When I was doing my Ph.D. in the ancient days of the Sanger Method sequencing and reading in the results with one hand on the keyboard and reading the GATCs on the read (and going to the lab in the snow uphill both ways), my purpose for slogging through all that was to eventually get a phylogeny of the sequences of the retrotransposable elements I was studying. Why did I want that phylogeny? Because I was comparing the phylogeny of the retroelements to that of the species in which they reside. We were attempting to determine if these retroelements were stable within the taxa lineage (they are) or there was promiscuous horizontal transfer occurring. We did those comparisons, but it would have been nice to have a ‘cophylogeny reconstruction’ program :D. There are often times similar comparisons of phylogenies are necessary. Host-parasite studies, coevolution, etc. Jane is a software package (free with registration) that uses a heuristic approach, “running a genetic algorithm with an internal fitness function that is evaluated using a dynamic programming algorithm.” It can often give an optimal solution for that cophylogeny you are studying. Jane was developed in the research group of Ran Libeskind-Hadas at Harvey Mudd College and you can read more about the algorithm and approach here. They also have an extensive written tutorial. In these tips we usually focus on web-interface to tools, but I liked this package (and it’s free) and wanted to play around with it, so today I’ll walk you through a very quick intro to downloading and getting started with the tool. Quick Links: Jane Jane Tutorial CoPhylogeny Reconstruction TreeMap (another cophylogeny reconstruction software) CopyCat (yet another) Book Chapter on Cophylogeny and reconstruction Conow, C., Fielder, D., Ovadia, Y., & Libeskind-Hadas, R. (2010). Jane: a new tool for the cophylogeny reconstruction problem Algorithms for Molecular Biology, 5 (1) DOI: 10.1186/1748-7188-5-16
BioBase is a provider of expert-curated biological databases. Two well known BioBase databases are TransFac and HGMD. Both have publicly available data (see previous links), but if you go to the BioBase site, you’ll find there are subscription based data access also for more feature-rich access. HGMD is the Human Gene Mutation database and ” represents an attempt to collate known (published) gene lesions responsible for human inherited disease.” TransFac on the other hand “provides data on eukaryotic transcription factors, their experimentally-proven binding sites, consensus binding sequences (positional weight matrices) and regulated genes.” As you can tell from a search of our blog, HGMD is often cited as a good location for human disease data, as TransFac is for TFBS.
BioBase has a series of video tutorials for both TransFac and HGMD (and more for the other databases such as Proteome, Genome Trax and ExPlain). For this weeks tip of the week, we’ve embedded two video tutorials.
This first explains MATCH, an analysis tool in TransFac to predict binding sites for Transcription Factors in a particular DNA sequence.
The second video tip is a quick tutorial on how to get started with searching HGMD
If you are interested in advanced searching of these two databases, or Genome Trax, Proteome or ExPlain, check out the video tutorials from BioBase.
This week I have claimed Mary’s “collected” tip idea & will be featuring one of their videos as this week’s quick tip.
The Integrative Multi-species Prediction (IMP) web server is a gene-gene network analysis resource. There are several such resources (Cytoscape, IntAct, MINT, STRING, VisANT, and one of my personal favorites GeneMania) that OpenHelix has tutorials on (see our Pathway catalog listing). The IMP developers provide a nice amount of help for their users – not only do they have multiple YouTube videos (as do we on the OpenHelix YouTube channel), but they also offer two interactive tutorials that allow users to be guided through an example usage of IMP.
For today’s tip I am featuring the third YouTube video that they list on their tutorial page, because I thought it had the best sound and image quality. The other videos are also informative & are worth a viewing – enjoy!
Wong AK, Park CY, Greene CS, Bongo LA, Guan Y, & Troyanskaya OG (2012). IMP: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks. Nucleic Acids Research, 40 DOI: 10.1093/nar/gks458
A recent NCBI Newsletter announced the release of a new resource named the 1000 Genomes Dataset Browser, and that is the resource that I will be featuring in this tip. It is one of the tools available through the new NCBI Variation resources page, which also features resources such as dbSNP, dbVar, dbGaP and ClinVar (many of which OpenHelix has tutorials for) as well as other variation tools – Variation Reporter (pre-release version), Clinical Remap (beta version) and the Phenotype-Genotype Integrator.
