GEO for plant scientists: Sharing data

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Published on: February 13, 2014

There is currently no microarray service provider in the UK that uploads your plant science microarray data to GEO on your behalf, but publication requires your data to be shared. The most common request from journals is that it is shared on GEO.

GEO has this information page about data submission. While the high-throughput sequence submission guidelines are a still little complicated, microarray experiments have well-established (and enforced!) minimum information requirements and the four main microarray chip providers have customized information pages. An email address is provided for users to email enquiries and ask for help from GEO’s curators.

The Affymetrix page is probably the most useful for UK plant sciences. Spreadsheet-based submission is recommended for Affymetrix deposits, so users should submit an Excel metadata worksheet, CEL files, and processed data for example a Tiling Array. The page gives advice on how to find certain information is given on finding GEO-specific information, and there are template and example spreadsheets.

Once submitted, your dataset becomes a GEO accession and can be identified with a unique accession number. The accession number should be used when you or anyone else references or links to your dataset, which seems like an easy means of tracking its usage within the community.

GEO for plant scientists: How to find Arabidopsis microarray data

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Published on: February 13, 2014

Submission of gene expression data to the Gene Expression Omnibus is now a requirement of publication in most journals, so it is an extremely valuable resource. It is also extremely big, and full of data that isn’t relevant to your question or task at hand – but it is easy to find the right data using the search bar if you follow a few rules. There are example searches on the GEO homepage.

To find data relating to Arabidopsis thaliana, search: (Arabidopsis thaliana[organism])

To find Arabidopsis microarray data, search: (Arabidopsis thaliana[organism]) AND “expression profiling by array”

The easiest way to find other Arabidopsis datasets is to search: (Arabidopsis thaliana[organism]). On the left hand side of the window, there is a ‘Study type’ section. If you click on ‘More…’ a list of study types pops up from which you can select the data type you are looking for (see screen shot below).

You can add any search term you like to the search bar. For example, you could specify author, publication time, types of tissue or stress… or any combination of these. Just keep adding AND in between each term. For example: (Arabidopsis thaliana[organism]) AND “expression profiling by array” AND leaf

GEO provides an informative guide to how to download original records or curated datasets individually or in bulk. You can download data directly from Accession Viewer pages (eg this one) in SOFT, MINiML or TXT formats. Raw data is also available in TAR. You can also do bulk downloads via GEO’s FTP site. All files are compressed using gzip.

It’s also possible to access GEO programmatically in order to, for example, quickly retrieve CEL files from Arabidopsis stress experiments. Again, GEO provide a guide to this, although this is probably something better tackled with some pre-existing knowledge of programming.

GEO post

CellSet confocal image analysis

Categories: guest blogger, resource
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Published on: April 25, 2013

Michael Pound is an image analyst at CPIB. He kindly agreed to write a guest post for GARNet on his recent project, confocal image analysis software CellSeT

CellSeT, which was recently published in Plant Cell (24:1353), is open source software which analyses confocal images of plant cells. CellSeT can extract information including fluorescence and membrane polarity objectively and quickly. A simple workflow begins with the program filtering noise out of the image, and then it segments the image into individual cells. Confocal images can produce excellent slices through root meristems, however some incorrect segmentation is inevitable deeper into the root tissue. CellSeT was designed with this in mind, and the user can then manually refine the cell segments. This optional manual step is followed by an automatic refinement using active contours, aimed at improving accuracy and reducing subjectivity. Finally the cells can be manually assigned semantic tags and measured. Plugins, which are also open source, allow users to carry out more specialised functions, or cell geometries can be exported into modelling packages such as OpenAlea.

CellSeT will be useful to researchers who produce confocal images at a cell scale, usually of root tissue, although CellSeT has been shown to work on other regions such as the plant leaf. The plugin architecture allows anyone with a basic programming knowledge to perform additional image analysis within each cell. For example, an existing plugin is used to detect and quantify nuclear fluorescence in a separate colour channel to the cell walls.

