Category Archives: Tip of the Week

Video Tip of the Week: Helium plant pedigree software, because “Plants are weird.”

A lot of people find our blog by searching for “pedigree” tools. We’ve covered them in the past, and we’ve got some training on the Madeline 2.0 web tools that we like. We have groused about the fact that some pedigree tools do not accommodate same-sex families. Largely focused on human relationships, there are a variety of options.

Another branch of this type of software is animal colony management software. This can be used to track animals in breeding situations. We’ve highlighted The Jackson Lab’s Mouse Colony Management Software, and we see a lot people going over to take a look. But there are other types of breeding software out there too.

Plant pedigrees are a special challenge, though. Although I did begin to look into that software at one point, I hadn’t looked again for a while. So when I saw the announcement about an upcoming talk at the  Bio-IT World conference, I thought it was time to look again. Helium was new to me, and I admit I laughed out loud at my first introduction to it:

BioVis 2013: Poster: Evaluation of Helium: Visualization of Large Scale Plant Pedigrees from VGTCommunity on Vimeo.

“Ok, so, plants are weird….” Best poster intro I’ve heard.

But really, the potential complexity of plant breeding pedigrees is much more daunting than even tricky human pedigrees. Their paper on the Helium efforts (linked below) describes some of those aspects in more detail:

Firstly, the named entities in plant pedigrees may, but not always, represent a population of genetically identical individuals, not a single plant. While it is relatively simple to grow many plants from seed, potentially many decades after production, in humans and animals this is understandably not the norm. The generation of these genetically identical (homozygous) varieties is possible through doubled haploidy, inbreeding, or crossing of pairs of inbred lines to achieve what is termed an F1 hybrid. Successive inbreeding by self-pollination of these F1 generation plants leads to individual plants that are close to homozygous across all alleles.

There are no standards for plant pedigrees yet, I learned from this paper. Zoiks! Well, I guess that gives them free rein to design something that users want. The folks on the Helium project got a bunch of potential users, asked them what they needed, what worked, what didn’t work, and they are building a nice looking tool with the specs they got. Their paper goes on to describe their paper prototyping, the feedback, and other interactions they got further downstream in the process. It’s a nice example of how to get some direction from the likely end users.

Another video offers a bit longer view on their software, but there’s no audio (below). The most detailed video is the one attached to their paper in the supplemental files, but I can’t embed it. Go over there to download and watch that, with captions about what’s happening.

I wasn’t able to find any downloadable software yet to kick the tires myself. And because of the blizzard I’m worried I won’t have power for the next few days to check it out. But from what I can see and read in the paper, it looks promising and I’m eager to try it out at some point. Looking forward to Jessie Kennedy’s talk.

Quick link:

Helium project page: http://ics.hutton.ac.uk/helium/

Best intro video version, with explanation captions: http://www.biomedcentral.com/1471-2105/15/259/additional

This is the item that caught my eye, via email. I’m going to be at Bio-IT World, so I’m hoping to be able to see this presented live.

Dr. Jessie Kennedy to Deliver Keynote Presentation on Visualization Tools Designed for Biologists at 2015 Bio-IT World Conference, as part of the Data Visualization and Exploration Tools Track.

Jessie KennedyKeynote Presentation: Pedigree Visualization in Genomics
Jessie Kennedy, Ph.D., Professor & Director, Institute for Informatics and Digital Innovation, Edinburgh Napier University Most visualizations that display pedigree structure for genetic research have been designed to deal with human family trees. Animal and plant breeders study the inheritance of genetic markers in pedigrees to identify regions of the genome that contain genes controlling traits of economic benefit and, ultimately, to improve the quality of animal and plant breeding programs. However, due to the size and nature of plant and animal pedigree structures, human pedigree visualizations tools are unsuitable for use in studying animal and plant genotype data. We discuss two visualization tools, VIPER (designed for cleaning genotyping errors in animal pedigree genotype datasets) and Helium (designed to visualize the transmission of alleles encoding traits and characteristics of agricultural importance in a plant pedigree-based framework), and show how they support the work of biologists.

Early Registration Rates Available Now!
Register by January 30 to Save up to $400

 

Reference:

Shaw P.D., Martin Graham, Jessie Kennedy, Iain Milne & David F Marshall (2014). Helium: visualization of large scale plant pedigrees, BMC Bioinformatics, 15 (1) 259. DOI: http://dx.doi.org/10.1186/1471-2105-15-259

Note: OpenHelix is a part of Cambridge Healthtech Institute.

