This week’s Video Tip of the Week demonstrates PANDA, a tool for generating and examining annotations that are available for a list of genes, and evaluating them in the context of pathways. Two great tastes that taste great together, you know? So have a look at how PANDA can help you and your team to annotate lists with both curated and personalized details, and see relevant connections among items in your lists. Note: all the data appears to be human–there are no actual pandas here, except one of the available icons to use to designate your gene list. If you are looking for actual panda genome stuff, try here.
We have developed PANDA (Pathway AND Annotation) Explorer, a visualization tool that integrates gene-level annotation in the context of biological pathways to help interpret complex data from disparate sources. PANDA is a web-based application that displays data in the context of well-studied pathways like KEGG, BioCarta, and PharmGKB.
Their nice intro video here will introduce the basic features. One thing though–the sample list that is used has been moved to the GitHub repository, and the one used is GENELISTB in the EXAMPLES folder. I copied that to an excel file and did the same thing as illustrated in the video and it worked great. [And yes, I know y'all hate excel, but it works for biologists, they'll get the annotation thing this way.]
It was really easy to move from my sample gene list into a KEGG pathway, and so clear which genes in my list were components of this pathway because of the overlay icon that PANDA let you assign to your data. And you can further overlay that with the DGIdb, OMIM, HPO (yes, I mentioned to you recently we were going to need to understand this), MalaCards, and PharmGKB links too.
So the idea is whatever kind of -omics data you have, as long as it is tied to a gene, you can upload it and explore the relationships in more detail with these handy mappings and additional details.
And there is a way for you to have multiple colleagues access your annotation set and add further details.
But you aren’t limited to the pathways already in the PANDA system, there are also ways to customize what you know about your pathways and annotations. There is a second video that will offer more detail on that using a network with Cytoscape. You can get the other video from the Mayo Bioinformatics YouTube channel or on their site.
Hart, S., Moore, R., Zimmermann, M., Oliver, G., Egan, J., Bryce, A., & Kocher, J. (2015). PANDA: pathway and annotation explorer for visualizing and interpreting gene-centric data PeerJ, 3 DOI: 10.7717/peerj.970
The helpful folks at NCBI have been ramping up their outreach. I’ve been watching a lot of their webinars, and they are trying different styles. The more traditional ones that are about the length of a seminar, and lately shorter quick-hit types of things. Both of them are valuable. Some topics need a good foundational overview sort of coverage. Others are quick tips or lighter-weight tools that don’t need the whole hour. This is much like our strategy of tutorial suites and tips-of-the-week.
In addition, they are also offering short videos to help you become aware of tools they have. These appear on the NCBI YouTube channel. This week’s tip of the week highlights one of their new videos, about the NCBI Tree Viewer for creating and viewing a dendrogram and the possible relationships among sequences. The larger Genome Workbench tool has a tree view function too, and you can make a tree from your Web BLAST results, but this version is a stand-alone option.
They provide several examples on the project landing page that you can explore. You can try out the different layouts easily. And you can upload your own data and try it out too. I created a tree with BLAST on some proteins I’m interested in. I saved it, and then re-uploaded it to the Tree View page and was able to do more with it–including obtaining a PDF.
As I’ve mentioned before, once I start looking over some new tools I’m often led to others in the same arena that offer related but different features. That’s what happened when I looked at the Proband iPad app for human pedigrees. I noted that they are using important community standards, and I decided to follow those threads a bit. That led me to last week’s tip, the Human Phenotype Ontology (HPO).
HPO has been around for a while and I’ve been aware of it, but this recent re-investigation made me realize how mature it has become, and I was impressed with the amount of adoption there’s been in the genomics community in the big projects. But it also led me to some new tools that I hadn’t encountered before. This week’s tip highlights PhenogramViz–combining my appreciation for controlled vocabularies, standards, and data visualization.
