分类存档: 提示的周


一周的视频提示: New UCSC Genome Browser Gateway look


For years now we’ve been doing training and outreach on the UCSC基因组浏览器. And there’s been a lot of change over the years–so much more data, so many new tools, 新种. All that ENCODE information and a portal 该. 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.

不过, 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. 但现在–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.

有在新闻领域的UCSC登陆页面上的其他详细信息, 包括信贷所涉及的开发团队. 另一个关键部分包括以前的按钮选择一些重定位:


*浏览器复位: 基因组浏览器 > 重置所有用户设置
*曲目搜索: 基因组浏览器 > 音轨搜索
*添加自定义轨道: 我的数据 > 自定义跟踪
*Track hubs: 我的数据 > 轨道枢纽
*配置跟踪和显示: 基因组浏览器 > 配置

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

当然, 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.



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

Training materials:

简介: http://openhelix.com/ucsc

进阶: http://openhelix.com/ucscadv


带动, 米, 科, 答:, 罗森布鲁姆, 光, 雷尼, 二, 赞助商, 二, Nejad, 体育, 李, 二, 学习, 光, Karolchik, 四, Hinrichs先生, 答:, 海特纳, 学, 硬, 河, Haeussler, 米, Guruvadoo, 属, 藤田, 体育, Eisenhart, 三, Diekhans, 米, 克劳森, 阁下, 卡斯帕, j的, 理发, 克, 豪斯勒速度, 四, 库恩, 河, & 肯特, 在. (2015). UCSC基因组浏览器数据库: 2016 更新 核酸研究 分类号: 10.1093/nar/gkv1275

披露: UCSC Genome Browser tutorials are freely available because UCSC 赞助商 us to do training and outreach on the UCSC Genome Browser.

expVIP example

一周的视频提示: expVIP, an Expression, 可视化, and Integration Platform

正如我上周提到的, 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, 看看 #Plant16 + 小麦 as a search. This is where the rubber of tractor tires and plant genomics hits the…好…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 isualization和 NTEGRATING Platform. 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 (下面链接):

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.


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 植物生理学, 170, 2172-2186 : 10.​1104/​pp.​15.​01667


一周的视频提示: 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. 他们有一个 YouTube频道 for some videos to help users access the SoyBase data. And one of the tools they illustrate is CMap的. 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 全球媒体点播 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, 虽然, 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 视频教程页. You never see how to use a two row harvester at human genomics databases, do you?


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



津贴, 四, 尼尔森, 河, Cannon, 学, & Shoemaker, ř. (2009). SoyBase, the USDA-ARS soybean genetics and genomics database 核酸研究, 38 (数据库) 分类号: 10.1093/nar/gkp798


一周的视频提示: 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 烦恼–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 (下面列举), 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的 途径. 在视频中, 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.

试试吧. There’s a demo site available (下面链接), 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:


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/


基督教Partl, 塞缪尔Gratzl, 马克·斯特雷特, Anne Mai Wassermann, Hanspeter菲斯特, 迪特Schmalstieg, & 亚历山大·莱克斯 (2016). Pathfinder: Visual Analysis of Paths in Graphs 计算机图形学论坛 ((EuroVis ’16)) In press.


一周的视频提示: 科, 数据分析基于Web的工具提供决策树

最近,我 highlighted a decision tree tool for experimental design. EDA, 或 Experimental Design Assistant, helps you to plan your experiment, choose the approrpiate groups and numbers you’ll need. Set some variables, 等. This week’s video also offers decision trees–but these help you to evaluate the data from your studies of interest instead. 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 (下面链接) 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.

试试吧. 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:


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


Gangavarapu, 光, Babji, V, Meißner, 吨, 他, 答:, & 好, 乙. (2016). 科: an interactive, web-based tool for testing hypotheses and developing predictive models 生物信息学 分类号: 10.1093/bioinformatics/btw117


一周的视频提示: RGD的OLGA工具, 对象列表发生器和分析仪

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 含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 (或全部) of a variety of querying options. The new tutorial video will walk you through the process of querying the RGD database using OLGA, 包括

  • 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, 例如, 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.