Before I discuss NCBI’s 1000 Genomes Dataset Browser, I’d like to spend a bit of time on the 1000 Genomes project, in order to distinguish what is from NCBI and what is from the project itself. From the 1000 Genomes Pilot paper:
“The aim of the 1000 Genomes Project is to discover, genotype and provide accurate haplotype information on all forms of human DNA polymorphism in multiple human populations. Specifically, the goal is to characterize over 95% of variants that are in genomic regions accessible to current high-throughput sequencing technologies and that have allele frequency of 1% or higher (the classical definition of polymorphism) in each of five major population groups (populations in or with ancestry from Europe, East Asia, South Asia, West Africa and the Americas).”
You can access the full paper from the link below. The project has now moved past the pilot phase and is releasing new data all the time. You can see announcements and project details, or access that data, through the official 1000 Genomes project site, or through the official 1000 Genomes version of the Ensembl Browser. As you might imagine for a “big data” project such as this, data has been added to a variety of NCBI databases, including dbSNP, the Sequence Read Archive (SRA) and BioSample. Although you could search for this data through the universal Entrez search system, previously to view the data you would have to view individual results at each separate database. The 1000 Genomes Browser at NCBI has been created as a powerful interface for comprehensively searching for, and viewing, 1000 Genomes data contained in NCBI resources on a single page.
In the video tip I will familiarize you to the various areas of the page - the browser is created with series of widgets, each with its own function. I will not be able to cover all of the features, or demonstrate how users can upload their own variation data to the browser – I’ll leave you the fun of exploring those on your own. Because the tool is so young, bugs and suggestions/comments are still being actively requested – if you find something, check out the FAQs (which discuss bugs at various stages of being fixed) and then email the team.
NCBI Newsletter announcement July 20, 2012: http://1.usa.gov/RQu5dR
NCBI Variation page: http://www.ncbi.nlm.nih.gov/variation/
NCBI 1000 Genomes Browser page:
1000 Genomes Project site: http://www.1000genomes.org/home
The 1000 Genomes Project Consortium (2010). A map of human genome variation from population-scale sequencing Nature, 467, 1061-1073 DOI: 10.1038/nature09534
In looking through the 2012 Web Server Issue of NAR, Nucleic Acids Research journal, I couldn’t help notice resource names that revealed a bit about the developers’ sense of humor, such as “TaxMan” and “XXmotif“. There were others on the list (“MAGNET“, “GENIES” and “VIGOR“, for example) whose names made me cringe imagining someone trying to find them with the average search engine. [Our family’s favorite such resource is iHOP, or Information Hyperlinked Over Proteins - I gotta think that the developers aimed at that name in honor of the other IHOP and breakfasts everywhere.]
I scrolled through many such names until I found a resource to feature in today’s tip. I wanted something dealing with a current topic – they all pretty much fit that criteria – and one that I was interested in, but that was outside my “normal area of expertise”. I decided on “MetaboAnalyst 2.0“, which is the resource that I will feature in today’s tip. It is described in the article “MetaboAnalyst 2.0—a comprehensive server for metabolomic data analysis” as follows:
“MetaboAnalyst is a web-based suite for high-throughput metabolomic data analysis. It was originally released in 2009… MetaboAnalyst 2.0 now includes a variety of new modules for data processing, data QC and data normalization. It also has new tools to assist in data interpretation, new functions to support multi-group data analysis, as well as new capabilities in correlation analysis, time-series analysis and two-factor analysis. We have also updated and upgraded the graphical output to support the generation of high resolution, publication quality images.”
As I often do, I began “exploring” MetaboAnalyst 2.0 by reading their NAR article. It is well written and describes how the goal of the interface is to be user friendly and intuitive, so I headed over to MetaboAnalyst 2.0 “kick some tires”, so to speak. I found that the interface is quite easy & intuitive to use. And to really help users understand the resource before launching into uploading their own data, the developers provide a wide range of example data sets that users can play with, as well as step-by-step guides (pdf, PowerPoint, & two articles that require journal subscriptions, no videos yet). In my video I use one of their datasets & show a quick example of some analysis steps. Of course there isn’t time to fully cover MetaboAnalyst 2.0, but hopefully I show you enough to tempt you to try it out on your own.
*Please note that the developers suggest that you download results immediately because all user data is treated as private and confidential by MetaboAnalyst 2.0 will remain on the server for only 72 hours before automatically deleted.
