카테고리 아카이브: 금주의 팁


주의 비디오 도움말: New UCSC Genome Browser Gateway look


For years now we’ve been doing training and outreach on the UCSC 게놈 브라우저. And there’s been a lot of change over the years–so much more data, so many new tools, 새로운 종. All that ENCODE information and a portal 그. But the look of the main site was largely the same. Here’s a post we did that included the UCSC site traffic in 2000, and another time we took a look at the old style interface ~2004. And there was the switch to the new blue look in 2012.

그러나, the main gateway page was largely the familiar look. The gateway–where you begin to do most text-based or region-based queries for a species–was mostly altered only with some additional buttons and options. And an increasingly long list of species to choose from. 하지만 지금은–it’s time to look again. The gateway is very different today. You’ll have faster and easier access to get started when you go to the site, and new ways to engage with the data that you want to begin to access.

There are additional details on the UCSC landing page in the News area, including credits to the development team involved. The other key pieces include some relocations of the previous button options:

Note that a few browser utilities that were previously accessed through links and buttons on the Gateway page have been moved to the top menu bar:

*Browser reset: 게놈 브라우저 > Reset All User Settings
*Track search: 게놈 브라우저 > Track Search
*Add custom tracks: My Data > 사용자 정의 트랙
*Track hubs: My Data > 트랙 허브
*트랙을 구성 및 표시: 게놈 브라우저 > Configure

The UCSC team has created a short intro video to the new look. That is our Video Tip of the Week:

물론 이죠, this means we’ll need to update our slides and exercises. We like things to stabilize a bit after a rollout to be sure things are solid. But soon we’ll include the new navigation in our materials.

The underlying ways to access the particular assembly features you need for a given genome, and the data for your tracks of interest, is unchanged. So those parts of our training materials will still help you to get the most out of your searches. We’ll let you know when we’ve made the changes to the materials as well.


빠른 링크:

UCSC Genome Browser main landing page: HTTP를://genome.ucsc.edu

Training materials:

소개: http://openhelix.com/ucsc

고급: http://openhelix.com/ucscadv


박차, 엠, 분기, 대답 :, Rosenbloom, 사장님, 래니 소장, B를, 스폰서, B를, Nejad, 추신, 리, B를, 배운, 사장님, Karolchik, 디, 힌릭스, 대답 :, Heitner, 미국, 하트, 기철, Haeussler, 엠, Guruvadoo, 실은, 후지타, 추신, Eisenhart, C., Diekhans, 엠, 클로슨, 반장님, 캐스퍼, 제이, 이발사, 샷, Haussler, 디, Kuhn, 기철, & 켄트, 에. (2015). UCSC의 게놈 브라우저베이스: 2016 업데이트 핵산 연구 간접 자원부: 10.1093/nar/gkv1275

공개: UCSC Genome Browser tutorials are freely available because UCSC 스폰서 us to do training and outreach on the UCSC Genome Browser.

expVIP example

주의 비디오 도움말: expVIP, an Expression, 시각화, and Integration Platform

내가 지난 주에 언급했듯이, I am watching a lot of farmers on twitter talk about this year’s North American growing season. To get a taste of that yourself, 에 모습을 가지고 #Plant16 + 밀 as a search. This is where the rubber of tractor tires and plant genomics hits the…음…rows. And just coincidentally I saw a story about this new plant genomics research tool–actually in the farming media.

It’s kind of nice to see plant bioinformatics get some recognition beyond the bioinformatics nerd community. The piece “New online tool helps predict gene expression in food crops” did a pretty good job of talking about the features of the expVIP tool, and I was eager to have a look.

expVIP stands for expression isualization과 NTEGRATING latform. expVIP exampleAlthough the emphasis here is plant data, it can be used for any species. A good summary of their project is taken from their paper (아래 링크):

expVIP takes an input of RNA-seq reads (from single or multiple studies), quantifies expression per gene using the fast pseudoaligner kallisto (Bray et al., 2015) and creates a database containing the expression and sample information.

And it can handle polyploid species–try that on some of the tools aimed at human genomics! They illustrate this with some wheat samples from a number of different studies. And then they use the metadata about the studies, such as tissues and treatment conditions, to show how it works with some great sorting and filtering options. They created a version of this for you to interact with on the web: Wheat Expression Browser. But you can create your own data collections with their tools, aimed at your species or topics of interest.

