a link to execute it. These queries allow you to quickly query PubMed or Google for a specific gene including all its synonyms and orthographic variations.
6/ You can also tune your iHop navigation, just use the Filter and Options link in the menu bar to change overall appearance parameters.

• Show official symbols - Select this option to show the official gene symbol beside the identified synonym; e.g. PC7 -> [PCSK7] PC7
• Show PMID instead of abstract icon
• Sort sentences by articles
• Select specific experimental data sources

More detailed/complete information about iHOP can be found by clicking on "help".

Get Functionally Related Genes Using The STRING Server

STRING is a web server containing 7 types of functional information for 736429 proteins in 179 species at the moment.
Let's see if it can give any useful information about our gene
1/ Go to the STRING web site ,
2/ Enter your gene name; alternatively if no gene is found you can paste a protein sequence
3/ Look at the result table:

the functionnally related protein found are displayed and the evidence type used to infer the relation is specified.
Here, you can:

• Click on any of the genes to get its fasta sequence
• Click on any of icons looking like , , etc ... to get details about a given type of information.
• You can change the research parameters using the box
  
• Finally, this option , is very interesting: it displays the network of proteins present in the result box and allows you to grow the network step by step.

The 7 types of information used to infer functional relationships are:

• Gene Neighborhood, i.e. looks whether there are gene(s) that are always next to yours in genomes. This is useful mostly for prokaryotes and may indicate an operon.
• Gene fusion, i.e. looks in other species if genes homologous to your gene (say A) are fused with some other genes (say B and C), in which case your gene (A) might interact with the homologous proteins of B & C in your specie.
• Co-ocurence, i.e. looks at genes that have the same phylogenetic profile. In other worlds, it looks for genes that appear in the same phylogenetique lineage(s).
• Co-expression, i.e. looks for genes that are expressed and repressed in the same way your protein is.
• Experiment, i.e. looks in the BIND database for some interactors with your protein.
• Textmining, i.e. look in the published literature for papers citing your gene. (This is close to iHOP)

More Functional Information at NCBI

Up to now, we used "NCBI protein", if you select "NCBI gene" , you will see that you actually have access to plenty of extra-information such as interactions.
1/ So, go there ,
2/ Type in .. do I need to tell you ;) ?
3/ Select the good gene,
4/ Look at all this information!

Model organism databases

If your gene of interest is from Yeast, Worm or Fly, you should have a look at the organism database. These databases summarize all of the features of a particular gene or protein. Therefore, they are good to get a quick overview of what is known in contrast to literature mining approaches (e.g. iHOP), which allow getting more detailed information.

• The Yeast database (SGD) is available here.
• The Worm database (Wormbase) is available here.
• The Fly database (Flybase) is available here.

It might also be worth checking EnSembl as it gives good access to gene structure and genome organization for a wide range of organisms.

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