MetaboAnalyst 2.0 – http://www.metaboanalyst.ca/
Jianguo Xia, Rupasri Manda, Igor V. Sinelnikov, David Broadhurst, & David S. Wishart (2012). MetaboAnalyst 2.0—a comprehensive server for metabolomic data analysis Nucleic Acids Research, 40 (W1) DOI: 10.1093/nar/gks374
Jianguo Xia, Nick Psychogios, Nelson Young, & David S. Wishart (2009). MetaboAnalyst: a web server for metabolomic data analysis and interpretation Nucleic Acids Research Volume 37, Issue suppl 2 Pp. W652-W660. , 37 DOI: 10.1093/nar/gkp356
In today’s tip I am linking to a YouTube video from NCBI that briefly explains the new Filters Sidebar feature that has been added to PubMed. We first saw a tweet that the change was coming back on May 2nd, just as I was completing a total update to our full PubMed tutorial*.
I struggled with whether to hold our production team for the new sidebar, or to produce our tutorial with the plan to update in the near future – it is always a struggle to know which is the best option because resource changes can occur at the speed of light, or according to geological time scales (ok, that’s an exaggeration but it feels that way when you want to release a wonderful, up-to-date project & something holds you up and causes delayed publication of our tutorial materials). With PubMed I was lucky – I saw a tweet that the sidebar feature would be added “in the next week”. I asked our voice professional to put the script on hold & I paced around PubMed waiting to see what (& when) things would occur.
True to their word, the sidebar feature showed up on PubMed results on May 10th, exactly one week since I had seen the “in the next week” announcement – my THANKS to the NCBI & PubMed Teams! Not only did they push out their updates in a timely manner, they made a YouTube video explaining the changes & discussing where future changes are slated to go. The video is clear, and quick, so I am using it as my tip this week. I’m not sure the feature is 100% stable, as I show in the image below, and describe later in the post, but I think the change might accomplish NCBI’s goal – for more people to notice & utilize filters for their searches.
In the video the narrator states that the filters area is gone & the two default filters are permanently selected, as indicated by the check marks that can’t be “unclicked”. I”m not seeing those check marks on either “Free full text available” link (shown) or the “Review” link, which is not in view in my image. I also see a difference as to whether I get the right filtered subsets depending on whether I am logged into My NCBI (the upper window shown in the back of the image), or not (the lower, front window). In my hands IE 9.0 & Firefox 12.0 both function similarly in these aspects.
The NCBI video doesn’t really show how results look after filters are added, but in playing with it to me it looks like all of your filters are applied to your search & you only get one set of results, not links to various subsets. Although it is now easier to add filters to searches, if that’s how filters are going to work going forward, I think I will miss the old filters – I kind of like being able to switch between various subcategories of results without having to change my filters or rerun searches. Be sure to share your thoughts & preferences with NCBI so that they can create the best resource for their users needs!
* OpenHelix tutorial for this resource available for individual purchase or through a subscription.
OpenHelix Introductory Tutorial on using PubMed (soon to be updated): http://www.openhelix.com/cgi/tutorialInfo.cgi?id=70
PubMed Resource: http://www.pubmed.gov/
Sayers, E.W., Barrett, T., Benson, D.A., Bolton, E., Bryant, S.H., Canese, K., Chetvernin, V., Church, D.M., DiCuccio, M., Federhen, S. & (2011). Database resources of the National Center for Biotechnology Information, Nucleic Acids Research, 40 (D1) D25. DOI: 10.1093/nar/gkr1184
There is none.
Ok, so that is the simple answer. The complicated answer is this: my ideal genome browser iPad app would have the flexibility to go from a mass market browser to look at an individual’s genomic variants in a genomic context with information about the research, genes, etc presented in such a manner so that any thoughtful person or doctor could understand, to a full fledged UCSC genome browser type research tool.
So, that’s not feasible. Instead, I’m going to look at three genome browsers for the iPad, two for research, one for the mass market. The former are GeneWall by Bioskoop and Wowser by the Children’s Hospital of Philadelphia, the latter is MyGenome by Illumina (links take you to iTunes app store).
The iPad and other touch tablets are perfect research assistants and the day is fast approaching, if it’s not already here, where most researchers will have one on the lab bench for entering and accessing data.
So what am I looking for in a iPad genome browser?
Navigable: it should have a very intuitive, iPad native navigation. I should be able to pinch and swipe my way through the genome with finely controlled ease.
