Category Archives: Tip of the Week

MorphoGraphX sample images, via: DOI: http://dx.doi.org/10.7554/eLife.05864.004

Video Tip of the Week: MorphoGraphX, morphogenesis in 4D

This week’s Video Tip of the Week covers a different aspect of bioinformatics than some of our other tips. But having been trained as a cell biologist, I do consider imaging software as an important part of the crucial software ecosystem. Also, since it’s a holiday week and traffic may be light in the US, I thought something really nice to look at was a good plan.

I found out about this software via ResearchBlogging, via The Node’s Anne-Lise Routier-Kierzkowska’s post about the work she and her team have done: MorphoGraphX: A platform for quantifying morphogenesis in 4D. It’s a nice overview of the kinds of things that this software can do, and what the origins were. I really like the backstory types of posts from researchers writing about their own work–go read that, I’m not going to replicate it here.

On the MorphoGraphX site, the other things they describe as features of their software include:

  • Shape extraction
  • Growth analysis
  • Signal quantification
  • Protein localization

The introductory video from their team is a nice overview. But you should definitely see their paper, which has additional video figures that show more of the features and the utility. There are several different video figures that are fascinating to watch. Really–go watch the paper–don’t print it. Paper or PDFs wouldn’t cut it for this story.

No audio for this video. Just lovely images with some text guidance. I don’t have the computing capacity to try it myself, nor to I have the stacks of images that I used to have. But there are many nice examples of what it can do. And Anne-Lisa’s blog post speaks about what researchers are doing with it.

Quick link:

MorphoGraphX: http://www.mpipz.mpg.de/MorphoGraphX

Reference:

Barbier de Reuille, P., Routier-Kierzkowska, A., Kierzkowski, D., Bassel, G., Schüpbach, T., Tauriello, G., Bajpai, N., Strauss, S., Weber, A., Kiss, A., Burian, A., Hofhuis, H., Sapala, A., Lipowczan, M., Heimlicher, M., Robinson, S., Bayer, E., Basler, K., Koumoutsakos, P., Roeder, A., Aegerter-Wilmsen, T., Nakayama, N., Tsiantis, M., Hay, A., Kwiatkowska, D., Xenarios, I., Kuhlemeier, C., & Smith, R. (2015). MorphoGraphX: A platform for quantifying morphogenesis in 4D eLife, 4 DOI: 10.7554/eLife.05864

google_scholar

Video Tip of the Week: handy way to make citations quickly

google_scholarThis is not a typical tip–where we explore the features and details of bioinformatics tools. But it’s one of those handy little features that may make your life easier. It’s made mine better lately.

I had been using the ScienceSeeker citation generator system for creating citations that would then aggregate to either ScienceSeeker or ResearchBlogging. But ScienceSeeker’s model recently changed. And ResearchBlogging’s support and stability is…well, uneven. But I still would like my posts tagged with appropriate citations and DOIs so they can be found later with other tools and searches.

The helpful folks at ScienceSeeker offered this alternative strategy for quickly grabbing a citation. I’ve already used it a few times now. And I thought other science bloggers might also find this handy. Or anyone wanting a quick formatted cite. And then to just tag it with the DOI is simple. (But boy, I wish they had a version that had DOIs. Maybe I should ask for that.)

I’ve been using Google Scholar a lot lately because the collection is getting better as the paper below notes, and it is becoming a bit more refined with less nonsense items pulled in. mr_happy In the past I was really upset to see detritus like “Mr. Happy’s Health News” in there. But I looked recently and Mr. Happy was gone. There were also some really terrible activist “reports” on biotechnology loaded with unsourced and incorrect information. I’ve seen less of that too, but I haven’t looked specifically for those of late.

But there have been many times I’ve been able to locate a PDF over there that has come in very handy. Yet I had never tried to use that software feature before to create the links. I’m glad it’s available. I just wish there was a version for blog posts with the links done up right. I checked with the Altmetric support pages to see what I need to have in the structure to be sure it gets counted, and here’s the suggested syntax: How do I ensure that my blog posts are picked up by Altmetric?

2. Always include links to the papers that you reference
If you blog a lot about research, the best way to make sure that your posts get picked up by Altmetric is to include a direct link to a scholarly article.

You can include a link to the journal in a variety of different formats, which include but are not limited to:

You can also link to datasets or objects that are hosted on figshare or Dryad Digital Repository, and these mentions will also be picked up by Altmetric. You can link to these objects using a link the DOI URLs, e.g., http://dx.doi.org/10.6084/m9.figshare.1167458.

