Tag Archives: disease

GWAS Monday

Ok, so we don’t have GWAS (Genome-wide Association Study) mondays, but we might as well have. The field of study seems to be growing hugely fast, especially when you consider one of the first major GWAS was published just a short 2 years ago (or 4 years ago, depending on how you define major, still… short time ago :).  I read this post at Spittoon last month and thought I’d link to it (better late than never), but it appears now that there now over 328 GWAS published and many more coming. The post goes on to wonder “what next?” and summarizes some interesting articles at the New England Journal of Medicine from last month.

While I’m at it, let me point out some past recent posts on GWAS and tools here ;). Last summer I posted a note about Ensembl and UCSC Genome Browser’s GWAS viewers, in November Mary posted a link to a list of then complete GWAS, in January she also posted a Tip of the Week on visualizing GWAS using HapMap (where a commenter pointed to this useful paper), in February I posted a quick link to a new GWAS viewer, and you can find a few other posts on GWAS by doing a simple boolean search of the blog.

Swine Flu: What's the Populous to Do?

Well, as far as I can tell, read & do all the normal stuff for staying healthy (you know, all the stuff Mom used to say – wash your hands, drink plenty of liquids, eat right & get plenty of sleep.) I heard about swine flu as I woke up yesterday morning listening to NPR, and the coverage of the “outbreak” seems to be spreading more virally than the virus itself. PubMed already has a special section of their homepage dedicated to swine flu info, with links to recent PubMed articles,  a link to the swine flu sequence in NCBI’s Influenza Virus Resource, and a widget to CDC’s swine flu information page.  Through one of the PubMed references I found a resource I had not heard of before – ESNIP2 – the European Surveillance Network for Influenza in Pigs.  From the sequence report I could like to structures in NCBIsmall_world_3‘s MMDB and from there to structures in RCSB PDB.  From the CDC’s site I followed a link to an update from the World Health Organization which reported the number of cases and deaths in various world locations. I find it really cool to be able to link so freely between biological/health information resources, and be able to counter all the popular media frenzy with reports of real science. And the fear mongering seems to be just that, at least for now, because nothing that I’ve read so far indicates that swine flu is any more deadly or virulent than ‘normal’ seasonal flu – it is just a different virus than we normally see.

Yea, ok, we live in a small world where people move around and potentially spread diseases far and wide. But there is so much information at our finger tips – with an internet connection and a bit of knowledge where to look. I’ll continue to take my risks, travel, read science, and of course listen to Mom & wash my hands! :)

Addition (Trey): These are some great resources that Jennifer linked to. I’d like to include some additional general and genomic data too. I’ll add more as I find them:

Kristi at Bioinformatics@Becker (she’s here at Wash U where we are giving a seminar today) has a great post with links to many general and science links.

BioHealthBase has the swine flu strain genome details. BioHealthBase “provides a comprehensive genomic and proteomic data repository for five pathogenic organism groups that pose a threat to public health”

Effect Measure is a MUST read blog for anything public health policy and science related, and is a great read right now.

A short primer on the science of the swine flu.

Tip of the Week: TDR Targets Database

tdr_targets_tip  For today’s tip, I would like to introduce you to the TDR Targets Database, which seeks “… to exploit the availability of diverse datasets to facilitate the identification and prioritization of drug targets in pathogens causing neglected diseases.” I found out about this database this past weekend as I was catching up on my ‘science reading’. The database was featured in an article published November of 2008 in Nature Reviews Drug Discover – if you have a subscription, I’ve cited it below. Even though the article is a bit old, it seems very timely for us here at OpenHelix right now since in February Trey participated in the first annual African Virtual Conference on Bioinformatics 2009, and later this week he is heading off to give a talk at the Genetics and Genomics of Infectious Diseases conference in Singapore. TDR Targets is a nice resource – here I show a few search options, but there is so much more here that you can explore on your own!

ResearchBlogging.org Agüero, F., Al-Lazikani, B., Aslett, M., Berriman, M., Buckner, F., Campbell, R., Carmona, S., Carruthers, I., Chan, A., Chen, F., Crowther, G., Doyle, M., Hertz-Fowler, C., Hopkins, A., McAllister, G., Nwaka, S., Overington, J., Pain, A., Paolini, G., Pieper, U., Ralph, S., Riechers, A., Roos, D., Sali, A., Shanmugam, D., Suzuki, T., Van Voorhis, W., & Verlinde, C. (2008). Genomic-scale prioritization of drug targets: the TDR Targets database Nature Reviews Drug Discovery, 7 (11), 900-907 DOI: 10.1038/nrd2684

Cold genomes

coldvirusRecently, we are learning a lot about the cold virus. The genomes of many have now been sequenced (that is a subscription-required Science report, you can read more about the report here).

