The OpenHelix Blog

Comprehensive tutorials on the publicly available NCBI resources enable researchers to quickly and effectively use these invaluable resources.

Seattle, WA (PRWEB) June 8, 2010 – OpenHelix today announced the availability of three updated tutorials on NCBI resources.

The National Center for Biotechnology Information, NCBI, is home to many of the most commonly used publicly available databases and tools in molecular biology today. They house such popular and widely used databases as GenBank, PubMed, GEO, Entrez Gene, Entrez Protein, and more. NCBI also produces, maintains and updates a variety of tools, like the large family of BLAST sequence similarity searching tools and the Entrez search and retrieval tools. In addition, they provide an extensive variety of services for education, news dissemination and different types of data submission. This tutorial presents a broad overview of NCBI’s databases, tools, educational resources and data submission protocols. In addition to an update on this overview, OpenHelix has updated both it’s PubMed and GEO tutorials. PubMed is the premiere search engine for biomedical literature. More than 18 million citations from life science journals can be searched through this free service. The Gene Expression Omnibus, or GEO, is a valuable resource designed to store high-throughput gene expression and molecular abundance data. These three tutorials, in conjunction with the many other OpenHelix up-to-date tutorials on NCBI resources such as BLAST, Entrez, dbSNP, MMDB, Viral resoruces, MapViewer and others will give you a set of training resources to help be efficient and effective at accessing and analyzing genome data.

The tutorial suites, available through an annual OpenHelix subscription, contain an online, narrated, multimedia tutorial, which runs in just about any browser connected to the web, along with slides with full script, handouts and exercises. With the tutorials, researchers can quickly learn to effectively and efficiently use these resources. The scripts, handouts and other materials can also be used as a reference or for training others.

These tutorials will teach users:

NCBI Overview

*to understand the basic structure of NCBI and its different types of resources
*to navigate NCBI to find the databases and analysis tools you need
*what types of educational resources are available at NCBI
*basic data submission procedures and background information
*how to search the entire NCBI site, as well as just the subset of Entrez databases

PubMed

*basic, advanced, and Boolean search methods
*additional searching methods like the Entrez Global query and the MeSH query
*tips to understand the visual cues and displays
*to use My NCBI to customize your results and save searches which can be run and emailed regularly

Gene Expression Omnibus (GEO)

*efficient ways to query GEO for specific genes or experimental designs
*how to navigate through GEO output displays to find the specific information you want
*how to navigate GEO’s complex data architecture to search GEO by specific record types

To find out more about these and over 85 other tutorial suites visit the OpenHelix Catalog and OpenHelix. Or visit the OpenHelix Blog for up-to-date information on genomics and genomics resources.

About OpenHelix
OpenHelix, LLC, (www.openhelix.com) provides a bioinformatics and genomics search and training portal, giving researchers one place to find and learn how to use resources and databases on the web. The OpenHelix Search portal searches hundreds of resources, tutorial suites and other material to direct researchers to the most relevant resources and OpenHelix training materials for their needs. Researchers and institutions can save time, budget and staff resources by leveraging a subscription to nearly 100 online tutorial suites available through the portal. More efficient use of the most relevant resources means quicker and more effective research.

The Lancet paper, Clinical assessment incorporating a personal genome, has held my fascination this weekend (yes, I read it at the beach). Mary posted Friday and again Saturday on the paper and related NPR segment. It feels to me to be a seminal paper, though I do agree with Daniel at Genetic Future, there are a lot there we still don’t know. A large portion of the variation is in non-coding regions, and thus predictions and propensities are hard to come by with the available analysis. In fact, as he pointed out, many of the coding region variations have little information as to their effect on disease. I would add also that even if we get to that holy grail of $1,000 to sequence a personal genome, this kind of extensive analysis would still be time and cost-prohibitive for the vast majority of sequenced genomes.

Yet, as with all early steps in science and medicine, there’s missing pieces, large gaps and huge efforts (think “space travel,” “computers,” “microwave ovens,” “internet,”) that over time become inexpensive and commonplace (ok, so the former isn’t necessarily “inexpensive”). Sequencing genomes will become inexpensive before the analysis does, but both will come. And I think this paper is pointing to that future.

The other hurdle to large scale personal genomics I see (of course) is the understanding and use of the genomics and data resources. The authors use a large (and excellent, in my opinion) suite of genomics resources to do obtain data and do their analysis. I’ll list them here with links in alphabetical order:

dbSNP (T)
GVS (T)
HapMap (T)
HGMD
OMIM (T)
PharmGKB
PolyPhen
PubMed (T)
SIFT
UniProt (T)

All of these resources have a wealth of data, but even then, that is a lot of analysis and familiarization that is needed with each tool. Each tool does have documentation and tutorials, and of course OpenHelix has tutorials on many of the ones mentioned (those with linked “T”s after the name). Still, this one analysis took a large number of tools and familiarization.

