Birthday Wishes for Bertrand

Posted by on Dec 9, 2016 in citizen science, community intelligence, crowdsourcing, mark2cure | 0 comments

The Might family has been an incredibly moving proponent of precision medicine, citizen science, and rare disease. Thank you for what you’ve done for Mark2Cure and the fields of health and science. We wish Bertrand and his family health, happiness, and prosperity in the upcoming year.

In case you haven’t noticed, we’ve carved our blog out from our lab’s main blog and will serve it up directly on our site at If would like to write a post to share with the Mark2Cure community, let us know! If you have recommendations, or if there are things you’d like to see discussed on our blog, contact us!

This week, Mark2Cure hosted the @IamCitSci twitter account to share what we learned to the rest of the citizen science community. For details, see our storify.

Lastly, thank you for your patience and a HUGE THANKS to those of you who have taken the time to report bugs in the system via email, posting on the talk pages etc. We really appreciate it very much, and are working to resolve them as quickly as possible.

Happy Thanksgivings Mark2Curators!

Posted by on Nov 23, 2016 in citizen science, community intelligence, crowdsourcing, games, mark2cure | 0 comments

It’s still a little early, but we wanted to wish you well before the rest of your week got hectic with all the Turkey-day goodness. Since the project started, we’re grateful to have had the chance to learn and be inspired by you. For those of you who have taken the time to write to us, we’re awed by how driven, analytical, humorous, creative, and helpful you are–and we wanted some means of sharing that with you.

If only there was some way to send you a gift for the holiday season…
…given our experience with citizen science, maybe we can crowd-source it?…

And that’s how we came up with the Mark2Cure Secret Santa! Keep an eye out for our newsletter in your email inbox for more details on how to sign-up/participate.

New Mission Available!
When an NGLY1 researcher heard about what you all were doing, she wondered if the community had found anything on her gene of interest. Help search for clues on this gene in our new mission.

Your comments matter!
With complex programs, something is bound to get broken when code is updated, and it would be impossible to find and squash all the bugs, if users didn’t report them. If Mark2Cure gets wonky for you, we’d LOVE to know! Post to the talk pages, or send us an email. We can’t improve without you!

Wikiconference North America

Posted by on Oct 8, 2016 in collective wisdom, community intelligence, conference, Gene Wiki, Wikidata, wikipedia | 0 comments

Wikipedia is one of the most widely used and freely accessible knowledgebases. As one of the largest, crowdsourced resources, many researchers are engaged in making the Wikimedia platform more useful in scientific research—including researchers from our lab here at The Scripps Research Institute. If you’re interested in how Wikipedia and Wikidata are being used in science, you should check out the Wikiconference, happening today through Monday. Registration is free (or $10-$25 with food included). More details here.

Three members of the Su Lab will be presenting their work on pushing the boundaries of how Wikidata can be used in academic research.

First up is Tim. Tim will present his work on developing a microbial specific semantic data model in Wikidata modeling bacterial species, genes, proteins, diseases they cause, and drugs that treat them. He’s part of the 1:00-3:00pm session today in the Clark Room at the San Diego Central Library. Learn more.
Tim’s presentation slides can be found on figshare.
Recording of Tim’s presentation on youtube.

Both Greg and Sebastian are scheduled to present during the 3:30-5:00pm session today, at roughly the same time too! Fortunately, the conference organizers have plans to record the talks, unfortunately the quality of the video recordings tend to vary greatly.

Greg will present on a Cytoscape browser he developed using the Wikidata SPARQL endpoint. This allows him to combine a powerful way for pulling more complex information from Wikidata with a useful method of visualization. Greg will be presenting in the Shiley room on the 9th floor of the library. Learn more.
An early version of his slides are available on google drive.
Recording of Greg’s presentation on youtube (pending)

At the same time, Sebastian will be in the Clark room to present his work on adding Drug and chemical compound items in Wikidata as a data source for Wikipedia infoboxes. Sebastian played an important role in adding gene and protein items into Wikidata which allowed Gene Wiki infoboxes to pull from Wikidata. Learn more.
An early version of his slides are available on slideshare.
Recording of Sebastian’s presentation on youtube.
2016.10.09 update – links to the slides for their presentations have been added. Video of their talks is pending.
2016.10.12 update – link to recording of Tim’s presentation added.
2016.10.19 update – link to recording of Sebastian’s presentation added.