Video Tip of the Week: GWATCH, for flying over chromosomes

Ok, so it’s not *just* for flying over chromosomes. There’s more to it, of course. But that’s the part of GWATCH (Genome-Wide Association Tracks Chromosome Highway) that caught my attention. I’m always looking for different ideas and strategies to visualize data, and this was the first time I drove along the whole length of a human Chromosome 9 highway, seeing the various SNPs along the way.

A post on Google+ pointed me to the GWATCH paper and software, so hat tip to Taras Oleksyk. And I was pleased to see that they’ve done a video explaining their project and demonstrating the software, so that will be this week’s Tip of the Week.

It’s not the first time I’ve seen a 3D representation of SNPs. I remember seeing that from GeneSNPs in the past. But GeneSNPs visual option was a way to look at the features within a single gene–you could seen introns, exons, and choose to view SNPs by features like “non-synonymous”, and you could examine the frequency. It was an interesting way to combine a lot of data, but captured only one limited region. GWATCH goes much wider than that, letting you scan along whole chromosomes for patterns. That said–it would be very cool to have those features, and maybe a pointer to possible promoter regions, along the roadway as well. At first I didn’t notice the gene symbol track–er sidewalk?–along the edge of the view. But seemed to me you could add more sidewalk, a bike lane….Of course, then I want to add a domain bypass….Anyway–it’s got me thinking about ways to explore.

And I’ve focused on that unusual “moving browser” for this post, but there’s more to the tool that that. There are other ways to slice the data in 2D that can be helpful for your analyses. And it’s not limited to GWAS data either. But you can see more about that in both the video and it’s covered in their paper. So explore GWATCH more from their site, and you can load up their sample data and take it for a spin. You go to the site and click on the “Active Datasets” to see the ones they’ve provided. Open one, click on the “Highway Chromosome Browser” to select one. But you can also see the other types of tools they have from there.

Quick links:

GWATCH: http://gen-watch.org/ for taking it for a spin

Reference:

Svitin A., Sergey Malov, Nikolay Cherkasov, Paul Geerts, Mikhail Rotkevich, Pavel Dobrynin, Andrey Shevchenko, Li Guan, Jennifer Troyer, Sher Hendrickson & Holli Dilks & (2014). GWATCH: a web platform for automated gene association discovery analysis, GigaScience, 3 (1) 18. DOI: http://dx.doi.org/10.1186/2047-217x-3-18

Video Tip of the Week: Genome assemblers and #Docker

Last fall there was a tip I did on Docker, which was starting to pick up a lot of chatter around the genoscenti. It was starting to look like a good solution for some of the problems of reproducibility and re-use of software in genomics–containerize it. Box it up, hand it off. There’s certainly a lot of interest and appeal in the community, but there are still some issues to resolve with rolling out Docker everywhere. However, my impression is that the Docker team and community seems interested and active in evolving the tools to be as broadly useful as possible.

So when this tweet rolled through the #bioinformatics twitter column on my Tweetdeck, I was excited to see this talk by Michael Barton (who has the best twitter handle in the field: @bioinformatics). It’s a terrific example of how Docker can be aimed at some of the problems in the bioinformatics tool space. It’s not the only option, or course. Some workflow resources like Galaxy can cover other features of genomics researchers’ needs. But as a general solution to the problems of comparing software and distributing complete working containers, Docker seems to developing into a very useful strategy.

Here’s the video:

Although this is longer than our typical “tips”, I’d recommend that you carve out some time to watch if you are new to the idea of Docker. In case you don’t have time right now for the talk, here’s a summary. For the first 10 minutes, there’s a gentle introduction for non-genomics nerds about what sequencing is like right now. Then Michael describes how the assembler literature works–with completing claims about the “better” assembler as each new paper comes along. This includes a sample of the types of problems that assemblers are trying to tackle with different strategies.

Around 14min, we begin to look at what it’s like to be the researcher who needs to access some assembler software. Then he describes how different lab groups–like remote islands–can instantly ship their sequence data around today. But that biologists are like “longshoremen for data”: they have to unload, unpack, install, try to get all the right pieces together to make it work in a new lab. We are doing “break bulk” science right now. That was a really terrific assessment of the state of play, I thought.