The paper does have a pretty good figure (figure 1) outlining the analysis process. For example, they SIFTed the genome to find gene-associated, non-synonymous, rare and novel and disease associated variations and then analyzed those using dbSNP, HGMD, OMIM and PubMed to analyze something like HFE2 which might have an association with Haemochromotosis. One of my quibbles with the paper, as often is with these papers, is that there isn’t a good methods ‘walk-through’ of the paper using something like Galaxy or Taverna in a history or workflow that would help reproduce the analysis.

We also have a tutorial I’d like to point you to, one that walks through a similar process and teaches users the basics of walking through that process. You can find this tutorial here, it’s free and publicly available. The tutorial walks the user through the analysis of a gene variation, in this case in the CYPC9 that effects an individual’s response to Warfarin. There is a similar variation (different gene, affects same drug response) in the paper. The tutorial uses the NIEHS SNPs site to get an overview of the variation including SIFT and PolyPhen predictions, then to the UCSC Genome Browser to find an overview of the region, walks through the dbSNP information and does a quick tag SNP analysis using GVS. That tutorial is only one very small step in what will have to be a immense education into genomics and genomics resources.

That is all to point out that the paper is an fascinating first step, and as a first step suggests the gaping holes we will have in bringing personal genomics to medicine.

Ashley, E., Butte, A., Wheeler, M., Chen, R., Klein, T., Dewey, F., Dudley, J., Ormond, K., Pavlovic, A., & Morgan, A. (2010). Clinical assessment incorporating a personal genome The Lancet, 375 (9725), 1525-1535 DOI: 10.1016/S0140-6736(10)60452-7

If you are someone who’s spent a lot of time deep in the recesses of databases — deeper than the average end users — sometimes you find some really interesting  things.  Sometimes they are instructive, such as: hmm…I didn’t realized mice had a bone there until I was working the the anatomical hierarchy at Jax…  Sometimes they are creepy.  Buried in the MeSH hierarchy was about the most repulsive term I’d ever seen in a controlled vocabulary.  I complained about this to them probably 10 years ago, and just realized it doesn’t appear in MeSH 2010, finally.

But then there are other times when a database search leaves one ROFL.  That happened some time ago when I came across this odd tidbit in a search for gray hair genes. It generated some discussion among my sphere of colleagues about other funny things we’ve come across in the databases.

Well, there’s one whole blog dedicated to the pursuit of humor in NCBI’s PubMed.  I just found out from the #scio10 tweets from the ScienceOnline2010 meeting that they have found a new home on the Discover blogs collection!

NCBI ROFL: Hello, world! (again)

Congrats to them.  If you find you need to chuckle at the literature sometimes–or need a funny sample for a presentation perhaps, check them out at their new home. They also take suggestions. So if you find something in PubMed that cracks you up, send it along.

Inigo Montoya: You keep using that word. I do not think it means what you think it means.

So I was reading the NLM Technical Bulletin for November-December this morning. (Yeah, gripping, I know–and a month late.  You know how the holidays are…).  But I came across something intriguing.  Here’s what it says:

Hmm…webcasts.  Ok.  But how are they going in?  And what are the sources?  How are they annotated?  So let’s have a look.  At PubMed I clicked on advanced searches.  Checked the “Type of Article” box.  And let the search run.

I got 3. Here are my results:

I went to the first one at the publisher’s site.  There’s a brief abstract-like introduction.  There you can link to 2 videos.  There is a patient examination, pre- and post-treatment.  There’s a word doc with the video legend.  Here’s the whole legend…I’m not really sure why this need to be delivered in a word doc and not on that page.

Legend: A 43-year-old man with thiamine deficiency, manifested as gait and eye movement abnormalities without encephalopathy (video 1) that markedly improves following prompt diagnosis and empiric thiamine replacement (video 2).

Ok. This is useful stuff for neurologists, I’m sure. But it’s not what I would have called a “webcast”. Maybe it’s just me…I would have thought that was pretty much essentially a figure in this paper.

The second one I didn’t expect to have access to.  But when I got to the site it appeared that I did.  My German is non-existent, but I was able to decipher the word “podcast”.  So I clicked. I had access to a German podcast.  That’s cool.  But also isn’t what I would have called a “webcast”.

Ok. The next one I don’t have access to.  I can’t assess what it really is.  But I have to say 2/3 are not what I expected as webcasts…

Maybe it’s semantic.  I thought webcasts would be seminars people gave on their work, or special published items like the training materials we have, or something.  Maybe recordings from conference presentations.

I like the idea–I think we need to start thinking about ways to make these types of valuable publications/presentations available.  I was wondering how the content would be indexed by the NLM.  All of our training webcasts have the full script available as text, but I would say that’s rather uncommon.  Most cases have a title and a bare abstract at best in my experience.

What do you think?  Is that what you call webcasts?  My current assessment of this development = Idea: excellent.  Execution (currently): eh.