Mark2Cure Pro-tip #2- Verifying your hunch

Posted by on Oct 7, 2016 in citizen science, crowdsourcing, mark2cure | 0 comments

In Mark2Cure, it’s almost guaranteed that you’ll encounter words you’ve never seen before. Often times, you can use the context to infer whether or not that term should be marked. Other times, the abstract may be just too jargon-laden or poorly written to make that determination. How do you determine whether or not to mark a term under this situation?
Aside from tips you may have picked up from other Mark2Curators via the talk pages (see pro-tip #1), here’s how a few of our Mark2Curators have approached this problem.

#1 – Revisit the rules: The rules for each concept/entity recognition task are linked via the colored boxes at the bottom of the task page.

Clicking on any of the colored boxes (‘Disease Concept’, ‘Genes Concept’, or ‘Treatments Concept’) will take you to the instructions page which details what you should mark

Clicking on any of the colored boxes (‘Disease Concept’, ‘Genes Concept’, or ‘Treatments Concept’) will take you to the instructions page which details what you should mark

Because many things in biomedical research are related it can be tempting to mark terms that are related to the concepts even if they’re not mentioned in the instructions. For example, specific gene variants may appear in the text but gene variants are not listed in the instructions. As tempting as it is, you should refrain from marking the gene variants and try to stay as close to the rules as possible.

#2 – Use the search button. The last term of phrase you highlight is captured and becomes the default terms for the search button at the bottom of the task page. Once you’ve highlighted something, click on the blue ‘search’ button on the bottom to search the terms you just highlighted. You are also welcome to look up terms for the Concept/entity recognition task—in fact, we encourage you to do so! We’re big fans of learning, and you will often learn from whatever you bother to look up! The instructions page also contains links to concept-specific databases which may be helpful, but Wikipedia is also a great resource to search.

#3 – Reach out. Talk pages are a great place to have doc-specific questions answered, and posting your questions to the talk pages helps the entire community so we can learn together! You can also contact us by posting on twitter, facebook, Wikia, or via email. Please note, facebook and Wikia are currently the slowest way to reach us, though we’ll try to be better with them. Although we may not have definitive answers for all your questions (by nature, language has ambiguity, and sometimes that makes it possible for multiple choices to be correct), we’ll try to at least provide guidance wherever you’re stuck.

Mark2Cure Pro-tip #1

Posted by on Sep 23, 2016 in citizen science, collective intelligence, crowdsourcing, mark2cure | 0 comments

As a citizen science project, Mark2Cure has been very fortunate to recruit a number of volunpeers who have grown to become experts at the concept recognition task and provide a lot of useful feedback, comments, and suggestions. While we encourage you to explore and use the site in the ways that work best for you, we would like to share some of the great tips that users have shared with us. Without further ado, here is our first user pro-tip.

Pro-tip #1 – Using the talk pages to learn together
Upon the completion of a doc, you will be presented with a feedback screen which shows what your partner has marked, and the option to visit the talk page for that doc.

The talk page button is right next to the 'Next Doc' button on the feedback screen.

The talk page button is right next to the ‘Next Doc’ button on the feedback screen.

If there was anything in the document that you felt was confusing or unclear (in terms of whether or not it should be marked), someone else probably felt the same way you did. If you click on ‘Yes, Let’s Talk’, you may find that your question has been asked and learn from the answer; or you can post your question there so that others will learn from your question.

If you’re worried that your question has been asked elsewhere, DON’T worry about that! Each talk page is viewable only by users who have contributed to that doc. This means that duplicate questions are welcome as they increase the likelihood new users will encounter them.
On the talk page itself, there are several ways to check your work.

First is in the comments that users submitted:

Comments and questions submitted by users may have very useful info!

Comments and questions submitted by users may have very useful info!

As seen here, this user has offered some very useful background information about some terms in the text.

Secondly, you can see how other users marked this doc and learn off them by scrolling over the numbers at the top of the talk page.

Different users may mark different terms in the text. Pay special attention to terms marked the same way by multiple users.

Different users may mark different terms in the text. Pay special attention to terms marked the same way by multiple users.

By looking at how other users marked the doc, you can get an idea on what annotations the community agrees should be marked.

If you dislike scrolling through the user annotations, you can also get a feel for what the community marked by looking at the frequency table below. The table exhibits the top 20-25 most frequently marked terms for each concept type.

