Tag Archives: network visualization software

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Friday SNPpets

This week’s SNPpets include stories about how DNA gets into the databases, network visualizations, historical genome browsers, CVS doing personalized medicine, fungal genome resource issues, mouse, dog, plant and human data stories, free data, and more.


Welcome to our Friday feature link collection: SNPpets. During the week we come across a lot of links and reads that we think are interesting, but don’t make it to a blog post. Here they are for your enjoyment…


https://twitter.com/timknut/status/628194770649804800

https://twitter.com/howplantswork/status/628198284658475008

Video Tip of the Week: yEd Graph Editor for visualizing pathways and networks

This week’s video tip of the week closes out a series that began last month. I started to explore one gene co-expression tool, which led me to another tool for visualization, and so on. This week’s tool is the final piece that you need to know about if you want to create the kind of interaction/network diagrams used in the modeling of a system that I covered last week.

The yEd Graph Editor is different than some of the tools. As a corporate product, it doesn’t have

yFiles layouts options in Cytoscape

yFiles layouts options in Cytoscape

the kind of scientific paper trail that some academic tools will. But if you search Google Scholar for “yED Graph Editor” you’ll see people from a wide range of disciplines have used it for their research projects. I first learned about yEd when I was using Cytoscape, and saw that some of the choices for layouts were based on the yEd features. This short overview video from the yWorks folks will explain what some of those layout styles are.

As you can see in this video, the use of yEd is not only for biological interactions–it can do a whole lot of graphing that is entirely unrelated to biology. But the features work for biological networks, and you can customize the graphics to represent your own topic of interest.

There are longer videos with more detail on the use cases for yEd. This one uses a sample flow chart to illustrate the basic editing features. It quickly covers many helpful aspects of establishing and editing a visualization.

You can also find videos from folks who use yEd for their projects on YouTube, some of which might be more specific for a given field of research. But these should give you the basics of why yEd can be used for the types of projects that you saw in the previous tips with Virtually Immune and BioLayoutExpress3D. And like I noted with Virtually Immune, you can get your hands on the files in the Pathway Models collection, and launch a yEd file to go into the features with a detailed example. The complexity you can generate with these models is astonishing.

There was no reference specifically for yEd that I was able to locate, but you can find that lots of people use yEd graph editor on a wide range of research topics in Google Scholar. So if you are looking to see if someone in your research area has used yEd, you may find some examples. If you are going to consider exploring the BioLayout and Virtually Immune tools, it will help to understand the framework. And also as I mentioned in Cytoscape–understanding yEd helped me to grasp the layout options there too. So try out yEd for pathway and network visualization if you have needs for those types of representations in your research and presentations. It’s free to download and use.

Quick links:

yED Graph Editor: http://www.yworks.com/yed

yEd Graph Editor Manual: http://yed.yworks.com/support/manual/index.html

References:

Wright D.W., Tim Angus, Anton J. Enright & Tom C. Freeman (2014). Visualisation of BioPAX Networks using BioLayout Express3D, F1000Research, DOI: http://dx.doi.org/10.12688/f1000research.5499.1

Smoot M.E., K. Ono, J. Ruscheinski, P.-L. Wang & T. Ideker (2010). Cytoscape 2.8: new features for data integration and network visualization, Bioinformatics, 27 (3) 431-432. DOI: http://dx.doi.org/10.1093/bioinformatics/btq675

Video Tip of the Week: Integrative Multi-species Prediction (IMP) Network Analysis Resource

A while back Mary saw the following tweet go by & collected it as a possible topic for one of our weekly tips:

RT @moorejh: #bioinformatics #genomics RT @GreeneScientist Interactive and video tutorials for IMP are available from: http://t.co/zvlVmoph

This week I have claimed Mary’s “collected” tip idea & will be featuring one of their videos as this week’s quick tip.

The Integrative Multi-species Prediction (IMP) web server is a gene-gene network analysis resource. There are several such resources (Cytoscape, IntAct, MINT, STRING, VisANT, and one of my personal favorites GeneMania) that OpenHelix has tutorials on (see our Pathway catalog listing). The IMP developers provide a nice amount of help for their users – not only do they have multiple YouTube videos (as do we on the OpenHelix YouTube channel), but they also offer two interactive tutorials that allow users to be guided through an example usage of IMP.

For today’s tip I am featuring the third YouTube video that they list on their tutorial page, because I thought it had the best sound and image quality. The other videos are also informative & are worth a viewing – enjoy!

Reference:
Wong AK, Park CY, Greene CS, Bongo LA, Guan Y, & Troyanskaya OG (2012). IMP: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks. Nucleic Acids Research, 40 DOI: 10.1093/nar/gks458

How do you represent genomes?

cnv_1Not just the genome, but genomeS. As Jan at Saaien Tist has mentioned, human (and other species) genomes are quiet variable. Though the linear representation of genome browsers makes perfect sense (like the UCSC Genome Browser, Ensembl, GBrowse and MapViewer among others) for much annotated data of the genome, structural variations are not so well visualized in a linear representation. And, as we are find the human and other specie genomes are quite variable, we might need to come up with another way to visualize these genomic data beyond the ‘reference genome’ linear model. Jan suggests deBruijn graphs,
pictured here. I find some difficulty in ‘visualizing’ how these are going to work for the _other_ annotations in the data. Though this representation looks like it might work great for CNV and the like, it seems to make viewing other types of data (expression, SNP, etc) more complicated. I’m looking forward to see how this develops.

Or perhaps we’ll be looking at genomes like this (ok, maybe not, but it’s geeky cool).

Tip of the Week: BioData Mining OpenAccess Journal

BioData Mining Tip First off, Happy New Year!

For the first tip of 2009, I am going to feature a ‘where to’ tip, as opposed to our normal ‘how to’ style tip. In our persuit of great tutorials to train on, we are hunting for and learning various data mining tools and software. My where to tip is that I recently learned where to find a lot of great info in this subject, namely from the journal BioData Mining. This journal is open access, and from BioMed Central. In my tip, I talk about the journal, and feature an article (also cited below) that I found really interesting, as well as some other journal-related tidbits. Enjoy!

 PS: Trey blogged about this journal here in Nov. but hey, you can never get enough of a good thing, right?

-Georgios A. Pavlopoulos, Anna-Lynn Wegener, Reinhard Schneider (2008). A survey of visualization tools for biological network analysis. BioData Mining, 1 (1) DOI: 10.1186/1756-0381-1-12