标记档案: 癌症

SNPpets_2

星期五SNPpets

This week we’ve got DNA in the gig economy and for sports fans (?), new software resources for virus and lipids, a handy collection of cancer genomics papers, microbiomes, biosecurity, 羊, pearl millet, and de-extinction. My favorite read this week, 虽然, was the mosquito and gene drive review paper. I am so on board to gene-drive those things….


SNPpets_2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…


And why I want gene drive mosquitoes:

SNPpets_2

星期五SNPpets

This week I left the “call to action” tweet at the top–you could vote for GenBank every day until the end of the competition. Today’s the last day. Vote once more. And remember how resources like GenBank let us have foundations for other important resources as well. There are new resources in this week’s SNPpets that are definitely descendants of GenBank. 还, more tit-for-tat on the Venter paper privacy issue drama. A very personal genome story from a personal genome researcher. Makes you think about the policy issues in new ways. 还, policy on gene editing in plants. The first species to have every members’ 基因组数据. Gene counts via RNAseq in a new resource, and a new cancer gene repository.


SNPpets_2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…


星期五SNPpets

This week was a busy and diverse week. From Bioinformatics as an Amusement Park, to the very serious FDA approval of 23andMe’s reports. From Lenski’s April Fool’s post to cancer databases. From diagnosing children with genomics details, to CRISPR sci-fi. We live in interesting times. But at least now we will be able to see everybody citing everybody else.


SNPpets_2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…


SNPpets_2

星期五SNPpets

This week’s SNPpets offer a rather eclectic collection. Visualization with PanViz, LepBase for lepidopterans, a simulated data generator, and a new collection of community-curated phylogenetic estimates. But the big noise was the cancer “moonshot” data commons and the clinical trial for NCI-MATCH and precision medicine. Also newborn genome sequencing. Funniest thing: passive-aggresive bioinformatics. Coolest thing: Paul Simon and CRISPR (scroll to the bottom of the list).


SNPpets_2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…


one query type from: genomeportal.stanford.edu/pan-tcga/

提示的周: 癌症基因组图谱的临床资源管理器

访问 TCGA cancer data has been approached in a variety of ways. This week’s tip of the week highlights a web-based portal for improved access to the data in different ways. The Stanford Cancer Genome Atlas Clinical Explorer is aimed at helping identify clinically relevant genes in the cancer data sets.

They note that the data is available in other places and tools, from tools we’ve talked about before such as cBioPortal, 肿瘤基因组学UCSC的浏览器, and interacting with the StratomeX 功能. But this portal helps peoplt to quickly focus on clinical parameters in ways that aren’t as straightforward in the other tools.

You can learn more about the project on their site from their Overview 在现场, and you can see their publication about it (下面). The paper also covers some issues they had with the downloaded data that might be worth noting. And they also supplemented their analysis and information with 宇宙目标 (umor lterations relevant for GEnomics-driven herapy) data as well.

one query type from: genomeportal.stanford.edu/pan-tcga/

One query type from: genomeportal.stanford.edu/pan-tcga/

The interface offers several quick ways to dive into the data.

有 3 main query types: genes associated with certain clinical parameters; query directly by gene/protein/miR; and a two-hit hypothesis test. The first query is the image I’ve shown here. When you get to the the results, you can explore them in more detail with sortable tabular outputs, and on gene pages tabs for copy number changes, 突变, and RNA-seq values.

They give you some “example queries” that you can use as a way to get started and see what’s available underneath. And although we usually like to highlight a video, the tutorial that they provide is a slide embed.

So have a look at this interface if you’d like to explore TCGA data with a handy and quick query strategy. It might offer some hunting license on genes you are interested in, or some ideas for other investigations in tumor types you study.

 

快速链接:

Stanford-TCGA-CE: http://genomeportal.stanford.edu/pan-tcga

参考:
李, 阁下, Palm, j的, Grimes, 学, & 这, ħ. (2015). 癌症基因组图谱的临床资源管理器: a web and mobile interface for identifying clinical–genomic driver associations 基因组医学, 7 (1) 分类号: 10.1186/s13073-015-0226-3

Comparison of cancer genomics tools, via: Swiss Med Wkly. 2015;145:w14183

一周的视频提示: UCSC Xena System for functional and cancer genomics

When we go out and do workshops, we get a lot of requests from researchers who would like some guidance on cancer genomics tools. Our particular mission has been to aim more broadly at tools that are of wide interest and not to focus on a particular disease or condition area. But certainly the cancer genomics arena is going to be one of the ones that’s got so much opportunity for great bioinformatics-based outcomes in the near term. So I keep an eye out for tools researchers may want to explore.