++++++++++++++++++
MacDonald, R., Stanich, P., Monrad, P., & Mateen, F. (2009). Teaching Video NeuroImages: Wernicke encephalopathy without mental status changes Neurology, 73 (20) DOI: 10.1212/WNL.0b013e3181c1de31

The two earliest web-based bioinformatics resources that I can remember relying on in my career were Pedro’s List and NCBI.  (For those of you who need a little nostalgia trip you can see a copy of Pedro’s list here.) There are plenty of descendants of Pedro’s list in various forms–including our recently launched resource search tool.  But the National Center for Biotechnology Information (NCBI) interface has kinda been…well…comfortingly stable–for a really long time.  I looked in the Wayback Machine to see what the older interfaces used to look like.   I was able to find one variant from 1997 which I had forgotten about until I saw it.  But then I kept looking and found the version I am most familiar with starting in 1999.  If you compare 1999 to 2009 you will see essentially the same layout.  Here is a comparison of the previous interfaces, and then the new one:

[caption id="attachment_2580" align="alignleft" width="300" caption="NCBI interfaces through the years"][/caption]

Well, that’s all changing now!  The NCBI is doing a MAJOR overhaul of the interfaces.  You can examine the homepage look at the  Preview site here (link may break when they move over to production with it), and you can look at the PubMed changes here, and even start using the PubMed preview site here.

This is a huge break with the past, and like all new interfaces will take a little time to get used to.  But I have to say I like the organization.  The left navigation will make finding the tools easier.  The “Popular” box will be quick access to the most frequently used items.  Highlights and news are available still as well.  There are some things I’ll miss. We liked the site map layout to explain the features in an overview sort of way, and the preview page doesn’t link to that–it links to the alphabetical list.  Might change, though.

Anyway–I think the new look is nice and effective.  Of course we’ll have to update all of our NCBI tutorials with new shots of the interfaces.  But it looks like the underlying tools don’t change much conceptually–but they may move the location of the items (like the PubMed filters).  So as soon as the interface becomes the main site and appears to be stable we’ll make our changes.

This short Tip of the Week introduces the new interface briefly to get you starting to think about how to navigate around.  Check it out!

NCBI: http://www.ncbi.nlm.nih.gov/

Comprehensive tutorial on the publicly available iHOP database enables researchers to quickly and effectively use this invaluable next-generation resource.

Ok, so I just got my new iPhone 3Gs. I couldn’t resist. Anyway, my contract on my first generation iPhone was up. So, it was time to reconfigure and explore the huge number of apps out there for the iPhone.

I use the iPhone for a lot of things, directions, finding out what stores are in the area, keeping my grocery list, listening to music, watching shows, browsing the web, keeping my calendar and contacts and a bunch more. Oh, and to make and receive phone calls .

I’ve read past posts on other blogs about scientific apps for the iphone, I decided it was time to check out what apps there are now.

I’ve found a few I like, some that might work (I do computational genomics now, so I haven’t tried the ones for the bench), and one that has nothing to do with biology (directly anyway), but I am in love with. Follow me below the fold.

There are a lot of research papers out there, more than ever. Along with the good news (increasing knowledge), comes some bad news: increasing duplication and plagiarism, more often than not going undetected. The developers of eTBLAST, which is a great tool we’ve had a tip on before, have created another tool using an eTBLAST search of Medline and other databases to find highly similar citations: Deja Vu.

These similar citations could be legitimate; a review of a previous article, an author using similar wording of an abstract from a previous paper for new research (the eTBLAST search can only search titles and abstracts), sanctioned duplications, etc, etc. as the author of the post “Deja Boo” points out. There are some real instances of duplications (authors attempting to pad their CVs) and plagiarism (stealing words and research). An earlier example (before Deja Vu) found at Panda’s Thumb is of a creationist attempting to pad a CV and look more legitimate. Errami and Gardner (two of the developers of the tool) published a paper in Nature earlier this year with many such instances of (and another in Science, reported on here with some interesting discussion) duplication and plagiarism.

Still, the database needs to be viewed with caution. Of the 74,792 ‘highly similar and duplicate citations’ found, 92% have not be verified. Of the 8% left that have been verified (this has to be done by manual curation), 65% have been found to be probably legitimate (as stated above) and 35% to be duplicates. But even the duplicates aren’t necessarily nefarious. Since full texts are not available, it is often the case that the duplication might be perfectly understandable (reusing an abstract with some minor changes for new research, etc). Still, it is a tool that, with some work, can help tremendously in that search for true duplicates and plagiarism, and perhaps even just the threat of it might lower the instances?  :D

So, with that in mind, this week’s tip of the week is a quick view of “Deja Vu.”

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 NCBI’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.

I was looking through PubMed for something recently, and I happened upon the strangest title I’ve ever seen for an article….

http://www.ncbi.nlm.nih.gov/pubmed/12079806

Ah, what?  Why is this in here?  Isn’t that odd….Seems to be a series from this publication.  But I can’t fathom why health column questions are titles….