The PhenogramViz team illustrates how they analyze and visualize gene-phenotype relationships
Here’s now the PhenogramViz team describes their tool:
A tool that automatically analyses and visualizes gene-to-phenotype relations for a set of genes affected by CNV of a patient and a set of HPO-terms representing the symptoms of said patient. The tool makes full use of the cross-species phenotype ontology “uberpheno” (see here).
So if you have a patient with copy-number variation issues in their genome, you may be able to use this tool to lead to the genes in that CNV segment that convey certain phenotypes. So the goal–as stated in their paper linked below–is to assist with the clinical interpretation of the genome alterations.
The additional layer of this effort that I find useful is that they use another ontology to take this even further for supporting information. They employ the “Uberpheno” cross-species phenotype ontology to find further details in model organisms.
I’ll let you get a sense of how this works with one of their tutorial videos from their YouTube channel. They have others too–which will help you with different aspects on everything from installation to analyses. I’ll embed the one that shows how you start with a list of patient symptoms or phenotypes, then loading the CNVs or genes, then from the results list you can simply click for graphical representations of the gene-phenotype relationships. Then with the Cytoscape tools you can interact with the “phenograms” in more detail. There’s no sound, you can read the guidance in the callouts.
The videos include some abbreviations–like HPO. That’s why I talked last week about the Human Phenotype Ontology. I was prepping you for this one. And in another video (Prioritization of pathogenic CNVs) they reference the scoring strategies, which you will find need further explanation in their paper linked below (Journal of Medical Genetics one). I would spend some time looking over how the scoring and ranking happens to understand what’s shown.
Although the focus of this is using the data for human diagnosis, I think it could also help researchers to choose more appropriate animal model for further testing. There are lots of complaints about the unsuitability of animal models for a range of subjects–but refining those choices would also be a huge benefit. Saving resources by helping to choose the right animal model would be another worthwhile use of this tool.
Köhler, S., Doelken, S., Mungall, C., Bauer, S., Firth, H., Bailleul-Forestier, I., Black, G., Brown, D., Brudno, M., Campbell, J., FitzPatrick, D., Eppig, J., Jackson, A., Freson, K., Girdea, M., Helbig, I., Hurst, J., Jahn, J., Jackson, L., Kelly, A., Ledbetter, D., Mansour, S., Martin, C., Moss, C., Mumford, A., Ouwehand, W., Park, S., Riggs, E., Scott, R., Sisodiya, S., Vooren, S., Wapner, R., Wilkie, A., Wright, C., Vulto-van Silfhout, A., Leeuw, N., de Vries, B., Washingthon, N., Smith, C., Westerfield, M., Schofield, P., Ruef, B., Gkoutos, G., Haendel, M., Smedley, D., Lewis, S., & Robinson, P. (2013). The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data Nucleic Acids Research, 42 (D1) DOI: 10.1093/nar/gkt1026
Köhler S., Doelken S.C., Ruef B.J., Bauer S., Washington N., Westerfield M., Gkoutos G., Schofield P., Smedley D. & Lewis S.E. & (2013). Construction and accessibility of a cross-species phenotype ontology along with gene annotations for biomedical research., F1000Research, PMID: http://www.ncbi.nlm.nih.gov/pubmed/24358873
Köhler, S., Schoeneberg, U., Czeschik, J., Doelken, S., Hehir-Kwa, J., Ibn-Salem, J., Mungall, C., Smedley, D., Haendel, M., & Robinson, P. (2014). Clinical interpretation of CNVs with cross-species phenotype data Journal of Medical Genetics, 51 (11), 766-772 DOI: 10.1136/jmedgenet-2014-102633
Shannon P., Markiel A., Ozier O., Baliga N.S., Wang J.T., Ramage D., Amin N., Schwikowski B. & Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks., Genome research, PMID: http://www.ncbi.nlm.nih.gov/pubmed/14597658
Typically, our Tips-of-the-Week cover a specific software tool or feature that we think readers would maybe like to try out. But this week’s tip is a bit different. It’s got a conceptual piece that is important, as well as referencing several software tools that work with this crucial concept to enable interoperability of many tools, helping us link different data types in a common framework.