当 “基因组学” twitter column in my Tweetdeck dropped this new mini-review of cancer genomics tools on my desktop, I went to look right away: Data mining The Cancer Genome Atlas in the era of precision cancer medicine. TCGA is the focus of the data source they are talking about, but the tools included may have more data sets and wider utility, 当然. Most of the tools described were familiar to me (cBioPortal, GDAC Firehose, 肿瘤基因组学UCSC的浏览器, canEvolve), but a couple of them were new. I had never explored the ProGeneV2 tools before. 和 UZH Cancer Browser was also new to me.

Comparison of cancer genomics tools, via: Swiss Med Wkly. 2015;145:w14183

Comparison of cancer genomics tools, 通过: Swiss Med Wkly. 2015;145:w14183

One thing that’s very helpful to me is the kind of table they provided as Table 2. It’s a comparison of the main tools they are discussing, with different features of each compared. That’s handy for choosing the tool to spend time on, depending on your own research needs.

But they also referred to another tool that was new to me, Xena. “The UCSC cancer browser will be updated in the future, with the new Xena platform for visualisation and integration with Galaxy“. I can never resist new genomics visualization tools, and as a giant fan of 星系, I certainly need to know more about this.

So I went to look around for some information on it, and their introductory video is this week’s Tip of the Week.

So Xena is designed to let you combine your own data with large public resource collection data, without leaving your firewall or without being too onerous to pull down all the public data and manage it locally. You can explore functional genomics data and related phenotype and clinical data. It uses the “集线器” strategy that is becoming increasingly adopted as a way to integrate across data collections. We were just talking about hubs in another recent tip if these are new to you. It supports a wide range of data types to examine and visualize. If you want to go deeper, there’s a lot more information over at the Xena homepage. They have documentation, presentation slides, and a step-by-step demo available from a recent workshop.

Certainly one of the key features appears to be that you can integrate your own research data–which might be subject to strict privacy regulations–on your own computer with all the other key information from public data providers. Increasingly researchers I talk to at workshops need this aspect very much.

So try out Xena, and explore the other tools in the cancer genomics space, to see what’s right for your research.


Hat tip to Oscar:

And you can follow Xena on twitter for news and updates: https://twitter.com/UCSCXena

快速链接:

Xena: http://xena.ucsc.edu/

参考文献:

克莱因, 米, Craft, 二, Swatloski, 吨, 高盛, 米, 但, 学, 豪斯勒速度, 四, & 朱, Ĵ. (2013). Exploring TCGA Pan-Cancer Data at the UCSC Cancer Genomics Browser 科学报告, 3 分类号: 10.1038/srep02652

Cheng PF, Dummer R, & Levesque MP (2015). Data mining The Cancer Genome Atlas in the era of precision cancer medicine. Swiss Med Wkly. (145) : 10.4414/smw.2015.14183

Biostars

答案是什么? (cancer data visualization tools)

This week’s highlighted question was less of a question than a notice about a new tool. And because I’m always interested in exploring new visualization tools, I was interested to have a look. 此外, we are frequently asked about tools specific for cancer genomics, and I like to be able to tell people about what I’ve found.


Biostars 网站是一个要求, 生物信息学的问题和问题的回答和讨论. 我们的成员 Biostars_logo 社区和发现它非常有用. 常的问题和答案出现在Biostars是有密切关系的读者 (基因组学的最终用户资源). Thursdays we will be highlighting one of those items or discussions here in this thread. 您可以询问一下该线程问题, 或者你可以随时参加在Biostars.


So here’s this week’s highlighted question (好, the question mark was edited out later, but this is what it said when I grabbed it):

工具: A new tool for cancer researcher developed by UCSC?

Another tool XENA that comes to the world of Bioinforamatics designed by UCSC. I am not sure if people are aware of it. It is developed recently and I got the notification last evening. Seems to be having a lot of potential for both data visualization and also producing quality images for publication. The paper is not yet out but the mention of the tool was done last year when the update paper of the UCSC Cancer Genomics browser was made for 2015.