I know it’s not that hard to add a DOI URL. But it is an extra step I didn’t have to do with the sciblogging citation generators. However, I can’t see an obvious place to offer suggestions or contact the developers. If anyone knows how to reach that team, let me know.

Quick link:

Google Scholar: https://scholar.google.com/

Reference:

Harzing, A. (2013). A longitudinal study of Google Scholar coverage between 2012 and 2013 Scientometrics, 98 (1), 565-575 DOI: 10.1007/s11192-013-0975-y

ZBrowse sample image

Video Tip of the Week: ZBrowse for GWAS viewing and exploration

Maybe you’ve heard of the others. ABrowse. BBrowse. CBrowse. [you get the idea] GBrowse has been widely adopted. JBrowse is picking up steam. Into the orderly arrangement we now throw ZBrowse: a new way to look at genome-wide association study data.

Sharing and chatter about ZBrowse for viewing GWAS was abundant when the paper was published recently.

I could see the appeal immediately. One of the first things I check when exploring new software is the species range. See, I’m agnostic on species, and especially like to find tools that support a wide range of species. ZBrowse does this. Right in their paper they provide a chart comparing their features to other tools, and that tidbit jumped right out at me.

Although we usually like to highlight web-based tools, this one was really different and worth covering even though it requires you to do a bit more lifting on installing it. But they help with that, in their videos and instructions. And ultimately it runs in your browser, once you’ve got the right pieces in place. I was able to set it up and run it (after updating my R and RStudio).

I’m going to skip the installation and data loading videos for now, but you should go over and see them when you are ready to try it out. I’ll just give you a look at the features they show in their introductory video for the browser part. That will give you the best idea of why it’s worth trying it out.

It does require you to have R installed, and RStudio. We’ve talked about both of those before, but if they are new to you, check’em out in these other Video Tips of the Week: Introduction to R Statistical Software, RStudio as an Interface for using R.

It comes loaded with some plant data, but you can use other data you have. It was very easy to look at the Manhattan plot view, and then focus on smaller chosen regions. I really liked how easy it was to see what’s in the neighborhood of a selected item when you turned to the annotation tab. ZBrowse sample image

It might also be worth trying this out as a software delivery strategy–I was just reading about other folks who are offering tools that sit on top of R and RStudio this way (come back tomorrow for another example). People who want to offer you the chance to look over large data sets they are providing are considering this.

Quick links:

ZBrowse at Baxter Laboratory: http://www.baxterlab.org/#!/cqi0

R: http://www.r-project.org/

RStudio: http://www.rstudio.com/

References:

Fertig, E. (2012) Getting Started in R.

Racine J.S. (2011). RStudio: A Platform-Independent IDE for R and Sweave, Journal of Applied Econometrics, 27 (1) 167-172. DOI: http://dx.doi.org/10.1002/jae.1278

Ziegler, Greg R., Ryan H. Hartsock, and Ivan Baxter. “Zbrowse: an interactive GWAS results browser.” PeerJ Computer Science 1 (2015): e3. DOI: 10.7717/peerj-cs.3

genome_connect_logo

Video Tip of the Week: GenomeConnect, the ClinGen piece for patients

GenomeConnect is part of the larger ClinGen effort that I began to discuss last week, but this aspect is specifically a portal for patients who have (or may get) genetic testing results of various types. The ClinGen team will use this interface to capture the testing data–the genotypes, and the health history, or phenotypes, and they want the patients to feel like active partners in the use of the information with doctors and researchers. It also allows patients to connect with others who have similar medical history or diagnoses. The participation details page notes that this is all de-identified, and that participants can choose which types of inquiries to respond to later.

GenomeConnect goalsThis week’s video provides details about the goals of this piece of the project (see screen shot). Adults and children (with guardian consent) can be included [there’s a brochure with that note, PDF]. They also specifically note that you don’t have to have a genetic diagnosis yet. They also show examples of the kind of health survey data they will collect from participants. They also note that they intend to maintain contact with participants, in case they need to clarify or update issues in the records, or potentially involve them in future studies. You can log back in to change health data if your health changes over time. They will include single gene testing results, disease panels, whole exome or genome sequencing, karyotypes, chromosomal microarrays, or GWAS. The input data will be curated by trained Genome Connect professionals, and de-identified data will be shared with other participants if you choose. They say that currently they cannot get submission information directly from health care providers, but that may be a direction they add in the future.