Conceptually, the Human Phenotype Ontology (HPO) is much like other controlled vocabulary systems you may have used in genomics tools–like Gene Ontology, Sequence Ontology, or others that you might find at the National Center for Biomedical Ontology. We’ve covered the idea of broad parent terms, increasingly precise child terms, and standard definitions in tutorial suites. It’s important to standardize and share the same language to describe the same things among different projects, software providers, and as we move more genomics to the clinic, sharing descriptors for human phenotypes and conditions will be crucial.
The concepts and strategies are becoming mature at this point. and we now have lots of folks who agree and want to use these shared descriptors. A really nice overview of the state of phenotype descriptions and how to use them for discovery and for integration across many data resources was published earlier this year: Finding Our Way through Phenotypes. It also offers recommendations for researchers, publishers, and developers to support and use a common vocabulary.
For this week’s video, I’m highlighting a lecture by one of the authors of that paper, Peter Robinson. It’s a seminar-length video, but it covers both the key conceptual features of the HPO, provides some examples of how it can be useful in translational research settings, and also describes the range of tools and databases that are using the HPO now. I think it’s worth the time to hear the whole thing. The audio is a bit uneven in parts, but you can get the crucial stuff.
The early part is about the concepts of specific terms, synonyms, and shared terms that can mean completely different things (think American football and European football). He describes the phenotype ontology. There are examples of research that leads to phenotypes that are then used as discovery and diagnostic tools. He talks about tools that utilize the HPO right now, including Phenomizer for obtaining or exploring appropriate terms, PhenIX, Phenotypic Interpretation of eXomes for prioritization of candidate genes in exome sequencing data sets. There is also PhenoTips, that can help you to collect and analyze patient data (and also edit pedigrees).
Many large scale projects and key genomics tools employ the human phenotype ontology.
He also notes how tools like DECIPHER, NCBI Genetic Testing Registry, GWAS Central, and many more include the human phenotype vocabulary. This is a great sign for a project like this, that’s it is being adopted by so many groups and tools world-wide. They’ve also worked with key large-scale projects in this arena to ensure that the vocabulary is suited and workable, and update them when needed. They credit OMIM and Orphanet as being crucial to their efforts as well. As part of the Monarch Initiative, there seems to be solid support going forward as well.
There are more tools to discuss, but I’m going to save those for another post. This one is already loaded with things you should check out, so be sure to come back for further exploration of the HPO-related tools and projects that are worth exploring.
References: Deans A.R., Suzanna E. Lewis, Eva Huala, Salvatore S. Anzaldo, Michael Ashburner, James P. Balhoff, David C. Blackburn, Judith A. Blake, J. Gordon Burleigh, Bruno Chanet & Laurel D. Cooper & (2015). Finding Our Way through Phenotypes, PLoS Biology, 13 (1) e1002033. DOI: http://dx.doi.org/10.1371/journal.pbio.1002033
Kohler, S., Doelken, S., Mungall, C., Bauer, S., Firth, H., Bailleul-Forestier, I., Black, G., Brown, D., Brudno, M., Campbell, J., FitzPatrick, D., Eppig, J., Jackson, A., Freson, K., Girdea, M., Helbig, I., Hurst, J., Jahn, J., Jackson, L., Kelly, A., Ledbetter, D., Mansour, S., Martin, C., Moss, C., Mumford, A., Ouwehand, W., Park, S., Riggs, E., Scott, R., Sisodiya, S., Vooren, S., Wapner, R., Wilkie, A., Wright, C., Vulto-van Silfhout, A., Leeuw, N., de Vries, B., Washingthon, N., Smith, C., Westerfield, M., Schofield, P., Ruef, B., Gkoutos, G., Haendel, M., Smedley, D., Lewis, S., & Robinson, P. (2013). The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data Nucleic Acids Research, 42 (D1) DOI: 10.1093/nar/gkt1026
