As you may know, we’ve been doing tips-of-the-week for three years now. We have completed around 150 little tidbit introductions to various resources*. At the end of the year we’ve established a sort of holiday tradition: we are doing a summary post to collect them all. If you have missed any of them it’s a great way to have a quick look at what might be useful to your work.

You can see past years’ tips here: 2008 I, 2008 II, 2009 I, 2009 II.  The summary of the second half of 2010 will be available next week is here.

January 2010

January 6: PSI SGKB’s Monthly Structural Genomics Update (Edited) a look at the Structural Biology Knowledgebase (SBKB). You can see more features in our SBKB full tutorial as well.

January 13: Like to show letter-based motifs in proportional display? IceLogo will do that for you.

January 20: Managing your references with Mendeley.

January 27: WebGBrowse, a great tool for non-programmers to set up sequence browsers.

February 2010

February 3: Draw phylogenetic trees with Phylowidget.

February 10:  Dynamic network and interaction displays with a web-based interface, GeneMania. For a deeper look, try the full tutorial.

February 17: Collecting and organizing biological names, including historical ones: uBio.

February 24: GOOD, the Gene-Oriented Orthology Database.

March 2010

March 3: A very cool 3-D display of different resources, Caleydo.

March 10: HapMap data in HaploView.

March 17: A Word plug-in to help authors standardize terminology as they write scientific papers.

March 24: Genomicus, a neat web interface to explore evolutionary relationships.

March 31: PubGet, a handy tool for searching and reading papers, and it works with ResearchBlogging posts.

April 2010

April 7: MitoCheck, a database of mitosis movies in normal and mutant cells. A very cool hybrid of cell and computational biology strategies coming together.

April 14: RatMine, one of the several “Mine” tools that is available for data mining.

April 21: The MEME suite of tools is introduced.  There are great motif analysis strategies here. We have several (subscriber) tutorials on MEME components as well.

April 28: An exploration of the International Cancer Genome Consortium (ICGC) site and their hot new BioMart query tools.

May 2010

May 5: Learn about WAVe, Web Analysis of the Variome. This also led to another cool look at WAVe when one of the developers did a guest post on it: Guest Post: WAVe – Pedro Lopes.

May 12: Chromhome, a karyotype-level web tool.

May 19: A tour of genome variation tools, part of a series by Trey.

May 26: The Cancer Genome Workbench, a tool that gathers multiple cancer data resources in a handy portal.

June 2010

June 2: The Mouse Resource Browser, a database of mouse databases.

June 9: Trey continues his genome variation tour.

June 16: A wealth of web service tools are collected in BioCatalogue.

June 23: We revisit the Structural Biology Knowedgebase to highlight some new features.

June 30: The third part of the genome variation tour explores more tools.

I would like to start by thanking Trey Lathe  for the opportunity to promote WAVe in this great blog. After his short tip of the week post, I’ll now try to make a more detailed overview of this new application.

What is WAVe?

WAVe stands for Web Analysis of the Variome and is a simple application focused on centralizing the access to distributed and heterogeneous locus-specific databases (LSDB). LSDBs are an emerging type of bioinformatics applications, aiming at providing gene-centric information regarding discovered genomic variants. In WAVe, we offer both LSDBs as well as to its variants. Moreover, we also provide access to a comprehensive list of carefully selected external resources. With this, users have, in a single application, access to gene and variation information enriched with a multitude of gene-related resources in a lightweight and easy to use web application.

What are WAVe’s key features?

At this early stage, WAVe’s publicly available features are related with data access. Users can easily browse through available genes, search for genes, view gene info and access each gene RSS feed. In WAVe’s entry page, users simply need to start typing a gene HGNC-approved symbol and several suggestions will appear: accepting one of them leads directly to the gene view page. Following the view all link, users can browse all available genes or check, for each gene, how many LSDBs and variants are available.

To access the application data, users just need to navigate in the gene tree. Each tree node represents a distinct data type and the various leaf provide access to external applications: by clicking a leaf, the destination page is loaded in the main content area. Repeating this process, users can navigate in the dozens of listed links for each gene.

WAVe also offers its core data to other developers. To obtain the gene tree and its links, users just need to add the rss tag to the end of gene address. This will output a RSS2.0 feed that can be easily parsed by any application or added to a feed reader.

How was WAVe born?

The european GEN2PHEN project is an initiative to link, as deeply as possible, data from genotype features to its phenotype counterparts. The first step consisted in an attempt to improve various genomic variation resource scenarios. This implied normalizing LSDBs (the “LSDB-in-a-box” approach, LOVD) and defining novel data models and formats for data exchanges from and to LSDBs.

In a long term perspective, applying the GEN2PHEN-approved data models, will enhance the creation of new services and applications to integrate and interact with the exponentially growing dataset of genomic variation data.

With WAVe we tried a different approach based on three questions: why wait for everyone to adopt these new formats? What will happen to legacy LSDBs that won’t adopt the new formats? How can we have an immediate solution? We have created a lightweight integration architecture, based on links to applications and adopted a simple (yet familiar) tree-based navigation interaction to deploy a new application that can be used right now and will easily scale to integrate the foreseen data exchanges formats. Technical details aside, based on a manually curated LSDB list, we can connect and integrated any kind of LSDB application whether it is a modern LOVD application or a simple text-based legacy LSDB.

How is it relevant?

To demo WAVe efficiency let’s just try to perform a simple search in our lab: Are there any LSDBs for COL3A1 gene in the human species? And known variants? And what are the associated proteins and pathways?

In a WAVe-free scenario, to find out COL3A1 LSDBs (if any), researchers need to google it (the main COL3A1 LSDB does not appear in the first result page) or, if you they are used to it, go to HGVS site, go to the “Databases & Tools” section, select “Locus-specific Mutation Databases” and then search for the gene in search box. Now for the variants researchers just need to browse the last page they’ve just entered. How many clicks (and time!) does it take?

For protein information, researchers enter in UniProt and search for COL3A1: that gives about 29 results. Add a filter for the human species and there are 5 results. Good enough to access directly to P02461 (SwissProt reviewed). Though, there is new window/tab open. Now for pathway information, a KEGG quick search for COL3A1 lists 14 results. In the end, there are about 3 windows/tabs and made some 20 mouse clicks to obtain the desired information.

Using WAVe, researchers simply need to access WAVe, start typing the gene HGNC symbol, select COL3A1 from the suggestions and access COL3A1 page. Once in the page, it’s as easy as browsing in the tree… Variations? Check the variation node, they’re even grouped according to the change type. UniProt information? Check the protein node where you have direct access to SwissProt, TrEMBL, PDB, Expasy and InterPro. And I guess you get the picture. In the end, one window/tab and about 6/7 mouse clicks.

Other UA.PT Bioinformatics tools

At the University of Aveiro’s Bioinformatics research group we are mainly young and enthusiast computer science experts, simply trying to make biology easier (at least in terms of computer applications!). Our more relevant web-based tools include MIND (a microarray analysis tool), GeneBrowser (a gene expression tools, useful to process data gathered from systems like MIND) and QuExT (a comprehensive MEDLINE mining application).

-Pedro Lopes