Getting patients involved, and delivering real benefits to them, will be crucial for acceptance and adoption of genomics. And it may advance research by connecting investigators with families who wish to participate in studies. But there are a lot of barriers. This week I was watching the NHGRI’s meeting: Genomic Medicine Meeting VIII: NHGRI’s Genomic Medicine Portfolio (GM8) #GenomicMed8. I was surprised at how much of the discussion was devoted to getting payers to cover the sequencing and testing. This included both insurance systems US-style, but also national payer systems (there were representatives from Canada, the EU, etc there too). Consenting (and re-consenting later for future research) was also discussed. Counseling access can be a problem. There was a whole segment on the second day on these issues, but they kept coming up interspersed in all the other discussions too. GenomeConnect was one of the named projects in an overview of patient-facing tools. Others included Genetics Home Reference, Cancer Genetics PDQ, Genetic Alliance, MEDLINEPlus, NORD / GARD, and OrphaNet. Other tools that were aimed more at explanation of results included LabTestsOnline, MyResults, YourGenome, and My46. All of these have different scope and features, of course, and some are limited to certain research or treatment facilities. But more efforts continue to be developed to get people involved in research and effective use of genomic information for health.

Quick links:

GenomeConnect overview and more details at the ClinGen site: http://clinicalgenome.org/genomeconnect/for-participants/what-is-genomeconnect/

GenomeConnect site for patients: GenomeConnect.org

If you want to provide information to patients about this resource, there are details and flyers and such here: http://clinicalgenome.org/genomeconnect/for-providers/

References:
Rehm, H., Berg, J., Brooks, L., Bustamante, C., Evans, J., Landrum, M., Ledbetter, D., Maglott, D., Martin, C., Nussbaum, R., Plon, S., Ramos, E., Sherry, S., & Watson, M. (2015). ClinGen — The Clinical Genome Resource New England Journal of Medicine DOI: 10.1056/NEJMsr1406261

ClinGen

Video Tip of the Week: ClinGen, The Clinical Genome Resource

The sequence data tsunami begins to crash into the shore, at the feet of clinicians and patients who want answers and treatment directions. But sometimes the tsunami is washing in debris. As the amount of sequence and variation information grows, some of it comes without clear evaluations of the impacts. Some of it comes with conflicting information. And some of it comes in wrong.

Attempting to wrangle the information into useful understanding and treatments with standardized descriptions, the team building the ClinGen resources published a paper last week that details their efforts. The paper describes their history and goals, and how they are moving to get to a point where they have useful information for and from patients, their doctors, testing labs, and researchers. Because of the different needs of different groups, there are several moving parts to the overall ClinGen collection.

In addition to the paper–and several related articles in this NEJM special report–there are videos on their site that tackle different aspects of the ClinGen projects. I’m going to highlight one of them here as the Tip of the Week, but you should also check out the others that are available on their webinars page or their YouTube channel. This video shows the Dosage Sensitivity Map features.

This video provides some of the history and framework for the ClinGen efforts, and then also introduces one of the tools that they have made available, a dosage sensitivity map. This piece focuses on “evidence based reviews of dosage sensitivity”, and they indicate haploinsufficiency losses of regions, and triplosensitivity duplications of regions. ClinGen dosage scoresThey describe a scoring system they use to rank structural variations (CNVs, SVs), and their curation of the evidence to support or to refute dosage sensitivity. They also note that their process is conservative, and you should keep that in mind as you consider the their team’s review of the evidence. But they are definitely open and interested in feedback and they hope you will contact them if you have a different understanding from their posted evaluations.

To follow along with the video, use this site to explore the features of this part of the ClinGen tool set: http://www.ncbi.nlm.nih.gov/projects/dbvar/clingen/. But you can also just click their example genes–for instance, the ZEB2 link shows you a typical page with the score information, links to other resources, and a genome viewer right on the page.  But you can also choose to look at external browsers at NCBI, Ensembl, or UCSC. I clicked the UCSC Genome Browser one to see how it displayed, and they automatically present to you tracks with the relevant ClinGen data loaded.

In other tips I’ll talk about other pieces of the infrastructure that they are building or coordinating with. Some we’ve talked about before–you can see a previous tip that included the ClinVar resource at NCBI that is foundational to the ClinGen suite and is discussed in their paper as well. They also note the importance of the data from OMIM, and how their mutual efforts are providing important feedback loops to be alerted to needed updates.  ClinGen also employs the Human Phenotype Ontology that keeps coming up at OpenHelix lately. Another important piece to this is the standards for naming variants that were recently described by the American College of Medical Genetics and Genomics (paper linked below).