The frequency table gives you a quick way to see the terms that were marked by people who have also completed this doc.

The frequency table gives you a quick way to see the terms that were marked by people who have also completed this doc.

Lastly, if you’re wondering why the majority of users marked something differently than you did, or if you just have doubts about what you’re doing, you are welcome to post about it! We will always try to respond in a timely fashion, but an amazing Mark2Curator may just beat us to it!

A big thanks to everyone who has posted to the talk pages for helping to improve the learning opportunities in Mark2Cure

A big thanks to everyone who has posted to the talk pages for helping to improve the learning opportunities in Mark2Cure

If you’d like to chime in on a discussion about a document you’ve contributed to before, click on the ‘Talk’ link at the top right corner of your dashboard header. This will show you the docs that you’ve done which are under discussion. If a doc you’ve done doesn’t appear, it’s likely because no one has discussed the doc. If another users begins a discussion on a doc that you’ve completed, it will appear in your list.

Special thanks to ckrypton for responding faster than I do on some of these, and for giving us the idea of this pro-tip!

BioGPS Spotlight on WormBase and WormMine

Posted by on Sep 2, 2016 in BioGPS, plugin, spotlight | 0 comments

If worms are your model organism of choice, you’re probably already familiar with WormBase. WormBase was founded in 2000 and is a multi-institutional consortium led by Paul Sternberg of the California Institute of Technology (CalTech), Paul Kersey of the European Bioinformatics Institute (EBI), Matt Berriman of the Wellcome Trust Sanger Institute, and Lincoln Stein of the Ontario Institute for Cancer Research (OICR). WormBase hosts a number of valuable tools for researchers, and is responsible for the development of WormMine, an Intermine-based tool that replaced WormMart. One of WormBase’s database curators, Ranjana Kishore, was kind enough to answer our questions for this series.

  1. In one tweet or less, introduce us to WormBase:
    WormBase is the central repository for data related to the genetics, genomics and biology of C. elegans and related nematodes.

  3. How did WormBase get its start? What was WormMart? At what point in WormBase’s history was InterMine integrated for the creation of WormMine?
    In the beginning was AceDB (A C. elegans database) a database developed by Richard Durbin and Jean Thierry-Mieg in 1989, for hosting genomic data, which included a graphical user interface with specific displays and tools for querying data. In early 2000, Paul Sternberg and colleagues at Caltech who worked with C. elegans as a model system realized that there was no online repository of information for this model organism that was increasingly being used, as a result of a fast-growing community of researchers. Fly researchers had FlyBase, but there was no online database for C. elegans. The C. elegans model system was drawing more and more researchers because of the several advantages it has–rapid generation time, simple nervous system, invariant cell lineage, transparent body, etc. Data was rapidly accumulating but there was no place to collect, organize or disseminate it for the good of the community. Thus began WormBase!

    WormMart was the WormBase implementation of BioMart for querying data in batch mode. WormBase switched to WormMine in 2013 which is based on InterMine, an open source data warehouse built specifically for the integration and analysis of complex biological data.

  5. Who is WormBase’s target audience?
    The C. elegans research community, the wider nematode research community, biomedical researchers and anyone interested in nematode model systems including college and high school teachers and students!

  7. WormBase seems to serve a thriving community of researchers. What is your greatest success story so far?
    WormBase serves a thriving and dynamic community of nematode researchers who use the C. elegans and other nematode systems to study diverse topics such as systems biology, signaling pathways, cell death, human diseases, drug efficacy and potential new drug screening. Research in C. elegans has won two Nobel prizes, in 2002 and 2006. WormBase itself has expanded to include diverse types of data including a portal, WormBase Parasite, which is dedicated to supporting helminth research. WormBase supports at least nine ‘core’ nematode species, hosting their genomes and providing tools such as genome browsers, including parasitic species that impact human health such as Onchocerca volvulus (causative agent of river blindness) and Strongyloides ratti (the rat laboratory analog of the causative agent of threadworm infection).

    Our user community not only includes Nobel prize winning researchers but also college and high school teachers who use C. elegans in their labs to teach basic biology and who use WormBase as an example of how to use a biological database. Our help-desk routinely fields questions from teachers and students who would like to obtain some worms and use WormBase to design simple experiments!

