Tag Archives: bacterial genomes

Video Tip of the Week: PATRIC Protein Family Sorter

So just last night my twitter feed was abuzz with bacterial infection drama. The Frontline PBS show was doing a whole episode called “Hunting the Nightmare Bacteria” and based on the chatter I’d say they struck a nerve. Our antibiotic arsenal is failing us, and it’s crucial to figure out new ways to battle these organisms. Some of those things we might be able to learn from studying their biology, but in other cases their own enemies might help us out in finding new compounds that can be used against them. And boy am I glad researchers are looking for ways to combat bad bacteria.

Just as a coincidence I had planned to highlight PATRIC today. PATRIC is the Pathosystems Resource Integration Center, sometimes also called BRC for bacterial Bioinformatics Resource Center. Their goal is to support researchers who study infectious diseases by focusing on these pathogenic organisms. Go over there and open the “Organisms” tab to get a feel for the species they examine. They offer annotation details of these species, but also provide lots of analysis tools as well. You can learn more about them from their “about” page.

We’ve talked about their nice tools before, so I won’t rehash that whole story, but I wanted to highlight the new feature they tweeted about the other day. It’s a protein family sorter and heatmap tool. They also provided a helpful video tutorial to get you up to speed with it.

But this is just one of their recent enhancements and additions, so be sure to check out some of the other new things. And note that their videos and tutorials page has a lot of great guidance on understanding their site features.

If this is a topic that interests you, there’s also going to be a live chat today on the subject:

https://twitter.com/frontlinepbs/status/392999046954356736

Quick links:

PATRIC BRC:  http://patricbrc.org/

Follow them on Twitter: https://twitter.com/PATRICBRC

Reference:

Gillespie J.J., Wattam A.R., Cammer S.A., Gabbard J.L., Shukla M.P., Dalay O., Driscoll T., Hix D., Mane S.P. & Mao C. & (2011). PATRIC: the Comprehensive Bacterial Bioinformatics Resource with a Focus on Human Pathogenic Species, Infection and Immunity, 79 (11) 4286-4298. DOI:

Video Tip of the Week: PATRIC, Pathosystems Resource Integrations Center

PATRIC is a integration portal (as the name implies) of  data concerning disease-causing infectious bacteria. Or to put it in their words:

PATRIC is the Bacterial Bioinformatics Resource Center, an information system designed to support the biomedical research community’s work on bacterial infectious diseases via integration of vital pathogen information with rich data and analysis tools.

We mentioned PATRIC at the beginning of the year in a SNPpets. Also, recently I was speaking with a threat abatement specialist who was lamenting the lack of coordinated data on infectious bacteria genomes. I was sure there was such a site, so we checked our blog here and voila, sure enough, exactly what they needed.

PATRIC indeed coordinates a lot of different types of data from disease-causing infectious bacteria. This includes data from all NIAID biodefense A/B/C pathogens. This includes hundreds of genomes from many isolation sources. For example, as of this writing there are nearly 500 genomes, including 57 complete, of Escherichia. In addition to genomic data, there are many other types of data including phylogenetic, host-pathogen protein-protein interactions, protein, pathways and more. One interesting feature, of many,  is the disease map (for mycobacterium only right now) that shows local outbreaks and alerts. There are many tools to access and analyze this data from specialized searches to browsers.

To get a good idea of what is available at PATRIC, check out the quick intro video embedded above from the PATRIC developers. They have two other video tutorials on the feature table and identifying novel proteins you also might want to check out. Also, check out the blog for more databases and resources for infectious disease pathogens.