ClinGen, and the various component tools within, are worth looking at, and contributing to, as we try to move more and better information to the clinic for patients and doctors to use effectively. Steven Salzberg has a take on the value of ClinGen here: 17% Of Our Genetic Knowledge Is Wrong.

It’s also very possible that some really important things will happen in the database–new submissions, changes to the status of a variant–that will occur before any papers come out about it. Or it is even possible that a paper never will come out about it. Spend some time learning about the features; I think it will be worth the time.

Quick links:

ClinGen overall project: http://clinicalgenome.org/

ClinVar: http://www.ncbi.nlm.nih.gov/clinvar/

ClinGen Dosage Sensitivity Map: http://www.ncbi.nlm.nih.gov/projects/dbvar/clingen/

References:

Rehm, H., Berg, J., Brooks, L., Bustamante, C., Evans, J., Landrum, M., Ledbetter, D., Maglott, D., Martin, C., Nussbaum, R., Plon, S., Ramos, E., Sherry, S., & Watson, M. (2015). ClinGen — The Clinical Genome Resource New England Journal of Medicine DOI: 10.1056/NEJMsr1406261

Richards, S., Aziz, N., Bale, S., Bick, D., Das, S., Gastier-Foster, J., Grody, W., Hegde, M., Lyon, E., Spector, E., Voelkerding, K., & Rehm, H. (2015). Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology Genetics in Medicine, 17 (5), 405-423 DOI: 10.1038/gim.2015.30

Video Tip of the Week: PANDA (Pathway AND Annotation) Explorer for lists of genes

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. panda_genelist 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.

I began by looking into the paper recently out in PeerJ, linked below. It sets out their goal of helping you to link all the -omics types of information you may be generating or collecting. Here’s a quick description of their tool from their abstract:

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.

Quick link:

PANDA: http://bioinformaticstools.mayo.edu/research/panda-viewer

Reference:

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

TreeViewer

Video Tip of the Week: NCBI Tree Viewer

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.

Another neat thing: you can embed your tree on a web page if you like. There are instructions over there to help you do that.

Quick links:

TreeViewer: http://www.ncbi.nlm.nih.gov/projects/treeview/

NCBI-NLM video channel: https://www.youtube.com/channel/UCvJHVo5xGSKejBbBj0A5AyQ

References:
NCBI Resource Coordinators (2014). Database resources of the National Center for Biotechnology Information Nucleic Acids Research, 43 (D1) DOI: 10.1093/nar/gku1130

The PhenogramViz team illustrates how they analyze and visualize gene-phenotype relationships

Video Tip of the Week: PhenogramViz for evaluating phenotypes and CNVs

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

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.

Check out PhenogramViz as a bridge between genomic segments and possible phenotypes. You can try it yourself with sample files they have available on their landing page.

Quick links:

PhenogramViz: http://compbio.charite.de/contao/index.php/phenoviz.html

Cytoscape: http://cytoscape.org/

References:

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

Video Tip of the Week: Human Phenotype Ontology, HPO

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.

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.

Quick links:

Human Phenotype Ontology: http://www.human-phenotype-ontology.org/

Phenomizer: http://compbio.charite.de/phenomizer/

PhenIX: http://compbio.charite.de/PhenIX/

PhenExplorer: http://compbio.charite.de/phenexplorer/

PhenoTips: https://phenotips.org/

Monarch Initiative: http://monarchinitiative.org/

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

Be sure to pick the right one on the iTunes store.

Video Tip of the Week: Proband for pedigrees with your iPad [update: now free!]

Update: since I started this post last month, they have changed the access–it’s now FREE! Yay! Go try it out.

For this week’s Tip of the Week we revisit pedigree tools. We see a lot of interest in pedigree tools from researchers and from the public, in fact. Families have been encouraged to collect their own family histories by the Surgeon General’s office in the US. We’ve been big fans of the web-based Madeline 2.0 for a long time, and we have a training suite on that. We’ve also talked about plant pedigrees–so we are agnostic on species.

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:

This tool has been developed by the DBHI–Department of Biomedical and Health Informatics at the Children’s Hospital of Philadelphia. So they have boots on the ground, and are dealing with real patient situations and have this need. For this week’s tip, I’ll let them show you the features of their tool (there’s no audio with this video).

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.

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.

If you are new to pedigree drawing, or need a reminder of the basics, there’s a terrific intro video by this Genomics Education group. Direct link to the larger Vimeo verison.

Quick link:

Proband Pedigree App for iPad: http://probandapp.com/

Find them on twitter: https://twitter.com/ProbandApp

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.