If you are ok with the other pieces, you can skip to around 16min, where we get to know about a specific example of the benefits of Docker for this type of research. Michael goes on to describe how Docker has helped him to build a system to catalog and evaluate various assemblers. He developed the project called nucleotid.es (pronounced just as “nucleotides”),  which he goes on to describe. It offers details about various assemblers, which have been put into containers that are easy to access and to use to compare different software. There are examples of benchmarks, but you can also use these containers for your own assembly purposes. You can explore the site for more detail and a lot of data on the assembler comparisons that they have already. A good overview of the reasons to do this can also be found in the blog post over there:  Why use containers for scientific software?

At about 25min, some of the constraints and problems they are noted. Fitting Docker into existing infrastructure, and incentivising developers to create Docker containers, can be issues.  But the outcomes–having a better strategy than traditional publication for reproducibility, having ongoing access to the software, and the “deduplication of agony” seems to be worth investigating, for sure. deduplication_of_agony Then Barton describes what the pipeline could look like for a researcher with some new sequence–you can use the data from a variety of assemblers to make decisions about how to proceed, rather than sifting through papers or just using what the lab next door did. And if you have a new assembler, you can use this setup to benchmark it as well.

So if you’ve been hearing about Docker, and have been concerned about access and reproducibility issues around genomics data and software, have a look at this video. It nicely presents the problems we face, and one possible solution, with a concrete example. There may be other useful methods as well–like offering a central portal for uses to access multiple tools, like AutoAsssemblyD has described–but that’s really for a different subset of users. But for the more general problem of software comparisons, benchmarking, and access to bioinformatics tools, Docker seems to offer a useful strategy. And I did a quick PubMed check to see if Docker is percolating through the traditional publication system yet, and found that it is. I found that ballaxy (“a Galaxy-based workflow toolkit for structural bioinformatics”) is offered as a Docker image, which means that having a grasp of Docker going forward may really be useful for software users rather quickly….

Quick links:

nucleotid.es: http://nucleotid.es

Docker: http://www.docker.com

References (and in this case the slide deck):



And other useful and related items from this post:

Automating the Selection Process for a Genome Assembler, JGI Science Highlights. October 17, 2014. http://jgi.doe.gov/automating-selection-process-genome-assembler/

Veras A., Pablo de Sá, Vasco Azevedo, Artur Silva, Rommel Ramos, Institute of Biological Sciences, Federal University Pará, Belém, Pará & Brazil (2013). AutoAssemblyD: a graphical user interface system for several genome assemblers, Bioinformation, 9 (16) 840-841. DOI: http://dx.doi.org/10.6026/97320630009840

Hildebrandt A.K.,  D. Stockel, N. M. Fischer, L. de la Garza, J. Kruger, S. Nickels, M. Rottig, C. Scharfe, M. Schumann, P. Thiel & H.-P. Lenhof & (2014). ballaxy: web services for structural bioinformatics, Bioinformatics, 31 (1) 121-122. DOI: http://dx.doi.org/10.1093/bioinformatics/btu574

Video Tip of the Week: PhosphoSitePlus, protein post-translational modifications

Nucleotide sequence data and analysis commands the bulk of my attention on most days. But certainly post-translational modification of proteins has a lot of influence on the ultimate function (or dysfunction, in some cases) of the genes in play in a given situation. A recent paper reminded me of a resource that I’ve known about for a long time, but I was pleased to have a fresh look at, PhosphoSitePlus. Although it has phosphorylation in the name, it’s broader than that–hence the “Plus”, I imagine.

PhosphoSitePlus® (PSP) is a publicly-accessible web-based portal offering detailed information on post-translational modifications (PTM) of proteins. The PTMs include various types of modifications, not only phosphorylation, but also methylation, acetylation, glycosylation, caspase cleavage, and ubiquitination, among others. There’s a helpful summary on the landing page of the numbers and types of PTMs.

phosphosite_mods

The information comes from high quality curation of literature (low-throughput, LTP) as well as from many high-throughput (HTP) studies. They have been building this resource over many years, and have been refining the collection over that time. In fact, they re-examined some of the older data they had and re-evaluated the quality to improve their collection. So it is actively being built and maintained.

They have a nice video tutorial, which is this week’s Video Tip of the Week. But it’s hosted on their site and I can’t locate an embed feature, so you will have to go over there to have a look. Here’s an image of it below. It has my favorite structure: overview, intro, advanced, and examples. I thought it was a helpful walk through the types of information you can get from their site. PhosphoSite Tutorial screen cap You can navigate to different sections with their menu, or you can close that menu out of the way as the material proceeds. In under 20 minutes you’ll have a great grasp of the features of the site.