The tool has got data from both TCGA and ICGC and is a powerful resource not only for public data comparing and viewing but also one can upload its own data or download the tool locally to a desktop app version and visualize it. The tool is available at the below link

http://xena.ucsc.edu/

The technical doc is 这里 . Am sure it will be a great resource for the researchers and bioinformaticians across the globe. For analysis it also integrates the galaxy as well and if you format your data in a version as mentioned in help docs one can view their data as well. Enjoy and appreciate the work. Hope people would like it.

P.S:

I am not involved in the work. I liked the tool a lot so thought of informing it to the community

vchris_ngs

It also generated a bit of discussion about the challenges of developing visualizations. 去看看.

一周的视频提示: 宇宙, 体细胞突变目录癌症

当我们在车间医疗中心, 我得到的最常见的问题之一是关于定位很好的资源用于癌症数据. 我们已经谈到了一些 大项目, 像ICGC. 我们已经谈到的方法 分层数据集, 与这样的一个例子是在癌症, 利用数据 癌症基因组图谱. 展望未来, 能力迅速序列正常VS肿瘤对应该帮助我们更快速地了解和目标肿瘤. 这将导致在某些情况下完全新的线索其他情况.

但是,我想一定要突出人的真正坚实的工具之一是 宇宙 集. 这不是新–它已经存在了十多年,现在. 但它的这些类型的核心数据资源,人们真正需要了解的一个. 他们的长期经验, 其高品质的策展, 并使其适用于数据量和数据类型的新涌入, 使他们的信息的真正有价值的来源.

阅读他们的更新文件中 2015 全国房地产经纪人协会数据库问题, 我想去过,并刷新我的,我知道的记忆功能, 并探讨一些新的功能太. 真的有一些严重的深度那边, 我不能触及的所有方面在这样的博客文章,他们有. 但我也发现,他们最近已经提供了大量的视频,帮助人们了解各种工具和选项.

对于本周本周视频提示, 我会包括自己 “概观” 片. 但你应该看看 他们的教程页面 额外的主题,以及.

我没有意识到的一个特点是,他们提供的是使用一个基因组浏览器 JBrowse 框架. 有关于如何使用一些指导一个独立的视频.

他们在论文的未来发展方向部分很清楚,他们正准备能够处理关于这一主题的输入数据. 和它们的评估新的工具和分析,可能是适当的. 但他们承诺保持其高度重视策展–这是音乐给我的耳朵. 我觉得质量手策展是由最终用户同时低估 (而可悲的是由资助者), 而被完全至关重要处理所有的到来的大数据. 所以,熟悉COSMIC癌症基因组数据. 这将是值得你的时间.

快速链接:

宇宙: http://cancer.sanger.ac.uk/

参考:

福布斯S.A。, ð. BEARE, P. Gunasekaran, ç. 梁, ñ. 宾达尔, ħ. Boutselakis, M. 的事, S. 班福德, ç. 油菜, S. 沃德 & ç. Ÿ. 毽 & (2014). 宇宙: 探索的体细胞突变在人类癌症世界知识, 核酸研究, 43 (D1) D805-D811. 分类号: http://dx.doi.org/10.1093/nar/gku1075

答案是什么? (癌症的数据差异)

映泰 网站是一个要求, 生物信息学的问题和问题的回答和讨论. 我们的成员 社区和发现它非常有用. 经常出现的问题和答案在映泰是我们的读者有密切关系 (基因组学的最终用户资源). 逢星期四,我们将突出这些项目或讨论,在这个线程. 您可以询问一下该线程问题, 或者你可以随时参加在映泰.

这周的问题凸显了癌症数据问题, 因为我做了癌症数据库 “提示的周” 得到一些像样的兴趣, 我想我会继续的主题. 一些研究人员是谁比较熟悉的癌症数据集可能会有一些见解.

问题: CBIO门户VS. Oncomine: 样品和表达数据的差异

我一直在寻找通过两个基因的表达水平参与浆液性卵巢癌数据TCGA CBIO门户. 基于一个Z分数门槛 2.0, 我发现了以下的样本百分比 (例) 表达水平的影响 (向上或向下)

案例集: 与mRNA的数据的肿瘤 (安捷伦芯片): 所有样品的基因表达数据 (489 样本) CCNE1 – 11% CDK12 – 7%

对于相同的数据集 Oncomine (我使用的是免费版本):

TCGA卵巢 (517 样本 <- 此电话号码高于报道 TCGA卵巢癌出版) 表达数据被提供作为日志-2的强度中位数和在Oncomine表明的CCNE1 CDK12的表达水平较高的表达水平与不同档次 – 例如级 3 瘤 (级 3 (431 样本) 这两个基因具有更高的表达水平.