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

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


Shimoyama, 米, De Pons, j的, Hayman, 克, Laulederkind, 学, 刘, 瓦特, Nigam, 河, Petri, V, 史密斯, j的, Tutaj, 米, 王, 学, Worthey, 大肠杆菌, Dwinell, 米, & Jacob, ħ. (2014). The Rat Genome Database 2015: 基因组, phenotypic and environmental variations and disease 核酸研究, 43 (D1) 分类号: 10.1093/nar/gku1026


一周的视频提示: 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, 等. 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. 该 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


该 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. 你应该看看.


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

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


Cressey, ð. (2016). Web tool aims to reduce flaws in animal studies 自然, 531 (7592), 128-128 分类号: 10.1038/531128一

UCSC Genome Browser new feature

一周的视频提示: Multi-region visualization in the UCSC Genome Browser

本周的视频尖在演示了一个新功能 UCSC基因组浏览器. I think it’s kind of unusual, 和概念上我花了一些时间来习惯,当我开始测试它. 所以,我想去过的基础知识为你, 给你一对夫妇的事情的提示,我不得不为神交我习惯了这种新的可视化选项.

标题的新闻项目 将其描述为: “结合基因组浏览器的多个区域为单幅可视化!” 和

你是否曾经希望能够从基因组浏览器显示删除所有内含子或间隔区的? 你是否曾经梦想能够可视化基因组的多遥远的地区? 嗯, 现在你可以用新 “多区域” 在基因组浏览器选项!

我也许应该开始与困惑我的第一件事情–名字 “多区域”. 我以为我将能够看到一个地区的染色体上的部分可能 1, 和一些染色体 8, 也许在同一时间. 但是,这不是如何运作. 在这种情况下, 你看多个区域沿同一染色体, 与某些间插序列的剪断出. 这将创建一个排序 “虚拟染色体” 为你互动.

在本周的视频, 我会告诉你如何看起来使用的BRCA1基因. 首先,我告诉你如何可以在所有外显子一起看–与内含子剪辑了. 然后,我告诉你如何看基因一起显示附近, 与非编码区裁剪出. 这些都是 2 用于查看单独的选项.

我用的是 “查看” 菜单选项来说明这个功能. 但还有另一种方式来访问它–您可以使用 “多区域” 按钮,在浏览器按钮区域.


为了保持视频短片, 我没去到每一个细节上这个工​​具. 你应该检查出 消息公布吧, 并链接到的其他详细信息 用户指南文档 更多. 新的功能也在介绍最新NAR简要提到在UCSC基因组浏览器 (下面链接). 你应该尝试一下, 当然! 这是真正了解它如何帮助你想象,你可能会感兴趣的基因组区域的最佳方式.

也如在新闻, 由于开发团队. 我一直在寻找新的可视化, 而这个有趣的测试!

谢谢高尔特理发, 马修·斯皮尔, 而整个UCSC基因组浏览器的质量保证团队,为所有的努力在创造这些激动人心的新的显示模式.



UCSC基因组浏览器: genome.ucsc.edu

多区域新闻项目: http://genome.ucsc.edu/goldenPath/newsarch.html#030816

在UCSC基因组浏览器培训教材: http://openhelix.com/ucsc


带动, 米, 科, 答:, 罗森布鲁姆, 光, 雷尼, 二, 赞助商, 二, Nejad, 体育, 李, 二, 学习, 光, Karolchik, 四, Hinrichs先生, 答:, 海特纳, 学, 硬, 河, Haeussler, 米, Guruvadoo, 属, 藤田, 体育, Eisenhart, 三, Diekhans, 米, 克劳森, 阁下, 卡斯帕, j的, 理发, 克, 豪斯勒速度, 四, 库恩, 河, & 肯特, 在. (2016). UCSC基因组浏览器数据库: 2016 更新 核酸研究, 44 (D1) 分类号: 10.1093/nar/gkv1275

披露: UCSC Genome Browser tutorials are freely available because UCSC 赞助商 us to do training and outreach on the UCSC Genome Browser.