In the new paper they reference some features that weren’t in their prior tutorial. Special emphasis on mutations and variations that affect modification sites are now included in their PTMVar collection. This is one of the newer features described in the paper, and this component of their collection offers a look at missense mutations that can impact post-translational modification aspects. This is particularly helpful as we get more sequence information from individuals, and we may come across some that affect PTM sites. The new paper provides details on the sources of this information, which includes cancer resources and OMIM, as well as UniProt.

I also found their Motif Analysis Tool quite handy. On the homepage in the Downloads and Applications area–check out the options. It will let you enter your sequences to analyze and deliver a Sequence Logo if you would like one. Again, there’s more details and nice examples in the paper of the logos. There’s also the option of downloading a Cytoscape plug-in.

So check out PhosphoSitePlus for knowledge about post-translational modifications on proteins you are interested in, and further details on motifs and pathways that are involved.

Quick links:

PhosphoSitePlus®: http://www.phosphosite.org/

PhosphoSitePlus® tutorial: http://www.phosphosite.org/staticTrainingTutorial.do

Reference:

Hornbeck P.V., B. Zhang, B. Murray, J. M. Kornhauser, V. Latham & E. Skrzypek (2014). PhosphoSitePlus, 2014: mutations, PTMs and recalibrations, Nucleic Acids Research, DOI: http://dx.doi.org/10.1093/nar/gku1267

Hornbeck P.V.,  J. M. Kornhauser, S. Tkachev, B. Zhang, E. Skrzypek, B. Murray, V. Latham & M. Sullivan (2011). PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse, Nucleic Acids Research, 40 (D1) D261-D270. DOI: http://dx.doi.org/10.1093/nar/gkr1122

Video Tips of the Week, Annual Review 2014 (part 2)

As you may know, we’ve been doing these video tips-of-the-week for seven years now. We have completed or collected around 350 little tidbit introductions to various resources through this past year, 2014. At first we had to do all of our own video intros, but as the movie technology became more accessible and more teams made their own, we were able to find a lot more that were done by the resource providers themselves. So we began to collect those as well. At the end of the year we’ve established a sort of holiday tradition: we are doing a summary post to collect them all. If you have missed any of them it’s a great way to have a quick look at what might be useful to your work.

You can see past years’ tips here: 2008 I, 2008 II, 2009 I, 2009 II, 2010 I, 2010 II, 2011 I, 2011 II, 2012 I, 2012 II, 2013 I, 2013 II, 2014 I.

July
July 2: NCBI Variation Viewer
July 9: Google Genomics, API and GAbrowse
July 16: VectorBase, for invertebrate vectors of human pathogens
July 23: Nowomics, set up alert feeds for new data
July 30: PhenDisco, “phenotype discoverer” for dbGap data

August
August 6: Biodalliance browser with HiSeq X-Ten data
August 13: EpiViz Genome Browsing (and more)
August 20: Immune Epitope DB (IEDB)
August 27: Phenoscape, captures phenotype data across taxa

September
September 3: NIH 3D Print Exchange
September 10: #Docker, shipping containers for software and data
September 17: GOLD, Genomes OnLine Database
September 24: StratomeX for genomic stratification of diseases

October
October 1: MEGA, Molecular Evolutionary Genetics Analysis
October 8: UCSC #Ebola Genome Portal
October 15: MedGen, GTR, and ClinVar
October 22: SeqMonk
October 29: PaleoBioDB, for your paleobiology searches

November
November 5: Genome Browser in a Box
November 12: UpSet about genomics Venn Diagrams?
November 19: GeneFriends
November 26: Thanksgiving week, light posting. One holiday genome (cranberries).

December
December 3: Video Tip of the Week: BioLayout Express3D for network visualizations
December 10: Video Tip of the Week: “Virtually Immune” computational immune system modeling
December 17: Video Tip of the Week: yEd Graph Editor for visualizing pathways and networks
December 24: Video Tips of the Week, Annual Review 2014 (part 1)
December 31: [this post]

Video Tips of the Week, Annual Review 2014 (part 1)

As you may know, we’ve been doing these video tips-of-the-week for seven years now. We have completed or collected around 350 little tidbit introductions to various resources through this past year, 2014. At first we had to do all of our own video intros, but as the movie technology became more accessible and more teams made their own, we were able to find a lot more that were done by the resource providers themselves. So we began to collect those as well. At the end of the year we’ve established a sort of holiday tradition: we are doing a summary post to collect them all. If you have missed any of them it’s a great way to have a quick look at what might be useful to your work.