You can find more genomic information at the picornaviridae.com at the NCBI’s Entrez Genomes and some structural information at MMDB. (just a side note, rhinovirus is now classified as enterovirus).

Tip of the Week: List of disease genes

disease_tip.jpgOne of the most common questions we get when we are out doing software training is: what do I do with a list of genes? People generate lists from all sorts of biomedical research forays: microarray results, database searches, literature searches, library screens, etc. The source doesn’t matter much–in the end people have this list that they need to analyze, assess, categorize, group, filter, and manage.

We’ve been looking into some tools to accomplish this. We’ve already demonstrated a few of them already (Reactome SkyPainter, Gene Ontology Term Enrichment, MatchMiner…). But there are more that I want to explore. What I decided to do was to create a standard list that I’m going to use to explore and evaluate different tools. Today the tip is where I got this list and how I created it. I want to be able to refer back to this list in the upcoming “list” tips, and thought that if I explained that first it would help.

So today’s tip is obtaining a list of disease genes from UniProt. Now, you could just go to UniProt yourself and get this handy list. But I show you how to get there starting from the UniProt homepage, and what I did to filter this list to a set of unique gene symbols for disease genes in Excel. I end up with ~2500 unique symbols for disease genes that will be the input for upcoming tips.


Tip of the Week: Discovering Chemicals-Gene-Diseases Interactions w/ CTD (or Google)

ctdThe Comparative Toxicogenomics Database (or CTD) is an excellent database to find information on chemical-gene-disease interactions. It is a manually curated database of chemical-gene interactions, chemical-disease and gene-disease associations. At your fingertips you can find information about chemicals, interacting gnees, inferred diseases, pathways, references and news. It’s worth a look. And you can use Google to quickly search the database. Check out this week’s tip to find out more about the database and using Google to search it quickly.

Tip of the Week: Homophila

homophilahomophila2(click either graphic to see the tip of the week movie) It’s not Halloween yet, but thought I’d get us started in the mood by introducing you to a database that has some obvious references to the movie “The Fly” (the 1958 version is the only really worth watching :). Ok, so the database doesn’t actually help you turn humans into flies, that’s a few years away (that’s a joke of course). No, this is one of those resources that does one thing and does it well. It’s very straightforward and simple… it takes human disease genes and sequences found in OMIM and finds the homologs in the Drosophila melanogaster genome. The name of the database is Homophila. From the results you can find the links to the data and go from there. Simple function that can be very useful. Give it a try.

Tool you might not know: F-SNP

We go through the thousands of resources and databases available online in our search to do tutorials we found many that are great resources but for one or more reasons we don’t or can’t do a tutorial for. Yet they are great resources. So, we occasionally do “Tip of the Week” on some, but even those are not enough to at least touch on all the great resources out there, so occasionally I we are going to give a quick “shout out” to some of these resources occasionally.

So today it’s F-SNP.

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Genome-wide Association studies in Ensembl and UCSC Genome Browser

ensemblGWASAs genome-wide association studies (GWAS) become much more widespread and useful, the genome browsers are finding ways to incorporate these data and to allow you to view the published data or your own.

The UCSC team has already developed a useful interface in their “Genome Graphs” tool which allows you to view and compare disease studies (9 diseases so far), browse regions in the genome browser, sort genes and more. It also allows you to import your own GWAS data.

The Ensembl blog has just announced that Ensembl too has now incorporated genome-wide association studies into their database (7 so far). You can access these in the DAS Sources menu  in the ContigView (in detailed view section as shown here, click to enlarge) and CytoView pages. These are listed as “WTCCC” and then disease initials (BD, CAD, CD, HT, RA, T1D, T2D) in the menu. I don’t yet see a way to easily upload and view your own data, but I’ll double check as I play around with it.

I see a blog post and  tip-of-the-week comparing these two in more detail coming! For now, just letting you know they are there.

Tip of the Week: Phenotypic Data from the PhysioNet

PhysioNet TipOne of the things I’ve been thinking about lately are connections between genotype and phenotype – it is a topic in the news & I have been working on tutorials for the genotype-to-phenotype resources PhenomicDB & NCI’s dbGaP. Recently a friend notified of an article in Science featuring the PhysioNet – a resource intended to stimulate current research and new investigations in the study of complex biomedical and physiologic signals. The article was interesting & so I checked out this nice resource. In this short video I introduce you to a few of the features and data types available from the PhysioNet. You can read more about the PhysioNet, or see their mission statment by following the ‘continue reading’ link.

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