Comprehensive: I should be able to access my genome of choice, past assemblies, and a huge range of annotations.
Flexible: I should be able to upload my own annotations with ease.
Why not just go to the UCSC Genome Browser? You could, but it fails the first test. It’s definitely usable, but many features available on a computer are not available on the iPad and navigation is obviously not iPad native.
GeneWall works nicely on the first criteria, but zooming in and out takes several (sometimes many) pinches of the fingers. There is no simple way to zoom in and out or walk the chromosome in a more fine tuned manner. On the second, though it comes with a single genome (human) with only a few annotation tracks so it’s not very comprehensive to start, you can easily add annotation tracks downloaded as bed files. So it has some flexibility. The pathway search and gene list function are nice too. A quick YouTube intro here.
Wowser is somewhat different in that it is an iPad interface to the UCSC Genome Browser. It works natively with the iPad and so is easy to navigate. Zooming and walking was intuitive. On a few different wireless networks it was slow to load, but not excruciatingly so. It is quite comprehensive, including the latest human reference sequence and many, but not all, of the UCSC tracks. The tracks are simple to hide or add in. Future updates are said to be including other genomes and more tracks. I could not find a way to add your own custom tracks or data, so on flexibility GeneWall wins out.
Both apps are great, if not quite “there” yet. I think either would be useful if you are looking at the human genome for research.
MyGenome is a different beast. From Illumina, it’s audience is not the researcher but the medical professional and patient. It’s a beautiful app with a nice interface. Easily navigable, it was simple to get to the information wanted. There is a lot of information there, but it is still quite limited. I took several variations that effect propensity for prostate cancer and other diseases and was unable to find information on them either because the variation is listed but no information, of variation was in an intergenic region which seemingly isn’t included. A user can not yet upload their own data,or other annotations, which is understandable since by Illumina’s own account this is only the beginning. Currently it’s a great educational tool (though I was a hung for more gene information), in the future it will be a good way to browse your own data.
So the bottom line for all three of these are that they are useful as they stand and for their stated purpose, but I’m looking forward to the future of browsing genome data like I was on an Avatar set :). It’s coming.
Who says social media is a waste of time? Not me – my LinkedIn updates keep including announcements of the “Image of the week” from The Cell: An Image Library. For my tip this week I decided to follow up on that & check out the images available from this resource, & I’m glad I did. The Cell Image Library is brought to you by the The American Society for Cell Biology (ASCB), and contains thousands of images, time series and groups of images, videos and animations of cells in a variety of organisms. Images are organized by Cell Process, Cell Component, Cell Type, Organism and Recently added. You can browse images or do a basic search from the homepage, or perform advanced searches. The advanced search form allows users to query with keywords, and for image attributes, specific image licensing categories, biological categories, imaging techniques, or associated anatomy terms.
To quote from their About page, the Cell Image Library:
“This library is a public and easily accessible resource database of images, videos, and animations of cells, capturing a wide diversity of organisms, cell types, and cellular processes. The purpose of this database is to advance research on cellular activity, with the ultimate goal of improving human health.”
And the library doesn’t merely allow you to access images, you can also provide your own images to be featured in the Library, as described in their “contribute” page. You contribute your raw data or minimally processed data images or videos to them and they will be annotated by professionals with broad disciplinary expertise. Each image receives a CIL, or Cell Image Library accession number, which can be used to reference an image.
In this tip I’ll touch on the features of the image displays, and anything else that I can fit in, but I can guarantee there is more for you to explore on your own. After watching our video tip, I suggest you head over to The Cell: an Image Library & check it out yourself. If you do, be sure to share your insights with the Library’s development team by filling out their user survey. Thanks!
The Cell: an Image Library – http://cellimagelibrary.org/
(On the utility of the Cell Image Library for science education) – Miller, K. (2010). Finding the key – cell biology and science education Trends in Cell Biology, 20 (12), 691-694 DOI: 10.1016/j.tcb.2010.08.008
Late last month the National Center for Biotechnology Information, or NCBI, released a new resource containing information on genetic tests. The resource’s name is the Genetic Testing Registry (GTR), and according to its homepage, the GTR:
” provides a central location for voluntary submission of genetic test information by providers. The scope includes the test’s purpose, methodology, validity, evidence of the test’s usefulness, and laboratory contacts and credentials. The overarching goal of the GTR is to advance the public health and research into the genetic basis of health and disease.”