You can see past years’ tips here: 2008 I, 2008 II, 2009 I, 2009 II, 2010 I, 2010 II, 2011 I, 2011 II, 2012 I, 2012 II, 2013 I, 2013 II, 2014 II [next week].

January 2014:
January 8: Slidify for generating slides from RStudio
January 15: Entourage and enRoute from the Caleydo team
January 22: StratomeX
January 29: NCBI Sequence Viewer PDF export

February 2014:
February 5: “Overview” for document sorting and mining
February 12: MetaPhlAn and Galaxy
February 19: Centralized Model Organism Database (CMOD)
February 26: Ambiscript Mosaic for visualizing nucleotide motifs

March 2014:
March 5: Introduction to IGB Genome Browser
March 12: JANE, comparing phylogenies
March 19: ICGC portal for cancer genomics
March 26: AGRICOLA for agricultural science searches

April 2014:
April 2: EuPathDB
April 9: List of genes associated with a disease
April 16: NaviCell for custom interaction maps for systems biology
April 23: Atlas of Cancer Signaling Networks
April 30: canEvolve

May 2014:
May 7: BioRxiv, A preprint server for biology
May 14: PheGenI, Phenotype-Genotype Integrator
May 21: PhenX, standardizing phenotype measurements
May 28: New UCSC “stacked” wiggle track view

June 2014:
June 4: LineUp, data ranking visualization tool
June 11: InterMine for complex queries
June 18: e-PathGen, Using Genomics to Support Public Health
June 24: Leukemia outcome predictions challenge

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: “Virtually Immune” computational immune system modeling

This week’s video tip of the week is the next in a series. It began when I took a look at GeneFriends, and their option to output the data for use in BioLayout Express3D. So of course we had to then take a look at BioLayout. While I was exploring BioLayout, I came across Virtually Immune. This project contains intricate network diagrams of immune-system related responses which you can load into BioLayout and explore. It is a very neat way to get further in your understanding of BioLayout functions, as well as being an amazing example of how to model a key system for human health. Here is their video overview:

Virtually Immune is developing computational models of the behavior of immune system responses, in part to help reduce the use of animal models. As part of a CrackIT project challenge, they developed a model of Influenza A lifecycle and macrophage responses that you can explore to help understand the goals of the project. On their “about” page, the overall goal includes:

By enabling scientists to run in silico experiments we hope to help them to model infectious and inflammatory disease-associated processes and thereby accelerate the development of of therapeutic agents. In so doing we hope this resource will assist in the reduction and refinement of the use of animals in immunological research.

Their text-based tutorial walks you through the basic steps of building the kinds of models they have: read the literature, draw the pathway you want to represent, initialize the conditions, and then simulate with BioLayout3D. The last step–Verify–means you go back to the bench and see if your computational model predictions make sense. Hopefully refining your ideas computationally can streamline the work in the lab.

To get the best sense of the capabilities of this project, go to their Pathway Models page. From here you can load up any of the examples in BioLayout and look around. When you hover over a pathway a “Show Me” button will appear near the bottom, and clicking that will load up the data in a larger format that you can explore it. On the bottom of the new page, you can click the BioLayout button to visualize this in 3D.

If you aren’t researching immune system features, that’s fine. But it will still help you to understand how pathways relevant to your work could be modeled.

Quick links:

Virtually Immune: http://www.virtuallyimmune.org/

Virtually Immune tutorial: http://www.virtuallyimmune.org/tutorial/

BioLayout Express3D: http://www.biolayout.org/

Reference:

[can't find one for Virtually Immune yet; will attach one if I find it in the future]

Enright, A., & Ouzounis, C. (2001). BioLayout–an automatic graph layout algorithm for similarity visualization Bioinformatics, 17 (9), 853-854 DOI: 10.1093/bioinformatics/17.9.853

Theocharidis A., Stjin van Dongen, Anton J Enright & Tom C Freeman (2009). Network visualization and analysis of gene expression data using BioLayout Express3D, Nature Protocols, 4 (10) 1535-1550. DOI: http://dx.doi.org/10.1038/nprot.2009.177 *cough* access from their publications page…

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

Video Tip of the Week: BioLayout Express3D for network visualizations

My previous Video Tip of the Week highlighted the GeneFriends tool. With GeneFriends you can search for co-expression of genes in RNA-seq data sets. But you can take these results further and visualize them with the BioLayout Express3D tool, so I wanted to bring more details about BioLayout in this tip since we haven’t covered it before.