  9. WormBase actually encouraged users to sign a petition in support of the Model Organism Databases. What role does WormBase play in the Alliance of Genome Resources?
    WormBase is a member of the Alliance of Genome Resources (AGR) and is playing an active role in the Alliance’s plan to build an integrated data resource comprising of data from several model organisms, including yeast, worm, fly, zebrafish, mouse and rat. WormBase will help in the integration of data from these model organism databases in order to increase accessibility for the biomedical researcher to model organism data, from a single integrated resource. AGR will also serve as a portal to the individual model organism databases.

  11. What improvements are coming in the future for WormBase? For WormMine?
    WormBase is increasing focus on curating and improving displays for data relevant to human health which includes curating human gene orthologs in the worm that serve as genetic models for disease, and associating mutant alleles in C. elegans with orthologous genomic variants in human. In addition, WormBase Parasite will focus on features for the identification of putative targets for anti-helminthic drugs.

    WormBase is currently in the process of moving to a more flexible and dynamic database architecture. In the future we plan to do more frequent data updates (currently WormBase has a two month release cycle) and eventually updates in real time, so that Users can access new data as soon as they are curated. We plan on even greater involvement of the community through contributions of data–not just large-scale data, but small scale data from authors of individual papers, and edits to existing data in WormBase. We plan to make these community curated data available on the website.

    For WormMine: In the future we plan to include several more data sets in WormMine, that are currently in WormBase.

  13. Who is the team behind WormBase and WormMine?
    WormBase is an international consortium led by Paul Sternberg, Lincoln Stein, Paul Kersey and Matthew Berriman. It consists of three groups, each located in–the European Bioinformatics Institute (EBI) at Hinxton, UK, the Ontario Institute for Cancer Research (OICR) in Toronto, Canada, and the California Institute of Technology (Caltech) in Pasadena, USA. (See the WormBase 2016 reference below for the current list of people).

    The development of WormMine takes place at the Ontario Institute for Cancer Research under Lincoln Stein and Todd Harris. Currently, Paulo Nuin is the primary developer of WormMine.

Thanks to Ranjana, for guiding us through this extremely useful and FREE tool. Be sure to check out their plugin in the plugin library. If you use WormBase in your research, be sure to cite their recent publication:

WormBase 2016: expanding to enable helminth genomic research.
Kevin L. Howe, Bruce J. Bolt, Scott Cain, Juancarlos Chan, Wen J. Chen, Paul Davis, James Done, Thomas Down, Sibyl Gao, Christian Grove, Todd W. Harris, Ranjana Kishore, Raymond Lee, Jane Lomax, Yuling Li, Hans-Michael Muller, Cecilia Nakamura, Paulo Nuin, Michael Paulini, Daniela Raciti, Gary Schindelman, Eleanor Stanley, Mary Ann Tuli, Kimberly Van Auken, Daniel Wang, Xiaodong Wang, Gary Williams, Adam Wright, Karen Yook, Matthew Berriman, Paul Kersey, Tim Schedl, Lincoln Stein, Paul W. Sternberg (2016). Nucleic Acids Res, 44, D774-80. PMID: 26578572. PMCID: PMC4702863. DOI: 10.1093/nar/gkv1217

New mission available in Mark2Cure

Posted by on Aug 26, 2016 in citizen science, crowdsourcing, mark2cure | 0 comments

Just a quick update—a new mission has been launched.  This one is centered around galactosemia and oxidative stress.  Check it out today!

A huge thanks to everyone who contributed to finishing the mission on stress response and muscle weakness. You can investigate the knowledge networks of any completed mission by clicking on the ‘toggle network’ link on the mission page.

Check out the knowledge network you’ve built by contributing to the stress response and muscle weakness mission below!

Stress response and muscle weakness network

In case you didn’t know, there are talk pages available for each doc. The talk page for a doc becomes available to you after you complete that doc.  Please don’t hesitate to add your questions to the talk pages or to share your opinion on a talk page discussion.  This way, we all learn together!

Functional gene constraint scores from ExAC now available from

Posted by on Aug 25, 2016 in BioGPS, data release, exac, | 0 comments

With the publication of a new paper in Nature, the Exome Aggregation Consortium (ExAC) has received some much deserved attention on their open data/open access practice. ExAC variant annotation data has been available for awhile through; however, their functional gene constraint data doesn't fit well into model. Speaking of ExAC annotations in, check out the latest ExAC changes in this post.