Köhler, S., Schulz, M., Krawitz, P., Bauer, S., Dölken, S., Ott, C., Mundlos, C., Horn, D., Mundlos, S., & Robinson, P. (2009). Clinical Diagnostics in Human Genetics with Semantic Similarity Searches in Ontologies The American Journal of Human Genetics, 85 (4), 457-464 DOI: 10.1016/j.ajhg.2009.09.003
Zemojtel, T., Kohler, S., Mackenroth, L., Jager, M., Hecht, J., Krawitz, P., Graul-Neumann, L., Doelken, S., Ehmke, N., Spielmann, M., Oien, N., Schweiger, M., Kruger, U., Frommer, G., Fischer, B., Kornak, U., Flottmann, R., Ardeshirdavani, A., Moreau, Y., Lewis, S., Haendel, M., Smedley, D., Horn, D., Mundlos, S., & Robinson, P. (2014). Effective diagnosis of genetic disease by computational phenotype analysis of the disease-associated genome Science Translational Medicine, 6 (252), 252-252 DOI: 10.1126/scitranslmed.3009262
Girdea, M., Dumitriu, S., Fiume, M., Bowdin, S., Boycott, K., Chénier, S., Chitayat, D., Faghfoury, H., Meyn, M., Ray, P., So, J., Stavropoulos, D., & Brudno, M. (2013). PhenoTips: Patient Phenotyping Software for Clinical and Research Use Human Mutation, 34 (8), 1057-1065 DOI: 10.1002/humu.22347
But I can also see the need to collect some of this information on mobile devices, during conversations with patients and families. Or even at the next family holiday gathering. And perhaps an iPad based tool might be handy for those sorts of things. I found out about one of them, Proband, via twitter:
Since I don’t have an iPad, I can’t evaluate it myself. But you may be interested in the review that was done of it a while back. Proband App lets anyone be a genetic counselor…or at least draw like one. The team is actively seeking out feedback, though, and some of these things may have changed. They have been presenting posters at conferences like ACMG and ASHG, and also reaching out in other ways too. When I emailed them with a question, they were very responsive. They also provided a data sheet with updates listed from March, so I’m certain they are continuing to actively develop this tool.
Be sure to pick the right one on the iTunes store.
Typically we like to highlight open access tools, but seemed to me the $4.99 app price isn’t exactly prohibitive if you already own an iPad. Just be sure to find the right one in the Apple store–it’s not the music one. Oh–related to this though–they are also considering other platforms. And they are piloting a server piece that will integrate the pedigree data with other parts of electronic health records systems. In another article about the team’s work (Genomic Singularity Is Near), they add more details on their larger goals:
“Miller asserts that in the future, Proband will be able to incorporate test results and other personal health information stored in electronic health records. “Querying pedigrees based on scientific and medical questions is another near-term goal,” he adds.”
Another point I’ll just quickly make about Proband: they noted in that Singularity article, and in one of their meeting abstracts that they are conforming to the standards established in the field:
The app enables the user to create complex family pedigrees by fully implementing, with minor exceptions, the nomenclature outlined by the Pedigree Standardization Work Group (PSWG) in Bennett et al. 2008.
This is crucial, of course, and I’m glad to see this. I’ve attached the reference for that Bennett paper below, and it has really helpful guidance on the symbols and meanings, and even astonishingly complex assisted reproduction relationships like this planned adoption: “Couple contracts with a woman to carry a pregnancy using ovum of the woman carrying the pregnancy and donor sperm.” (Fig 3. Wow, that’s some diagram. PS: *cough* googlescholar for pdf). I also saw that Proband are using the Human Phenotype Ontology (HPO). I expect to be exploring HPO in some other upcoming tips as well–this is going to be increasing important as we collect more sequencing data from individuals and try to figure out what it all means.
So if you need to draw pedigrees for clinical or research situations, or maybe for genealogies, you might want to have a look at this app. It might be an engaging teaching tool as well.
