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….
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.
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.
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).
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 和 目标 (吨umor 一lterations relevant for GEnomics-driven 吨herapy) data as well.
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.
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, 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.
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 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):
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
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.
I am not involved in the work. I liked the tool a lot so thought of informing it to the community
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