ExAC also released a computed "functional gene constraint" metrics for each transcript containing variants from ExAC dataset. Since this is at transcript/gene level, we have now imported this data into, instead of (which is designed as variant-specific). You can now access ExAC's latest release of functional gene constraint data from our v3 API of (under the field of exac). You can find out the definitions of all sub-fields under exac, or learn more about the functional gene constraint data from their FAQ and README files.

Ready to try pulling this new data? Try the following query examples:

If you use our python client, you'll be pleased to know that the most recent update of our client also enables you to run similar queries.

Python example:

In [1]: import mygene

In [2]: mg = mygene.MyGeneinfo()

In [3]: mg.getgene(1017, fields='exac')  
In [4]: mg.getgene('ENSG00000153230', fields='exac')  
In [5]: mg.query('exac.transcript:ENST00000266970', fields='exac')  

Just want to mention again this new exac field is only available from our v3 API, not available from the still-live v2 API. Just another reason to switch to v3 API now :-).


BioGPS Spotlight on ZFIN and ZebraFishMine

Posted by on Aug 19, 2016 in BioGPS, plugin, spotlight | 0 comments

The Zebrafish Model Organism Database (ZFIN) has served the scientific community for over twenty years. As with the Rat Genome Database, which was previously featured in our Spotlight series, ZFIN also manages an InterMine-based resource called ZebrafishMine. The ZFIN team is based at University of Oregon, and their data curation manager, Doug Howe, has kindly answered our questions about this important resource.

  1. In one tweet or less, introduce us to ZFIN:
    ZFIN is the community resource for expertly curated genetic and genomic data involving the zebrafish (Danio rerio) as a model organism.

  3. Who is your target audience? Has your audience changed over time, and how has ZFIN evolved to meet the needs of your audience?
    In the beginning, ZFIN was lovingly tagged as “zphone,” because of its role as a community contact for zebrafish researchers, students and publishers. Curators took an early role in establishing nomenclature guidelines and extracting gene models, loading mapping data and adding mutation details from papers even as early as 1996. In the early 2000s, ZFIN began cross linking with several other genomic data sets including GenBank, Vega, UniProt, Ensembl and many, many more resources.

    In 2003, ZFIN began representing additional biological details including orthology to human, mouse and fly genes, more complicated genotypes including double and triple mutants, transgenics, morpholinos (and in 2013, CRISPRs and TALENs), gene ontology annotations, expression, phenotype and experimental conditions details. During the next decade, ZFIN curators also developed (and continue to develop) the Zebrafish Anatomical Ontology and several other biological term hierarchies that help define the language with which zebrafish data can be annotated. This ontological development and subsequent curation has allowed many other research scientists to develop more complex data retrieval pipelines, some of which have even begun identifying human diseases with aggregated, model organism phenotypes curated from papers and data loads using ontologies. It has also contributed to the unification of data from disparate sources in ZFIN.

    Most recently, ZFIN has started incorporating human disease annotations both directly from papers and via zebrafish models of disease. Curators are excitedly discussing cross linking fish and their phenotypes with annotations to human diseases in order to help clinicians understand disease phenotypes in humans.

    Throughout ZFIN’s history, researchers, students, data aggregators, bioinformaticians and other online genetic resources and databases, have been central to the development of this resource. ZFIN was founded with a partnership of biological scientists and user interface developers. Maintaining this important cross-disciplinary and connected relationship with user-centered design as its core has helped ZFIN remain connected to the research community even in its most recent developments. With the advent of the Alliance of Genome Resources, ZFIN hopes to be even more useful to the basic science research communities of zebrafish and other model organisms, and increasingly to communities including clinicians.

  5. It looks like ZFIN originates from a 1994 Cold Spring Harbor meeting on zebrafish. Can you elaborate on this story? When did ZFIN begin collaborating with ZIRC and at what point in ZFIN’s history was InterMine integrated for the creation of ZebrafishMine?
    ZFIN was started by Monte Westerfield after the 1994 Cold Spring Harbor meeting (the first open international zebrafish meeting). Initially it was funded by the NSF. In 1998 Monte received an NIH grant for ZIRC that included a database aim that supported ZFIN. This started the collaboration between the two resources. Independent funding for ZFIN was first secured in 2002.

    ZebrafishMine was a collaboration between staff at ZFIN and the Intermine project starting in 2009. By 2009, ZFIN was nearly 15 years old. We created our intermine instance to provide functionality for advanced ZFIN users and in response to wish lists we’d heard from researchers in our community. ZebrafishMine was up and running in 2013.