Reference: Bennett R.L., Kathryn Steinhaus French, Robert G. Resta & Debra Lochner Doyle (2008). Standardized Human Pedigree Nomenclature: Update and Assessment of the Recommendations of the National Society of Genetic Counselors, Journal of Genetic Counseling, 17 (5) 424-433. DOI: http://dx.doi.org/10.1007/s10897-008-9169-9
Edited to note that the app is now freely available, and changed the screen shot accordingly.
Browsing around genomic regions, layering on lots of associated data, and beginning to explore new data types I might come across are things that really fire up my brain. For me, visualization is key to forming new ideas about the relationships between genomic features and patterns of data. But frequently I want to take this to the next step–asking where else these patterns appear, how many other instances of this situation are there in a data set, and maybe adding additional complexity to the problem and refine the quest. This is not always easy to do with primarily visual software tools. This is when I turn to tools like the UCSC Table Browser, BioMart, and InterMine to handle some list of genes, or regions, or features.
We’ve touched on all of these before–sometimes with full tutorial suites (UCSC, BioMart), and sometimes as a Tip of the Week, InterMine and InterMine for complex queries. Learning about the foundations of these tools will let you use various versions or flavors of them at other sites. I love to see tools that are re-used for different topics when that’s possible, rather than building a whole new system. There are ModENCODE, rat, yeast mines, and more. This week’s tip is about one of those others–TargetMine is built on the InterMine foundation, with a specific focus on prioritizing candidate genes for pharmaceutical interventions. From their site overview, I’ll add this description they use:
TargetMine is an integrated data warehouse system which has been primarily developed for the purpose of target prioritisation and early stage drug discovery.
For more details about their framework and philosophy, you should see their papers (linked below). The earlier one sets out the rationale, the data types, and the data sources they are incorporating. They also establish their place in the ecosystem of other databases in this arena, which helps you to understand their role. But you should see the next paper for a really good grasp of how their candidate prioritization work with the “Integrated Pathway Clusters” concept they’ve added. They combined data from KEGG, Reactome, and NCI’s PID collections to enhance the features of their data warehouse system.
This week’s Video Tip of the Week highlights one of the tutorial movies that the TargetMine team provides. There’s no spoken audio with it, but the captions that help you to understand what’s going on are in English. I followed along on a browser with their example–they have a sample list to simply click on, and you can see various enrichments of the sets–pathways, Gene Ontology, Disease Ontology, InterPro, CATH, and compounds. They call these the “biological themes” and I find them really useful. You can create new lists from these theme collections. They also illustrate the “template” option–pre-defined queries with typical features people may wish to search. The example shows how to go from the list of genes you had to pathways–but there are other templates as well.
Another section of the video has an example of a custom query with the Query Builder. They ask for structural information for proteins targeted by acetaminophen. It’s a nice example of how to go from a compound to protein structure–a question I’ve seen come up before in discussion threads.
In their more recent paper (also below), they have some case studies that illustrate the concepts of prioritizing targets for different disease situations with their system. They also expand on the functions with additional software to explore the pathways: http://targetmine.mizuguchilab.org/pathclust/ .
So have a look at the features of TargetMine for prioritization of candidate genes. I think the numerous “themes” are a really useful way to assess lists of genes (or whatever you are starting with).
Chen, Y., Tripathi, L., & Mizuguchi, K. (2011). TargetMine, an Integrated Data Warehouse for Candidate Gene Prioritisation and Target Discovery PLoS ONE, 6 (3) DOI: 10.1371/journal.pone.0017844
Chen, Y., Tripathi, L., Dessailly, B., Nyström-Persson, J., Ahmad, S., & Mizuguchi, K. (2014). Integrated Pathway Clusters with Coherent Biological Themes for Target Prioritisation PLoS ONE, 9 (6) DOI: 10.1371/journal.pone.0099030
Kalderimis A., R. Lyne, D. Butano, S. Contrino, M. Lyne, J. Heimbach, F. Hu, R. Smith, R. Stěpán, J. Sullivan & G. Micklem & (2014). InterMine: extensive web services for modern biology, Nucleic Acids Research, 42 (W1) W468-W472. DOI: http://dx.doi.org/10.1093/nar/gku301
We’ve been doing training on the UCSC Genome Browser for over 10 years now. We’ve seen it grow from just a few genomes and a few tracks to the enormous trove of information it is today. In fact, one of the toughest things about training is how to balance all the new information and features with the foundational things one needs to really grok the framework and the functionality.