BioLayout isn’t a new tool, it’s been around for some time. The first published report of it appeared in 2001. Their publications page reflects their progress over the years, including a new paper recently put out for open peer review (very nifty, kudos on that). BioLayout keeps getting new features as it is under active development, and it keeps incorporating the key standards like BioPax that are important for interoperability of tools in this space. You can learn more about BioPax and related standards from the The ‘COmputational Modeling in BIology’ NEtwork (COMBINE) site.

This video tip will highlight their overview video to give you a taste of what BioLayout Express can do. But they have a page with more videos that can take you further on understanding and using the features of the software.

There’s a Nature Protocols paper that they produced a few years back that helped me to grasp what they want to accomplish and how to work with BioLayout. Although some of the details will have changed, I like these kinds of papers as a way to approach the concepts of working with the tools, so I’ve included that below as well. You can access it from their publications page.

BioLayout Express can handle very impressive numbers of data sets and the corresponding nodes and edges. Their publications page also offers a look at how some researchers have used their tool to advance their research. I like when tool providers offer these kinds of published examples, it helps to see how people really are using the tools.

Quick links:

BioLayout Express3Dhttp://www.biolayout.org/

References:

Enright, A., & Ouzounis, C. (2001). BioLayout–an automatic graph layout algorithm for similarity visualization Bioinformatics, 17 (9), 853-854 DOI: 10.1093/bioinformatics/17.9.853

Theocharidis A., Stjin van Dongen, Anton J Enright & Tom C Freeman (2009). Network visualization and analysis of gene expression data using BioLayout Express3D, Nature Protocols, 4 (10) 1535-1550. DOI: http://dx.doi.org/10.1038/nprot.2009.177 *cough* access from their publications page…

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

Thanksgiving week, light posting. One holiday genome (cranberries).

cranberry_rakeThere’s nobody reading the blog this week each year, everyone is traveling or napping, at least in the US. So I’ll just bring a holiday genome I came across recently. Cranberries. This fruit is one of very few native North American fruits that are widely cultivated. I went looking to see if a genome paper was out yet, and there it was:
The American cranberry: first insights into the whole genome of a species adapted to bog habitat

As I was reading about the project, I thought I should know a bit more about specifically how they are grown. I’ve seen the flooded harvesting images, but I didn’t know what happened prior to that–the “bog habitat”. Conveniently, one of the research sites had links to some interesting videos of how cranberries are farmed. Sand–really–sand is the foundation of the fields. These dead-looking vines are laid out, and then partially buried in the sand. In a few years you will get cranberries. It’s kind of astonishing to actually see it–it looks so barren and lifeless at first.

Planting cranberries was the part new to me, and that video is posted here, but there are several more that include harvesting and shipping.

This genome project has also been added to the Sequenced Plant Genomes wiki that James Schnable maintains at CoGePedia: https://genomevolution.org/wiki/index.php/Sequenced_plant_genomes. And it’s on the phylogenetic tree right near the blueberries (another North American native) on that page.

Cranberry genetics and genomics research site: http://cggl.horticulture.wisc.edu/ . They also link to other groups involved in this work, but this is the one where I found the video.

Another fun fact for you to share at the dinner table: Probing Question: What is a heritage turkey?

“Some of these varieties were the progenitors of our current commercial turkeys, and they are fairly closely related to them genetically,” explains Hulet. “Today’s commercial turkeys are white because people didn’t like the little dots of pigment left on the skin after the feathers are pulled out, so breeders selected for a white-skinned turkey.” The white color is more natural for chickens, he explains, “while it’s a mutation for turkeys.”

Enjoy your mutant foods this holiday season.

Back to regular posting next week.

Reference:

Polashock J., Ehud Zelzion, Diego Fajardo, Juan Zalapa, Laura Georgi, Debashish Bhattacharya & Nicholi Vorsa (2014). The American cranberry: first insights into the whole genome of a species adapted to bog habitat, BMC Plant Biology, 14 (1) 165. DOI: http://dx.doi.org/10.1186/1471-2229-14-165