    In Zebrafishmine a user can search using lists, like a gene list, and create personalized queries and templates to run at their convenience. This kind of personalization and querying of data at ZFIN can be essential to research that requires large data sets. ZFIN provides generic search interfaces that we believe are helpful to the majority of users. Alternatively, ZebrafishMine provides advanced searching and downloading (as well as programmatic access) to this same data for users who have more specific use cases in mind, or the desire to deal with data in bulk. In addition, the InterMine system promotes easy access to many model organism species including: fly, rat, mouse and yeast. Zebrafish users can see orthology in mouse and human, directly linked in ZebrafishMine.

    Finally, the user interface of ZebrafishMine is the same as the user interface for many other organism mines. Once a user learns to use ZebrafishMine, it is quite easy to use all the other mines as well. This reduces barriers to inter-species data retrieval and helps satisfy one of ZFIN’s main goals: to aid researchers in determining genetic and environmental causes of human disease.

  7. Why is ZFIN unique and special? How does it differ from ZIRC?
    ZFIN is the only freely available resource focused on integrating zebrafish research data in a searchable, downloadable, and easily accessible framework that allows the community to work together to turn data into knowledge. ZFIN is a curated resource: we work with researchers to quality check data, we accept direct data submissions, and we cross link data with other databases. ZFIN also provides the official nomenclature support for zebrafish genes and mutants, and hosts a community wiki for exchange and discussion of protocols and antibodies. We maintain the definitive reference data sets of zebrafish research information and link this information extensively to corresponding data in other model organism and human databases.

    The Zebrafish International Resource Center (ZIRC;, also located at the University of Oregon, is the primary stock center in the United States distributing fish strains and providing veterinary and pathology support services. ZFIN and ZIRC are independently funded by the NIH but work in close collaboration to provide fish strains and associated data in a coordinated fashion to the research community. Two other resource centers are also available: the Chinese Zebrafish Resource Center (CZRC) and the European Zebrafish Resource Center (EZRC). ZFIN provides links to all three of these stock centers when they have fish strains available for purchase.

  9. What level of traffic does ZFIN typically see?
    ZFIN typically has two-thousand users per day who spend an average of four minutes on various pages. Our most heavily used tools are “expression search” and our summary gene pages.

  11. Looking at the committees involved in ZFIN, it’s clear that ZFIN is an important hub for zebrafish-related knowledge. ZFIN’s job board activity for zebrafish-related positions is another testament to its value as a community resource. What is one of ZFIN’s greatest success story so far?
    Basic research in model organisms is done to provide insights into mechanisms of human disease and ultimately to illuminate paths to disease remediation or cure. The role ZFIN plays in this process is to gather zebrafish research data in a quality controlled and readily sharable format. ZFIN serves the research and clinical communities both as a data processor and repository and also, critically, as a source for the high quality expertly curated data used to facilitate biomedical discovery based on prior work. Researchers and clinicians are now using data obtained from ZFIN (and other model organism databases) combined with human clinical data as inputs to new algorithms designed to discover disease causing genes and define novel treatments. The critical role we play in this cyclical process of discovery is one of our great successes.

  13. Good resources have good documentation on how to use them; great resources have documentation on how to integrate and improve them. When did ZFIN decide to create a comprehensive guide for users to contribute data, and how long did it take to realize this guide?
    Direct submission and integration of data has been a core function of ZFIN since it’s inception. As data, and hence the ZFIN database, have become more complex, it has become increasingly challenging for researchers to gather data and provide it in a readily sharable format. Data prepared without enough forethought on how it will be shared can lead to significant work on data transformation before data can be loaded into the ZFIN database. In 2015 we had the opportunity to contribute the chapter in Methods in Cell Biology, so we took that opportunity to provide the comprehensive data submission guide.