In the training materials that we have today, we touch briefly on the amino acid display in the reference genome track. And one of our very first Tip-of-the-Week blog posts was about how to visualize the 3-frame translation on the browser. But we don’t have time to go into every track and show the options that you can employ for all the displays. We stress that you should check each track for more features, but in the short workshops we can only cover a few examples.
The UCSC team has started a great video series that can supplement the main training suites that are available. You can see their whole YouTube channel here: UCSC Genome Browser. But today I’ll highlight one of the recent ones so you can see the type of help they offer. And it covers a feature–display of amino acids, codon numbers, and amino acid variation information, that we don’t have time to do in our workshops.
How do I identify codon numbers with the UCSC Genome Browser?
Bob Kuhn and Pauline Fujita’s video here, as well as the others in the series, focus on a specific task and give great tips and details. Be sure to check out the others as well, and subscribe to their channel to be notified when new ones become available.
Note: UCSC has sponsored our training materials for years, and because of this sponsorship they are freely available to everyone.
Rosenbloom K.R., J. Armstrong, G. P. Barber, J. Casper, H. Clawson, M. Diekhans, T. R. Dreszer, P. A. Fujita, L. Guruvadoo, M. Haeussler & R. A. Harte & (2014). The UCSC Genome Browser database: 2015 update, Nucleic Acids Research, 43 (D1) D670-D681. DOI: http://dx.doi.org/10.1093/nar/gku1177
The multiple sequence alignment editing question recently on our What’s the Answer? feature was popular. We have covered MSA editors in the past, and we include a bit on Jalview in our Clustal tutorial, but I hadn’t revisited them lately. In preparation for that post I specifically looked over at the Jalview site, and I realized that they have recently provided a number of training videos to help people use their tools. So this week’s tip of the week will highlight them.
At the Jalview site, they give this brief description of the features:
Jalview is a free program for multiple sequence alignment editing, visualisation and analysis. Use it to view and edit sequence alignments, analyse them with phylogenetic trees and principal components analysis (PCA) plots and explore molecular structures and annotation.
On the Jalview online training Youtube channel, they have a number of videos. Some are general overview, some are specific tasks. For a general overview of what it does, this intro video will help you to decide if it’s a tool that would help you:
If you are ready to try it out, there are some handy tips in this video with more details about actually using the features of the software. It covers basic navigation, understanding the interface layout, working on editing, and good tips for accomplishing things efficiently.
For more of the philosophy and foundations of Jalview, check out their paper (linked below). And check out their other videos to go further.
Waterhouse, A.M., Procter, J.B., Martin, D.M.A, Clamp, M. and Barton, G. J. (2009)
“Jalview Version 2 – a multiple sequence alignment editor and analysis workbench”
Bioinformatics25 (9) 1189-1191 doi: 10.1093/bioinformatics/btp033
Generally we like to highlight new features and new tools from bioinformatics software providers. But this week we wanted to introduce some new features of our own OpenHelix site. If you’ve been using the site for a while, you will have noticed that recently we rolled out some changes. All the same tutorial materials and tips are available, but we’ve provided new ways to access them.
The most important thing about the new site is accessing our training suites.
Access the training video, slides, and exercises on the training suite page.