To cite or learn more about PATRIC, see:

Gillespie, J., Wattam, A., Cammer, S., Gabbard, J., Shukla, M., Dalay, O., Driscoll, T., Hix, D., Mane, S., Mao, C., Nordberg, E., Scott, M., Schulman, J., Snyder, E., Sullivan, D., Wang, C., Warren, A., Williams, K., Xue, T., Seung Yoo, H., Zhang, C., Zhang, Y., Will, R., Kenyon, R., & Sobral, B. (2011). PATRIC: the Comprehensive Bacterial Bioinformatics Resource with a Focus on Human Pathogenic Species Infection and Immunity, 79 (11), 4286-4298 DOI: 10.1128/IAI.00207-11

There’s a database for everything, even uber-operons

I was playing around with Google Scholar’s new citation feature that allowed me to collect my papers in one place easily (worked pretty well, btw, save a few glitches, see below) when I noticed it missed a paper of mine from 2000: “Gene context conservation of a higher order than operons.” The abstract:

Operons, co-transcribed and co-regulated contiguous sets of genes, are poorly conserved over short periods of evolutionary time. The gene order, gene content and regulatory mechanisms of operons can be very different, even in closely related species. Here, we present several lines of evidence which suggest that, although an operon and its individual genes and regulatory structures are rearranged when comparing the genomes of different species, this rearrangement is a conservative process. Genomic rearrangements invariably maintain individual genes in very specific functional and regulatory contexts. We call this conserved context an uber-operon.

The uber-operon. It was my PI’s suggested term. Living and working in Germany at the time, I thought it was kind of funny. Anyway, I never really expanded more than another paper or so on that research and kind of lost track whether that paper resulted in much. I typed in ‘uber-operon’ in google today and found that it’s been cited a few times (88) and, I found this interesting: there have been a few databases built of “uber-operons.”

A Chinese research group created the Uber-Operon Database. The paper looks interesting, but unfortunately the server is down (whether this is temporary or permanent, I do not know), the ODB (Operon Database) uses uber-operons (which they call reference operons) to predict operons in the database , Nebulon is another, HUGO is another. Read the chapter on computational methods for predicting uber-operons :)

Just goes to show you, there’s a database for everything.

Oh, and back to Google Scholar citation. It did find nearly every paper I’ve published, though it missed two (including the one above) and had two false positives. Additionally, many citations are missing (like the 88 for this paper, and many others from other papers). That’s not to say it’s not useful, I find it a nice tool but it’s not perfect. You can find out more about google scholar citation here, and about Microsoft’s similar feature here.

Oh, and does this post put me in the HumbleBrag Hall of Fame? If that’s reserved for twitter, than maybe I should twitter this so I can get there :). (though I’m not sure pointing out relatively small databases based a relatively minor paper constitutes bragging, humbly or not LOL).

Next-gen sequencing, with cartoons!

Mike the Mad Biologist points to a nice article that describes aspects of the next-generation sequencing technologies with some helpful animations to illustrate the different styles. Mike goes on to describe that the sequencing itself isn’t the rate limiting step–the assembly and analysis steps are the hurdles really.

The dust certainly hasn’t settled on the strategies for that at this time–and as Mike describes the challenges may vary by species, but we are keeping an eye on some of the software that is being used (see Next-gen sequencing issue in Bioinformatics and Curious about short read sequencing? among others here).

This data is turning up in databases now (see this ENCODE data at the UCSC Genome Browser as just one example), and will continue to flood in at dramatic rates.  And the same technologies are being used for analysis of other aspects of biology (not just sequencing new species and individuals)–such as promoter binding or nucleosome positioning or RNA protein binding.  So it is worth taking a look at the underlying technology to understand what’s being sequenced.

Mike’s post: The Future of Bacterial Genomics: It’s Not the Sequencing, It’s the…

and the Wellcome Trust article he describes is: Genomics – the next generation

Bacterial Browser ala Google Maps

Came across a nice bacterial genome browser today via “Discovering Biology in a Digital World:” the Genome Projector.Genome Projector The is a map of over 100 bacterial genomes including a circular genome map, a genome map, a pathways map and a “DNAWalk” map. Put in a search term (I put “iron” in here, you know, as in ‘mining for,’ tried gold, but alas.. there is no gold in them thar… anyway…) and the hits show up as numbers in the tabs and pins in the maps. The maps are zoomable (just like GoogleMaps) and the pins are clickable with a popup to links out to databases and more information. It’s not quite as useful or in depth as perhaps IMG as a browser or Reactome or Kegg for pathways, but it’s simple and cool way to browse the genomes for more information and links to databases. Below the fold (continue reading link) are two more screenshots of my search in pathways and zoomed with clicked pin. Continue reading