This week’s Video Tip of the Week is their sample of how this Wheat Expression Browser works. Although you see the wheat data here, it’s just an example of how it can work with any species you’d like to examine.

I followed along and tried what they were showing in the video, and I found it to be a really slick and impressive way to explore the data. The dynamic filtering and sorting was really nice. You can customise the filtering/sorting/etc for the visualizations with the metadata that’s useful to your research. So you could set the tissue types, or treatment conditions, or whatever you want–and filter around to look at the expression with those. They go on to show that their strategies to compare genes in different situations seemed to reflect known biology in disease and abiotic stress conditions.

So their pipeline for gene matching, as well as the tools to explore and visualize RNA-Seq data, offer a great way to look at data that you might generate yourself or you could mine from existing submitted data–but that might not be well organized and available in a handy database just yet.

빠른 링크:

Wheat expression browser: www.wheat-expression.com

expVIP at GitHub: https://github.com/homonecloco/expvip-web


Philippa Borrill, Ricardo Ramirez-Gonzalez, & Cristobal Uauy (2016). expVIP: a customisable RNA-seq data analysis and visualisation platform 식물 생리학, 170, 2172-2186 : 10.​1104/​pp.​15.​01667


주의 비디오 도움말: SoyBase CMap

SoyBaseOver the years I’ve started to follow a lot of farmers on twitter. It might sound odd to folks who are immersed in human genomics and disease. But I actually find the plant and animal genomics communities to be pushing tech faster and further to the hands of end-users than a lot of the clinical applications are at this point in time. And as #Plant16 rolls out to feed us, there was a lot of soybean chatter in my twittersphere.

So when SoyBase tweeted a reminder about some of their videos, I thought the timing was great. 그들은이 YouTube 채널 for some videos to help users access the SoyBase data. And one of the tools they illustrate is CMap. Although we’ve touched on CMap a couple of times on the blog and in our training videos, we never featured it. It’s one of the GMOD family members that can offer you comparisions of different map coordinate data sets. But conceptually I think it’s a good idea for people to think about physical map vs sequence mapping data. And this video shows how you can examine these different representations at SoyBase.

Besides their software videos, 그래도, SoyBase also links to a lot of other videos that help people to understand more about many aspects of soybean cultivation. Check out their wide range of topics on their 비디오 자습서 페이지. You never see how to use a two row harvester at human genomics databases, do you?

빠른 링크:

SoyBase: http://www.soybase.org/

모자 팁:


부여, 디, 넬슨, 기철, Cannon, 미국, & Shoemaker, 연구. (2009). SoyBase, the USDA-ARS soybean genetics and genomics database 핵산 연구, 38 (데이터베이스) 간접 자원부: 10.1093/nar/gkp798


주의 비디오 도움말: Pathfinder, for exploring paths through data sets

Pathfinder_scapI didn’t expect to do another tip on the paths through experiments or data this week. But there must be something in the water cooler lately, and all of these different tools converged on my part of the bioinformatics ecosphere. As I was perusing my tweetdeck columns, a new tool from the folks who do the Caleydo projects offered more paths through data: Pathfinder, Visual Analysis of Paths in Graphs.

For years I’ve been celebrating the great visualization options from the Caleydo tools. The first time we highlighted them was 2010. But I’ve been continuing to follow their work and kick the tires when they have new ones. My most recent favorite of theirs was 당황–a better-than-Venn way to look at sets and subsets among your data.

This new tool offers another way to look across relationships in data sets. Finding paths through data is only getting harder with every new data set we get, but continues to become more important to pull in the characteristics of the alternate routes and yet still have the context of the overall picture. Scaling paths is hard. So the Calydo team aims at several key aspects of the problem with their new Pathfinder tool. The full details are in the paper (아래의 인용), but I’ll list the points for the features they deliver here:

1. Query for paths.
2. Visualize attributes.
3. Visualze group structures in paths.
4. Rank paths.
5. Visual topology context.
6. Compare paths.
7. Group paths.

In addition to clever visualization and query strategies, the team always offers an nice intro video to give you a sense of what the tool can do for you. So the new video on Pathfinder is our Video Tip of the Week.