"Frictionless connection of bioinformatics tools"

一周的视频提示: GenomeSpace方向,en

"Frictionless connection of bioinformatics tools"

“Frictionless connection of bioinformatics tools”

The GenomeSpace site has been highlighted before in our “一周提示”. 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, 可视化, and workflow tools. This intro video focuses on a couple of them, 基因表达模式 for demonstrating workflow, 和 Cytoscape for visualization. But you can see how the others would help support many types of genomics analyses.

This overview talks about their “recipes” 概念, with step-by-step analysis protocols, which can be found here: 銈://recipes.genomespace.org . And there’s a demo of the recipe resource. 也有一些 “官方” 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.

另外还有一 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:


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


这, 光, Garamszegi, 学, 吴, 楼, Thorvaldsdottir, 阁下, Liefeld, 吨, Ocana, 米, Borges-Rivera, 四, Pochet, 全, 罗宾逊, j的, Demchak, 二, 船壳, 吨, Ben-Artzi, 克, 白山, 四, 理发, 克, 李, 二, 库恩, 河, Nekrutenko, 答:, 西格尔, 大肠杆菌, IDEK, 吨, Reich, 米, Regev, 答:, 张, 阁下, & Mesirov, Ĵ. (2016). Integrative genomic analysis by interoperation of bioinformatics tools in GenomeSpace 自然方法, 13 (3), 245-247 分类号: 10.1038/nmeth.3732


一周的视频提示: Introduction to Biocuration and the career path


The ISB is a professional organization for biocurators

在OpenHelix, 我们早就唱策展人的赞美. 我们中有些人已经策展人和策展和数据库开发团队工作. 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 (~6:30我的). A sample UniProt entry illustrates what kind of information is captured and where it appears. She also touches on their work with 基因本体论. And a bit about the ecosystem of curation, how teams at different resources help each other but don’t wish to duplicate work, 使用 HGNC nomenclature as an example.

About 8min, the skill sets for biocuration are covered: data basics, curation skills, programming and database concepts, 本体, and usability of the data collected. This also includes data access and management, as well as dissemination and outreach. This includes user training (YAY!) 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 (下面链接). 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, 使用 Center for Target Validation (CTTV) 作为一个例子. 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!


国际社会为Biocuration: http://biocuration.org/

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



Holliday, 克, Bairoch, 答:, Bagos, 体育, Chatonnet, 答:, Craik, 四, 芬兰人, 河, Henrissat, 二, 康特里曼, 四, 曼宁, 克, Nagano, 全, 多诺万, 三, 普鲁特, 光, Rawlings, 全, Saier, 米, Sowdhamini, 河, Spedding, 米, 斯里尼瓦桑, 全, Vriend, 克, 巴比特, 体育, & 贝特曼, 一. (2015). Key challenges for the creation and maintenance of specialist protein resources 蛋白质: 结构, Function, and Bioinformatics, 83 (6), 1005-1013 分类号: 10.1002/prot.24803

德特, 体育, 穆尼奥斯 - 托雷斯, 米, Robinson-Rechavi, 米, 阿特伍德, 吨, 贝特曼, 答:, Cherry<三pan>, J.,j的span class ="tr_" id="tr_吨" data-token="Q2hpc2hvbG0," data-source="">Chisholm, j的, Kania, 河, 多诺万, 三, & 山崎, ç. (2013). DATABASE, 生物资料库和打捞杂志, is now the official journal of the International Society for Biocuration 数据库, 2013 分类号: 10.1093/database/bat077