I’m always interested in checking out new resources from NCBI, especially when it is my turn to do a weekly tip. Initially I figured that I would check out the GTR and post a video on how to use it – but the NCBI beat me to that. You can see their YouTube tips (there are two) by clicking the link on their homepage & learn some search tips, etc. [Note, the two videos continued to loop for me & I needed to stop them after viewing them once].
But the question that I came up with is, “What will the GTR provide me with that I am not already getting from other clinical resources that I use, and that OpenHelix trains on?” I try to address that question in my video by doing the same search, for “Cystic fibrosis”, at five different clinically-related resources, and discussing what each offers and specializes in doing. Of course, in a five minute video I can’t be comprehensive – either for resources or what they cover – but I think it will give you enough of a taste for you to appreciate what the GTR offers you, or to continue the comparison on your own.
The resources that I visit in the tip movie are: the GTR, GeneTests, the Genetic Home Reference (GHR), OMIM, and Orphanet. At each resource I do a basic search for the the disease “Cystic fibrosis” and show the initial results display that resulted. I don’t have time to compare the detailed reports available at each, but lower on the post I link to a reference on the resource (if available), as well as the landing page for OpenHelix training materials on the resource – since we have a tutorial on many of these resources. I also include direct links to each resource.
I’d suggest that you read the NIH News article on the GTR release for some background on the GTR. I won’t cover everything here, but there are a couple of paragraphs that I want to point your attention to. The first explains the relationship between GeneTests and GTR, and says:
“GTR is built upon data pulled from the laboratory directory of GeneTests, a pioneering NIH-funded resource that will be phased out over the coming year. GTR is designed to contain more detailed information than its predecessor, as well as to encompass a much broader range of testing approaches, such as complex tests for genetic variations associated with common diseases and with differing responses to drugs. GeneReviews, which is the section of GeneTests that contains peer-reviewed, clinical descriptions of more than 500 conditions, is also now available through GTR.”
It seems to be another case where it was deemed easier to start a new resource (GTR) than to try and revamp an old resource (GeneTests) to handle the amazing influx of new data. Often resources aren’t retired as soon as expected, due to user feedback, but it is important to note that GTR seems to be in place to eventually replace GeneTests. I assume the GeneReviews will still be edited by & copyright to the University of Washington, Seattle, but I don’t have a reference for that. The similar transition occurred for OMIM, which was hosted at NCBI for years but now has a new URL at Johns Hopkins (watch for our new tutorial on OMIM, which is currently in the works).
The second paragraph that I found particularly interesting was the one on what the GTR contains, and will contain. It states:
“In addition to basic facts, GTR will offer detailed information on analytic validity, which assesses how accurately and reliably the test measures the genetic target; clinical validity, which assesses how consistently and accurately the test detects or predicts the outcome of interest; and information relating to the test’s clinical utility, or how likely the test is to improve patient outcomes.”
I didn’t immediately find mention of who will provide the validity or utility information in the GTR documentation, which is currently under construction. It is clear that much of the content of the database will be “voluntarily submitted by test providers”, and it is stated that “NIH does not independently verify information submitted to the GTR; it relies on submitters to provide information that is accurate and not misleading.”, but I also saw that experts will input on GTR’s content regularly, as can be read here. The GTR team is also very interested in receiving input on the resource, which can be submitted through the GTR feedback form.