This is now quicker with buttons right from the main page at OpenHelix: full catalog, or list of free tutorials. And when you find a suite you want to watch (like the UCSC Genome Browser one shown here as an example), it loads right on that landing page instead of requiring another click to launch a new window. If you still want the larger original size version in a new window, though, that’s still available from the button below. Access to our slides, handouts, and exercises is still there–right below the video. And you can still quickly hop over to the site that’s described on the page with the “Visit the Resource” link.
Further down on the page we have the links to related content. This can be other tutorials about this resource (for example, UCSC Genome Browser Advanced Topics), or other genome browsers. We also collect the blog posts related to this resource that may offer new tools or features and link them from this page. And we also text-mine the BiomedCentral open-access publications to seek out those citing this resource–this way you can see what researchers are doing with the tools in their research programs.
Our search feature still provides access to our complete collection of resources that we’ve examined over the years. But the search results are now also refined to let you tab to those with our popular Video Tip of the Week subset if you just want to locate those with short video tips.
With that overview, I’ll also offer this week’s video tip overview of the new site as well.
Our basic philosophy remains the same, as we explained on our paper (linked below).
To accomplish its outreach mandate, bioinformatics education needs to do a minimum of four things:
raise awareness of the available resources
enable researchers to find and evaluate resource functionality
lower the barrier between awareness and use of a resource
support the continuing educational needs of regular resource users
We want to provide introductory training on many of the core resources in bioinformatics, and help educators and trainers elsewhere to provide this to students and staff who will need to access these tools for their research. We hope you like the new look. If you have any issues, let us know.
Williams J.M., M.E. Mangan, C. Perreault-Micale, S. Lathe, N. Sirohi & W. C. Lathe (2010). OpenHelix: bioinformatics education outside of a different box, Briefings in Bioinformatics, 11 (6) 598-609. DOI: http://dx.doi.org/10.1093/bib/bbq026
This week’s tip isn’t about a specific tool–but a really interesting look at how a tool was used in the context of some general public outreach messaging. Recently I posted about Aquaria, a new tool available to let biologists explore protein structures, mutations, and domains in user-friendly ways. But an interesting example of how the information about protein structures can be used to drive understanding came from a video animation of protein accumulation in Alzheimer’s. Just have a look at the video first and enjoy it. How cool is that clathrin basket pulling the vesicle in?
Christopher Hammang’s “Alzheimer’s Enigma” which explores the neurons of the human brain, and reveals how normal protein breakdown processes become dysfunctional and result in plaque formation during Alzheimer’s disease.
I found out about it as I was looking at the upcoming VIZBI talks and exploring their site for other features. In the VizbiPlus section there are a number of excellent animations of molecular processes, and this video was one of them. Be sure to watch for other tweets with the #vizbi hashtag for the next few days. I bet you’ll see some amazing tools and visualizations, as always.
Recently I mentioned the longer, more comprehensive, video from the Aquaria team, but I didn’t use that for my tip–I just used the short version overview. But the longer version had this extra bonus piece of how their software had been used by this animator. Here is Christopher Hammang, creator of this video, describing how he used the Aquaria information to generate the structural model for his animation:
Often it helps people to see how someone else used a tool for a project to get a better grasp of it. And this seemed like such a compelling and unusual example, I wanted to highlight it.
So again I’ll point you to the Aquaria tool tip from earlier this month to explore more, now with an understanding of an example of its use. But I would also encourage you to have a look at the other animations coming out of VIZBI at the VizbiPlus page. I swear, the animated intestine is way cooler than you might expect. The diabetes + insulin receptor videos are really informative and helpful. A cancer video illustrates a misbehaving p53. Go look.
Reference: O’Donoghue S.I., Kenneth S Sabir, Maria Kalemanov, Christian Stolte, Benjamin Wellmann, Vivian Ho, Manfred Roos, Nelson Perdigão, Fabian A Buske, Julian Heinrich & Burkhard Rost & (2015). Aquaria: simplifying discovery and insight from protein structures, Nature Methods, 12 (2) 98-99. DOI: http://dx.doi.org/10.1038/nmeth.3258