The example used is the sets of authors on publications. But it’s easy to imagine signalling pathways, or some types of sequence variation pathways, or many other kinds of paths researchers need to represent. They have a use case example in the paper of KEGG 경로. 동영상에, there’s a quick look at a pathway that includes copy number variations and gene expression data as attributes that may be important for understanding the paths.

한번 사용해보세요. There’s a demo site available (아래 링크), and start to think about how you could use Pathfinder to analyze data that you are interested for your research directions.

Hat tip to Alexander Lex for the notice of the new tool:

빠른 링크:

Pathfinder demo: http://demo.caleydo.org/pathfinder/

Pathfinder overview site: http://www.caleydo.org/publications/2016_eurovis_pathfinder/

Source code: https://github.com/Caleydo/pathfinder/


기독교 Partl, 사무엘 Gratzl, 마크 얼굴 그래픽 업데이트를 실시하므로 원하시는, Anne Mai Wassermann, Hanspeter 피스터, 디터 Schmalstieg, & 알렉산더 렉스 (2016). Pathfinder: Visual Analysis of Paths in Graphs 컴퓨터 그래픽 포럼 ((EuroVis ’16)) In press.


주의 비디오 도움말: 분기, 데이터 분석을위한 웹 기반 툴을 제공하는 의사 결정 나무

최근에 내가 highlighted a decision tree tool for experimental design. EDA, 또는 Experimental Design Assistant, helps you to plan your experiment, choose the approrpiate groups and numbers you’ll need. Set some variables, 등등. This week’s video also offers decision trees–but these help you to evaluate the data from your studies of interest instead. 분기 is a web-based tool to help you test your hypotheses and develop models using data that’s available in a given data collection.

BranchThere’s a paper (아래 링크) with the backstory and information about how the tool works. But they’ve also done a nice series of videos to show you how to interact with the tools. The first one will be this week’s Video Tip of the Week. But be sure to check out the other ones for additional features as well. Each video tackles different aspects of the functionality that will help you to get the most from your explorations.

한번 사용해보세요. You can use existing examples, or include your own data. You can make your own data private, or make it available to share with others. Be sure to read their disclaimers and think carefully if you are using certain data sets that have privacy issues. But there are probably many publicly available data sets that could get you exploring some hypothesis around your topic of interest.

Hat tip to the author whose tweet sent me looking to investigate this:

빠른 링크:

Branch web site: http://biobranch.org/


Gangavarapu, 사장님, Babji, 브이, Meißner, 토니, 그의, 대답 :, & 좋은, B 조. (2016). 분기: an interactive, web-based tool for testing hypotheses and developing predictive models 생물 정보학 간접 자원부: 10.1093/bioinformatics/btw117


주의 비디오 도움말: RGD의 OLGA 도구, 목록 발생기 및 분석기 개체

Lior_RatVenn_smOne of the really persistent issues in genomics is how to either get a list of things, or handle a list of things. or the overlap among the things. I think that was one of the most popular topics we dealt with in the early days of OpenHelix, but it’s still a issue that people need to handle in various ways. Some of the most interesting solutions have been various organism Venn diagrams, and the Rat Genome one is a classic, modeled here by Lior Pachter. I’m certain the need to list and organize genome features won’t go away. So when I saw that the RGD folks had another tool to offer ways to do this, I put it right in my list of upcoming tips. And then the draft post got buried under a list of other things I had to do. But I wanted to get back to it–so here is their step-by-step guide to the OLGA tool they offer, as this week’s Video Tip of the Week.

OLGA stands for: Object List Generator and Analyzer tool. Their newsletter announcement describes it in more details.

OLGA is a straightforward list builder for rat, human and mouse genes or QTLs, or rat strains, using any (또는 모든) of a variety of querying options. The new tutorial video will walk you through the process of querying the RGD database using OLGA, 포함

  • how to perform a simple query in OLGA
  • how to further expand or filter your result set using additional criteria
  • how to change your query parameters on the fly to refine your result set
  • what options OLGA gives for analysis of your list once you have it.

You can get a list of items using various ontologies–maybe you want a specific type of receptor, 예를 들면, you can get a list of them. Or you can quickly create a list of genes in a certain genomic span. You can get the items that fall in a QTL. Or you can start with a list and get annotations. You can also look for overlaps among sets.

The video is a nice walk-through of how to construct your query and what you can access. One key feature is that it’s not just rat data as you might expect at RGD. Mouse and human data are also available.

You can create complex and clever queries, and link to all sorts of related data in very easy steps. Have a look at their resources, and their other videos for more help with different aspects of their collections.

빠른 링크:

RGD main site: http://rgd.mcw.edu/

OLGA directly: http://rgd.mcw.edu/rgdweb/generator/list.html


Shimoyama, 엠, De Pons, 제이, Hayman, 샷, Laulederkind, 미국, 리우, 더블유, Nigam, 기철, Petri, 브이, 스미스, 제이, Tutaj, 엠, 왕, 미국, Worthey, 이봐요, E., Dwinell, 엠, & Jacob, H 조. (2014). The Rat Genome Database 2015: 게놈, phenotypic and environmental variations and disease 핵산 연구, 43 (D1) 간접 자원부: 10.1093/nar/gku1026


주의 비디오 도움말: EDA, Experimental Design Assistant

Most of the bioinformatics tools we examine are things that come into play downstream of an experiment. People wish to analyze their data, look at genes that popped up (or dropped down), visualize relationships, 등등. So this week’s Video Tip tool is unusual–it’s software that helps people design the upstream pieces of their experiments.

Experimental Design Assistant is targeted at the proper design of animal research studies. Using animals carefully and responsibly includes well-designed experiments, because wasted experiments due to poor design is something researchers should want to avoid. It’s bad animal welfare practice, and it’s also expensive. The EDA folks describe this very nicely on a background piece linked on their site.

Because of the way they have their Vimeo settings, I can’t embed their video here. You’ll have to click to watch it on their site: https://eda.nc3rs.org.uk/guide-tutorials


The 13 minute video is a nice overview of how the workflow will guide you. They recommend that you start with some of their templates that might be similar to your research goals, and edit that. They show you how to start with a blank canvas or a template in the video. They illustrate how you can set up different groups of animals, denote some kind of pharmaceutical intervention or treatment–in the case they show it’s different light cycles. You can establish doses or other variables that are appropriate. Then you move on to a “Measurement” node. They demonstrate that only the right connections in the diagrams can be made, or you get warnings. Then an outcome node can be added. There’s a way to add numerous variables and other experimental details that need to be accounted for.

Other shorter tutorials cover other pieces–like critiquing your experiment, power calculation and randomization sequence, exporting/importing and sharing the diagrams you create.

This is a different but really useful kind of biology software tool. I think it could be great in teaching situations as well. 당신은 그것을 체크 아웃해야합니다.

빠른 링크:

Experimental Design Assistant: http://eda.nc3rs.org.uk/

Videos page: https://eda.nc3rs.org.uk/guide-tutorials

참고 문헌:

Cressey, 디. (2016). Web tool aims to reduce flaws in animal studies 자연, 531 (7592), 128-128 간접 자원부: 10.1038/531128a

UCSC Genome Browser new feature

주의 비디오 도움말: Multi-region visualization in the UCSC Genome Browser

This week’s video tip demonstrates a new feature at the UCSC 게놈 브라우저. I think it’s kind of unusual, and conceptually took me a little while to get used to when I started testing it. So I wanted to go over the basics for you, and give you a couple of tips on things that I had to grok as I got used to this new visualization option.

The headline for the news item describes it as: “Combine Multiple Regions of the Genome Browser into a Single Visualization!” 및

Have you ever wished you could remove all of the intronic or intergenic regions from the Genome Browser display? Have you ever dreamed of being able to visualize multiple far-flung regions of a genome? 음, now you can with the new “multi-region” option in the Genome Browser!

I should probably start with the first thing that confused me–the name “multi-region”. I thought that I was going to be able to see maybe part of a region on chromosome 1, and something on chromosome 8, maybe at the same time. But that’s not how this works. 이 경우에는, you look at multiple regions along the same chromosome, with some of the intervening sequences snipped out. This creates a sort of “virtual chromosome” for you to interact with.

이번 주 비디오, I’ll show you how that looks using the BRCA1 gene. First I show how you can look at all the exons together–with introns clipped out. And then I show how you can see the genes in the neighborhood displayed together, with the non-coding regions clipped out. 이들은 2 of the separate options for viewing.

내가 사용하는 “전망” menu option to illustrate this feature. But there is another way to access it–you can use the “multi-region” button in the browser buttons area.


To keep the video short, I didn’t go into every detail on this tool. You should check out the news announcement for it, and the link to the additional details in the User Guide documentation 자세한. The new feature is also mentioned briefly in the lastest NAR paper on the UCSC Genome Browser (아래 링크). And you should try it out, 물론! That’s the best way to really understand how it might help you to visualize regions of the genome that you might be interested in.

Also as in the news, thanks to the development team. I am always looking for new visualizations, and this fun to test!

Thank you to Galt Barber, 마태 복음 Speir, and the entire UCSC Genome Browser quality assurance team for all of their efforts in creating these exciting new display modes.

Follow UCSC on Twitter:

빠른 링크:

UCSC 게놈 브라우저: genome.ucsc.edu

News item on multi-region: http://genome.ucsc.edu/goldenPath/newsarch.html#030816

Training materials on the UCSC Genome Browser: http://openhelix.com/ucsc


박차, 엠, 분기, 대답 :, Rosenbloom, 사장님, 래니 소장, B를, 스폰서, B를, Nejad, 추신, 리, B를, 배운, 사장님, Karolchik, 디, 힌릭스, 대답 :, Heitner, 미국, 하트, 기철, Haeussler, 엠, Guruvadoo, 실은, 후지타, 추신, Eisenhart, C., Diekhans, 엠, 클로슨, 반장님, 캐스퍼, 제이, 이발사, 샷, Haussler, 디, Kuhn, 기철, & 켄트, 에. (2016). UCSC의 게놈 브라우저베이스: 2016 업데이트 핵산 연구, 44 (D1) 간접 자원부: 10.1093/nar/gkv1275

공개: UCSC Genome Browser tutorials are freely available because UCSC 스폰서 us to do training and outreach on the UCSC Genome Browser.

"Frictionless connection of bioinformatics tools"

주의 비디오 도움말: GenomeSpace Orientation

"Frictionless connection of bioinformatics tools"

“Frictionless connection of bioinformatics tools”

The GenomeSpace site has been highlighted before in our “주 도움말”. We appreciated this site that pulled together a lot of different useful types of data sources and analysis strategies. On their site they describe their ethos as “Frictionless connection of bioinformatics tools”. Since that time (2012), it’s continued to grow and provide new features. So I was delighted to see that there was a new orientation video that they offered, and that is this week’s Video Tip of the Week.

Currently there are 20 tools connnected in GenomeSpace, many more than when we first looked. These include mining, 시각, and workflow tools. This intro video focuses on a couple of them, 유전자 패턴 for demonstrating workflow, 및 Cytoscape for visualization. But you can see how the others would help support many types of genomics analyses.

This overview talks about their “recipes” 개념, with step-by-step analysis protocols, which can be found here: HTTP를://recipes.genomespace.org . And there’s a demo of the recipe resource. 몇 가지가 있습니다 “공무원” recipes from their team, but they definitely want to have people contributing their own as well. Towards the end of the video they describe how to do that (~28min).

The one used to illustrate the features of the recipes includes a narrative description, but also the specific steps that would be employed. This has the GenePattern and Cytoscape steps examples that they use in the demo.

About half-way through, the demo of the analysis starts (~14min). It’s a helpful walk-through of how to use the recipes effectively to reproduce an analysis. Sara Garamszegi, our guide here, completes the pieces of the work that need to be done with GenePattern, and then shows how to pull out the file you generate from GenomeSpace for Cytoscape to use on your desktop.

도있다 separate video of the question/answer section, so if you had some unresolved issues you might check if they were covered, or you can hear about how others might be considering using the tools. I often learn as much from the questions as from the formal presentation pieces. They have transcribed the issues in their video info section as well so you could just quickly scan them.

Follow them on Twitter for more like this, and you can also follow their YouTube channel:

빠른 링크:

GenomeSpace: http://www.genomespace.org/

참고 문헌:

숨어, 사장님, Garamszegi, 미국, 우, F., Thorvaldsdottir, 반장님, Liefeld, 토니, Ocana, 엠, Borges-Rivera, 디, Pochet, 북아 일, 로빈슨, 제이, Demchak, B를, 선체, 토니, Ben-Artzi, 샷, Blankenberg, 디, 이발사, 샷, 리, B를, Kuhn, 기철, Nekrutenko, 대답 :, Segal, 이봐요, E., IDEK, 토니, Reich, 엠, Regev, 대답 :, 창, 반장님, & Mesirov, Kokocinski. (2016). Integrative genomic analysis by interoperation of bioinformatics tools in GenomeSpace 자연 방법, 13 (3), 245-247 간접 자원부: 10.1038/nmeth.3732


주의 비디오 도움말: Introduction to Biocuration and the career path


The ISB is a professional organization for biocurators

OpenHelix시, we’ve long sung the praises of curators. Some of us have been curators and worked with curation and database development teams. All of us have relied on quality information in the databases for research and teaching. But I think there are a lot of people who don’t understand the value of quality curation, how it’s done, and who curators are. They are widely taken for granted.

A recent talk by Claire O’Donovan of EBI-EMBL helps to explain the roles and the importance of biocurators. So although this talk isn’t a typical software talk, I think understanding this is crucial to everyone’s appreciation of how information you rely on gets into the databases you use. And if you find yourself in situations where you are guiding students, knowing about this career is also worthwhile.

Claire O’Donovan has had a front row seat to the development of this field, and has great enthusiasm for the future. And going forward, in your doctor’s office as precision medicine and treatments become a thing–how much do you want correct information in the databases? Mining data, standardizing language for descriptions of features, and sharing this information is crucial for all of us.

Here’s what’s covered in this video, from the agenda slide:

  • Introduction to the concept of biocuration.
  • The different kinds of biocurators, and the skill set needed.
  • Our community: Biocuration Society and conference.
  • The future of biocuration and career paths.

Specific examples of what curators do are illustrated (~6:30나의). A sample UniProt entry illustrates what kind of information is captured and where it appears. She also touches on their work with 유전자 온톨로지. And a bit about the ecosystem of curation, how teams at different resources help each other but don’t wish to duplicate work, 사용 HGNC nomenclature as an example.

About 8min, the skill sets for biocuration are covered: data basics, curation skills, programming and database concepts, 온톨로지, and usability of the data collected. This also includes data access and management, as well as dissemination and outreach. This includes user training (봄!) and the concepts of data analysis for users.

There’s no formal degree path for curation practitioners at this point, and different groups will have different needs. But the community is begining to think about this, and about professional qualifications. She also mentioned a recent report from the National Academy of Sciences press on the topic of the future workforce skills and needs (아래 링크). This is an alternative career route for people with science training, and it’s important to understand not only the science but computational pieces. And it should be taken seriously as a discipline. There is now a journal that reflects this (also linked below).

Claire also takes a look at the future of biocuration, 를 사용하여 Center for Target Validation (CTTV) 예를 들어. And she talks about the importance of quality information in medical records as we increasingly have genomic details in diagnosis and treatment situations. If we want precision medicine to work, we have to have the precise and correct information in the databases. So respect and value the curators. They are worth it. And if you know anyone that deserves special recognition–nominate!

빠른 링크:

Biocuration 국제 학회: http://biocuration.org/

Preparing the Workforce for Digital Curation: HTTP를://www.nap.edu/catalog/18590/preparing-the-workforce-for-digital-curation

참고 문헌:


Holliday, 샷, Bairoch, 대답 :, Bagos, 추신, Chatonnet, 대답 :, Craik, 디, 핀 사람, 기철, Henrissat, B를, 촌뜨기, 디, 매닝, 샷, Nagano, 북아 일, 도노반, C., 프륏, 사장님, Rawlings, 북아 일, Saier, 엠, Sowdhamini, 기철, Spedding, 엠, SGingerasn, 북아 일, Vriend, 샷, 배빗, 추신, & , A., 블레 이크, J., Bult , A. (2015). Key challenges for the creation and maintenance of specialist protein resources 단백질: 구조, Function, and Bioinformatics, 83 (6), 1005-1013 간접 자원부: 10.1002/prot.24803

고뎃, 추신, 무 노즈 - 토레스, 엠, Robinson-Rechavi, 엠, 엣우드, 토니, , A., 블레 이크, J., Bult , 대답 :, Cherry, J.,제이span class ="tr_" id="t토니46" data-token="Q2hpc2hvbG0," data-source="">Chisholm, 제이, Kania, 기철, 도노반, C., & Yamasaki, C 조. (2013). DATABASE, 생물 학적 데이터베이스와 Curation의 학회지, is now the official journal of the International Society for Biocuration 데이터베이스, 2013 간접 자원부: 10.1093/database/bat077