The Genetic Testing Registry (GTR) – http://www.ncbi.nlm.nih.gov/gtr/
GTR YouTube Tips from NCBI – http://www.youtube.com/playlist?list=PL1C4A2AFF811F6F0B
GeneTests Introductory Tutorial by OpenHelix* – http://bit.ly/genetests
Genetic Home Reference (GHR) – http://ghr.nlm.nih.gov/
GHR Introductory Tutorial by OpenHelix* – http://bit.ly/geneticshomeref
Online Mendelian Inheritance in Man (OMIM) – http://www.omim.org/
OMIM Introductory Tutorial by OpenHelix – (coming soon, currently being updated)
Orphanet – http://www.orpha.net/
*OpenHelix tutorials for these resources available for individual purchase or through a subscription
For GeneTests (free from PMC) – Pagon RA (2006). GeneTests: an online genetic information resource for health care providers. Journal of the Medical Library Association : JMLA, 94 (3), 343-8 PMID: 16888670
For GHR (free from PMC) – Mitchell JA, Fomous C, & Fun J (2006). Challenges and strategies of the Genetics Home Reference. Journal of the Medical Library Association : JMLA, 94 (3), 336-42 PMID: 16888669
For OMIM (open access article) – Amberger, J., Bocchini, C., & Hamosh, A. (2011). A new face and new challenges for Online Mendelian Inheritance in Man (OMIM®) Human Mutation, 32 (5), 564-567 DOI: 10.1002/humu.21466
For Orphanet (full access requires subscription) - Aymé, S., & Schmidtke, J. (2007). Networking for rare diseases: a necessity for Europe Bundesgesundheitsblatt – Gesundheitsforschung – Gesundheitsschutz, 50 (12), 1477-1483 DOI: 10.1007/s00103-007-0381-9
In today’s tip I will briefly introduce you to the beta version of the updated DGV resource. The Database of Genomic Variants, or DGV, was created in 2004 at a time early in the understanding of human structural variation, or SV, which is defined by DGV as genomic variation larger than 50bp. DGV has historically provided public access to SV data in humans who are non-diseased. In the past it both accepted direct data submissions on SV and also provided high quality curation and analysis of the data such that it was appropriate for use in biomedical studies.
We’ve had an introductory tutorial on using DGV for years, and we’ve posted on changes at DGV in the past, so we were quite interested to read in their recent newsletter that there is a newly updated beta version of the DGV resource. The increase in SV data being generated by many large-scale sequencing projects as well as individual labs, has made it difficult for the DGV to continue to collect SV data, to provide a stable and comprehensive data archive AND to manually curate it at the level they have in the past. Therefore the DGV team is now partnering with DGVa at EBI and dbVar at NCBI. DGVa and dbVar will accept SV data submissions, and will function as public data archives (PDA) and, according to the publication sited below, DGVa and dbVar will:
“...provide stable and traceable identifiers and allow for a single point of access for data collections, facilitating download and meta-analysis across studies.“
DGV will no longer accept data submissions, but will instead use accessioned SV data from the archives and focus on providing the scientific community and public at-large with a subset of the data. Again quoting from the paper referenced below:
“The main role of DGV going forward will be to curate and visualize selected studies to facilitate interpretation of SV data, including implementing the highest-level quality standards required by the clinical and diagnostic communities.“
The original DGV resource is still available while comments are collected on the updated beta site. For more information on the updated DGV I suggest you check out this documentation from the DGV team: From their FAQ – “What is the data model used for DGV2?” and from a link in their top navigation area – “DGV Beta User Tutorial“. Be sure to check out the new displays & data that’s available, and most importantly to send your comments & suggestions to the group so that they can design a resource best suited for your needs.
Original Database of Genomic Variants: http://projects.tcag.ca/variation/
New beta version of the Updated DGV: http://dgvbeta.tcag.ca/dgv/app/home
Introductory OpenHelix on Original DGV: http://www.openhelix.com/cgi/tutorialInfo.cgi?id=88
DGV Beta User Tutorial from DGV: http://dgvbeta.tcag.ca/dgv/docs/20111019-DGV_Beta_User_Tutorial.pdf
Church, D., Lappalainen, I., Sneddon, T., Hinton, J., Maguire, M., Lopez, J., Garner, J., Paschall, J., DiCuccio, M., Yaschenko, E., Scherer, S., Feuk, L., & Flicek, P. (2010). Public data archives for genomic structural variation Nature Genetics, 42 (10), 813-814 DOI: 10.1038/ng1010-813
(Free access from PubMed Central here)
Edit, March 5, 2012 – I wanted to add a clarification that we recieved through our contact link. I am pasting it in full, with permission from Margie:
We at TCAG think you did a great job on your video blog of the New Database of Genomic Variants.
I wanted to make a correction to one of your statements: “The increase in SV data (…) at the level they have in the past.”
We, the DGV team, have built a system that CAN handle the new volumes and types of SV data now being published, and we are able to curate all of these data. The reason we partnered with DGVa and dbVar was primarily to provide stable, “universal” accessions for SV data. We also work with DGVa and dbVar to define standard terminology, data types, and data exchange formats.
I just wanted to make sure it was clear that we are fully capable to handle the SV data being published now. Our reason for partnership was to foster standardized data and open data sharing across systems.
Thanks again for your blog post!