  15. What is in store for ZFIN? Does the uncertainty in Model Organism Database funding (MODs) affect ZFIN’s planned developments, and has there been a response to the GSA’s letter of support for MODs (in which ZFIN was mentioned)?
    The GSA letter of support garnered over 10,000 signatures in a very short time prior to the TAGC meeting in Orlando. The letter, presented to NIH Director Francis Collins during that meeting, brought a clear positive message to the NIH leadership regarding how significant the model organism databases and the Gene Ontology Consortium are in facilitating NIH funded research and furthering the mission of the NIH itself. We continue to put the needs of our research community first and are focused on maintaining our level of service while the funding models and sources shift. One significant positive outcome of ongoing discussions about how to fund database resources in general has been the formation of the Alliance of Genome Resources, a consortium of six model organism databases and the Gene Ontology Consortium, whose aim is providing better cross-organism data integration and standardization. This is the next step in the evolution of these resources, as we prepare to tackle together some of the most challenging data integration issues that hamper broad use of model organism data in furthering our understanding of human disease. These are very exciting times, and I think our best days in support of the research community are ahead of us!

  17. Who is the team behind ZFIN and ZebrafishMine?
    Though the following list represents ZFIN staff in 2016, prior members of our team contributed significantly to the success of this resource.

    • Principal Investigator – Monte Westerfield
    • Data Curation Manager – Doug Howe
    • Technical and Project Manager – Anne Eagle
    • Technical Team:
      • Patrick Kalita
      • Prita Mani
      • Ryan Martin
      • Christian Pich
      • Xiang Shao
      • Kevin Shaper
      • Sierra Taylor-Moxon
    • Curation Team
      • Yvonne Bradford
      • David Fashena
      • Ken Frazer
      • Holly Paddock
      • Sridhar Ramachandran
      • Leyla Ruzicka
      • Amy Singer
      • Ceri Van Slyke
      • Sabrina Toro
    • Administrative Assistant – Jon Knight
    • Literature Acquisition Assistant – Ruben Lancaster

    ZebrafishMine is a collaboration between ZFIN at the University of Oregon and the InterMine project at the Cambridge Systems Biology Centre.

Thanks to Doug Howe and the rest of the ZFIN team for guiding us through this extremely useful and FREE tool. Be sure to check out the ZFIN-related plugins here and here in the plugin library. If you use ZFIN in your research, be sure to cite their publication:

ZFIN, the Zebrafish Model Organism Database: increased support for mutants and transgenics. Howe DG, Bradford YM, Conlin T, Eagle AE, Fashena D, Frazer K, Knight J, Mani P, Martin R, Moxon SA, Paddock H, Pich C, Ramachandran S, Ruef BJ, Ruzicka L, Schaper K, Shao X, Singer A, Sprunger B, Van Slyke CE, Westerfield M. (2013). Nucleic Acids Res. Jan;41(Database issue):D854-60. PMID: 23074187. DOI: 10.1093/nar/gks938

If you would like to contribute data to ZFIN, please see their recent guide here:

A scientist’s guide for submitting data to ZFIN. Howe DG, Bradford YM, Eagle A, Fashena D, Frazer K, Kalita P, Mani P, Martin R, Moxon ST, Paddock H, Pich C, Ramachandran S, Ruzicka L, Schaper K, Shao X, Singer A, Toro S, Van Slyke C, Westerfield M. Methods Cell Biol. 2016;135:451-81. Doi: 10.1016/bs.mcb.2016.04.010. Epub 2016 May 12. PubMed PMID: 27443940.

New release: Python client updated to v3.0.0

Posted by on Aug 18, 2016 in BioGPS, client, mygene,,, newrelease, python | 0 comments

A few weeks ago, we released v3 API, which brings changes to exon field data structure and added accession version numbers to refseq and accession fields, along with some other back-compatible changes.

Our Python client mygene module is now updated to use our v3 API as the default. We increased its version from v2.3.0 to v3.0.0, just to match the underlying API version.

The mygene module itself has no incompatible changes (see detailed CHANGES.txt if you like). But you might want to check out the changes in our underlying v3 API from this detailed migration guide, especially if you are using refseq, accession and exon fields in your application.We encourage all of our users to upgrade to this new version. The upgrade is as easy as one line of command:

pip install mygene -U  

To verify you have the latest version installed:

In [1]: import mygene

In [2]: mygene.__version__  
Out[2]: '3.0.0'  

And you can see it's using our v3 API as the default now:

In [3]: mg = mygene.MyGeneInfo()  
In [4]: mg.url  
Out[4]: ''  

As a side note, in case you still want to use our v2 URL for a while, you can still do that by setting mg.url=''. Our v2 API will still be live till most of our users are migrated to v3 API, but the annotation data from v2 API will not be updated any more.

As always, you can find more info about our Python client here: