标记档案: RNA - Seq的

UCSC Genome Bioinformatics

一周的视频提示: UCSC基因组外显子的浏览器仅模式,en

球队在 UCSC基因组浏览器 continues to update their resources and offer new ways to find and visualize features of interest to researchers. One of the newer features is the “多区域” option. When it was first launched, I did a tip on how to use that, with some of the things that I noticed while I was testing it pre-launch. But now the folks at UCSC have their own video on the exon-only display that you might also find useful.

One of the things that is illustrated here is how the exon-only mode is handy to enhance your exploration of RNA-Seq data. It also uses a great 进行编码 data set as an example, and if you haven’t been using that collection it’s a good reminder of the kinds of things you can find in that resource still. And this extensive data set shows how much easier it is to look at different isoforms in the data in this new exon-only mode.

So have a look at this display option if you haven’t before, especially how it can help you to see transcript differences. 如果你不熟悉的 编码资料 that’s being used, you can also see our training on that which will help you to understand how to use that data and the filtering features that are also used in this video.

特别说明: I have updated the UCSC Intro slides to include the new Gateway strategies as well. So download those slides for the latest look.


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.


UCSC基因组浏览器: http://genome.ucsc.edu

UCSC Genome Browser training materials: http://openhelix.com/ucsc

进行编码: 銈://www.openhelix.com/ENCODE2


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

ENCODE项目联盟 (2012). 一个集成的人类基因组中的DNA元件的百科全书 自然, 489 (7414), 57-74 分类号: 10.1038/nature11247

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


This week’s SNPpets include RNAMiner for mining RNA-seq data and MarkerMiner for angiosperms, who qualifies to be a bioinformatician, how to attract women to scitech careers, dangers of default parameters, 和 10 simple rules to win a Nobel Prize, 多….

欢迎来到我们的链接集合星期五功能: SNPpets. 一周之内,我们遇到了很多链接和读取,我们认为很有趣, 但不要到一个博客帖子. 在这里,他们是您的享受…


对传染性疾病的基因组学的影响 (与视频)

作为的一部分 基因组学在医学系列讲座 从NHGRI的, 乔纳森Zenilman了资助新的基因组技术已在临床诊断的方法,对各种方式的讲座,, 管理, 和治疗传染性疾病. 这个系列讲座,提供临床医疗的情况和基因组学的各个路口的视频, 不只是在该领域的基础研究.

以前阴暗的微生物状况的机会学习–包括不可培养的生物体, 和混合菌落的微生物可以侵入伤口, 真的很有趣 (但对社会的思考在脑脓肿…是…午餐时间观看可能不适合). 但不仅这些情况几乎是不可能的前理解–但他们不知道在那里是否有抗药性的. 因此,它严重影响治疗.

一个有趣的数据点: 脑脓肿, 使用标准的养殖技术, 他们发现 22 错误. 利用PCR技术,他们发现 72! 有些不明, 太. 一位病人 16 株. 这是相当的战斗.

这些新战略的一个副作用,但它是怪胎了医院管理者. 突然,因为测试的敏感性增加, 有在其报告中反映了很多更多的生物体.

新技术真的要帮助在治疗慢性伤口. 其中重要的一点是,检查的RNA-seq的数据是关键, 因为重要的是要知道哪些转录活跃. 死虫子复杂分析, 所以知道哪些是目前活着的和影响伤口是关键.

这项工作的另一个重要成果会得到更多的病原体导向治疗的指针. 广谱治疗造成自己的问题, 并有更精确的方式,针对坏的错误真的是值得的.

我已经附加的各种数据的样本,Zenilman和同事们公布工种,他介绍,在此讲学, 但你可以找到更多的例子. 我想选择一个开放访问的例子,虽然, 所以这是我包括.


价格, 属, 刘, 三, 梅伦德斯, j的, 弗兰克尔, 华, engelthaler, 四, 阿齐兹, 米, 鲍尔斯, j的, 拉特雷, 河, 使纠缠, j的, 金斯利, 三, 细菌, 体育, 拉撒路, 克, & zenilman, Ĵ. (2009). 社区分析慢性创面细菌的16S rRNA基因为基础的焦磷酸测序: 糖尿病和抗生素对慢性创面菌群的影响 科学公共图书馆一, 4 (7) 分类号: 10.1371/journal.pone.0006462

ENCODE RNA - Seq的数据标准–我们会需要他们

我刚刚从重要的电子邮件 ENCODE公告邮件列表 在UCSC基因组浏览器. 我还没来得及通过他们去,因为我包装旅行, 但我认为PDF文件将作一些精细飞机阅读!

的编码协会已完成“的标准, 准则和RNA - Seq的V1.0最佳实践′, 作为财团的持续努力生成数据标准的一部分. 该文档可在 ENCODE门户 这里:


“RNA - Seq是定向的实验方法在生物样品中转录特征,旨在. 该文件提出了一套专注于最佳实践的准则和标准,为创建'参考质量’ 转录测量。”

其次是直接链接到RNA - Seq的PDF文档: http://encodeproject.org/ENCODE/protocols/dataStandards/ENCODE_RNAseq_Standards_V1.0.pdf

我认为这将是有趣的阅读本, 我只是考虑RNA - Seq的数据有一天,当斯蒂芬特纳开始讨论一些热点新闻:

@ genetics_blog 希望我能读 ($UB) MT @ GenomeWeb: 转录丰度大幅不服btwn RNA - SEQ expts W /同一平台 http://bit.ly/kfShZH
@ OpenHelix: @ genetics_blog指本文件 http://bit.ly/l9akCF
该文件关于从同一样品RNA - Seq的数据编制的技术变化是完全相同的方式. 我认为这将是非常重要的是在这个数据变化所知,我们探讨它. 而且我敢肯定,ENCODE联合体乡亲都会有这个文件看看,并认为信息.

关于事实的ENCODE联合体正在开发的标准伟大的事情之一是,有这些伟大的大数据集,是提供给大家看看, 并有与被控人评价技术和方法来获得他们最出.

如果你不与ENCODE项目和数据集熟悉, 请有在ENCODE教程资料,我们已经看, 这是免费的,因为它们是由UCSC ENCODE团队赞助. 我们显示有关项目的框架内你, 如何识别那边数据, 并与它进行交互的一些重要方面.