我也注意到,该数据集 517 样品被分配为没有关联的纸 2011/03/24. 我想知道,如果数据是定做本文TCGA卵巢癌数据集. http://www.nature.com/nature/journal/v474/n7353/full/nature10166.html

我很奇怪,为什么这样的差异,我在这里失去了一些东西.

聚苯乙烯. 我已经张贴了这个问题Oncomine CBIO列表, 但没有收到任何回应. 我想知道是否有人在这里有经验的平台上,可以提供洞察.

另一个问题我感兴趣的是支持的问题. 这是一个熟练的超级用户,努力把事情做对–网站的支持团队联系, 并没有得到响应. 我认为这是这个舞台上最令人沮丧的事情之一. 有些项目以及资源的用户支持. 有些是没有. 但是,如果智能用户无法弄清楚发生了什么事情与你的网站的数据, 你的资源是不是有用,因为你认为它是. 我希望支持更有价值. 但是,如果你知道什么是–走了过来,并提供答案.

一周的视频提示: 我的癌症基因组

computer_doc那里有很多的癌症数据库资源. 我们大多数的重点一直是数据存储库类型. TCGA, ICGC, CaBIG, 宇宙, 癌症基因组工作台, 加州大学圣克鲁兹分校癌症基因组浏览器, 当然大仓库一样 全球环境展望. 研究人员将需要这些数据源的定位关键在癌症细胞和组织的改变, 和处理条件的变化进行评估. 但这些可能不是最有用的地方,医生面临着一个特定的样本, 或患者试图了解他们的情况. 随着越来越多的肿瘤采样数据变得可用, 直接和具体的可操作条信息的访问将是至关重要的.

的MyCancerGenome网站的目的是服务,可操作的数据频谱年底. 它已经发展了一段时间, 但最近在纽约时报的故事提醒我: 在基因上的变化, 和工具,以查找他们. 因此,本周的视频提示的周, 我给你看看我的癌症基因组资源. 他们有一个不错的开场视频,我这里将包括. 它突出的特点,我不会一直能够访问–链接部分病人的病历 + 突变 + 策划详细页面的突变及相关研究. 公众有机会到最后一部分, 但是你将无法看到的电子健康记录部分来自公共端.

论文来描述信息的沉积到MyCancerGenome网站. 编目的体细胞突变,在最近的一篇文章中的临床相关的直接,你可以学到更多的理念和策略 (ðNA mutation NVENTORY řefine和 énhance ç相关关系 T施工日期:) 项目. 一个选项卡,在该网站上显示您与该初始数据, 非小细胞肺癌 (非小细胞肺癌) 在表皮生长因子受体的基因突变 (EFGR). 随着越来越多的这个数据走来,我们会看到它的成长, 当然. 似乎是一个很好的一步转化医学. 所以,看看在特定癌症相关的变化,他们正在收集有用的,以证据为基础的信息.

另一个特点是一个搜索选项,找到临床试验–因疾病或通过基因. 我不认为这种信息之前,我见过一个特定基因的搜索. 这可能是有用的人谁需要访问新的治疗方案,如果他们有自己的肿瘤的特定基因突变有关数据.

看看我的癌症基因组, 想想我们要去的地方与此数据. 我希望新的癌症基因组数据将真正帮助驱动适当和有效的治疗策略.

快速链接:

我的癌症基因组网站: http://www.mycancergenome.org/

纽约时报“的文章: 在基因上的变化, 和工具,以查找他们

参考文献:

斯旺顿, ç. (2012). 我的癌症基因组: 一个统一的基因组学和临床试验门户 “柳叶刀肿瘤学, 13 (7), 668-669 分类号: 10.1016/S1470-2045(12)70312-1

叶, 体育, 陈, 阁下, AndrewsGrossman, j的, 纳塞尔, 河, 报, 瓦特, & 号角, L. (2013). DNA突变库存,完善和加强癌症治疗 (DIRECT): A产品目录启用基因组导演的抗癌疗法的临床相关癌症突变 临床癌症研究, 19 (7), 1894-1901 分类号: 10.1158/1078-0432.CCR-12-1894

我的癌症基因组. 2013. http://www.mycancergenome.org (访问 4/30/2013).