You can download CellSeT from Sourceforge. Due to its use of Windows graphics libraries, CellSeT only runs in Windows. If you don’t use Windows, you will have to run a virtual windows environment to use it. CellSeT works successfully on software such as parallels if this is necessary.

Image credits: CPIB 

Imaging trichomes

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Published on: December 13, 2012
cryo-scanning electron microscope image of a trichome on an Arabidopsis leaf

Even if you don’t work on trichomes, you have definitely experienced them first-hand, as stings on nettles are trichomes that have evolved down a particularly nasty route. Other trichomes pack less of a punch, but they are still important for phytochemical production and herbivore defence.

Arabidopsis trichomes are rather more tame than stinging nettle trichomes, and present an excellent way to study cell differentiation as well as being a target for crop improvement. But although trichomes are easy to see using light microscopy, they are difficult to study. Manually counting them and recording their length and position is tiresome in the extreme, and imaging technologies tend to require specialized skills and software that not all labs can access.

In today’s highlighted paper, Pomeranz et al. present a new method of analysing trichomes consisting of polarizing light microscopy (PLM) and a web-based imaging tool. In fact PLM is an old technique described by Ballard in 1916, and is an excellent way of imaging trichomes because of the highly crystalline cellulose in trichome cell walls which confers polarizing (birefringent) properties. As the authors say, this new technique is a ‘repurposed’ method, and the key to the novel technique is the online resource TRICHOMENET, which allows imaging and easy analysis of trichomes, and can be linked with ImageJ.

It certainly appears that this method would be easy to set up in any lab. Preparing samples for PLM involves methanol or ethanol, lactic acid, and a water bath – the method is in the paper or in Bischoff et al. (2010). PLM itself requires polarizing filters, which can be bought in a kit, for example from Motic, or as individual filters. The image is then uploaded to TRICHOMENET, which guides the user through counting the trichomes. Once the data is recorded, TRICHOMENET can analyse trichome positional data, density, and distances.

Highlighted article: Marcelo Pomeranz, Jeffrey Campbell, Dan Siegal-Gaskins, Jacob Engelmeier, Tyler Wilson, Virginia Fernandez Jelena Brkljacic, and Erich Grotewold (2012) High-resolution computational imaging of leaf hair patterning using polarized light microscopy. The Plant Journal ‘Accepted Article’, doi: 10.1111/tpj.12075

Image credit: Emmanuel Boudet.

SpotOn London 2012 in brief

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Published on: November 15, 2012

This weekend, Ruth and I were in London for SpotOn London 2012 at the Wellcome Trust. There were too many incredible sessions to attend, let alone to cover on this little blog – but all the talks were recorded and you can see them on the SpotOn youtube channel. There will be Storifies aplenty before the end of the week, which I will tweet if they cross my path.

I plan to write at least one ‘proper’ post about the sessions I attended, but for now here are some brief summaries of the topics most discussed in the sessions I attended at SpotOn 2012.

Open data: All the speakers and delegates assumed that everyone else understood and supported open access publishing. What was more interesting was the discussions of other issues in open science – digital licensing, openness in peer review, accessibility of raw data. A longer blog post on this is forthcoming, but I recommend Ross Mounce’s blog, in particular this post on price and ‘openness’ in open access journals, for more information about open science.

Crowd-funding: Around the fringes of publically funded science are small projects supported by funds raised by the researchers. Crowd-funded science is very much in the minority, but in the UK the University of Buckingham has survived for over thirty years without government support, including research programmes. For crowd-funding, excellent marketing and PR are crucial. If you have a public-good, sexy, relatively low-cost research project in your to-do list, and you have a flair for public relations and promotion, it is worth considering. You also need to be able to reward donations in some small way. Check out crowd-funded projects by Matthew Partridge (Cranfield University) and Ethan Perstein (Princeton) to find out more, or donate to their projects. Kickstarter is the best platform to raise your funds.

(more…)

Phytotracker

Categories: methods, resource
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Published on: November 6, 2012
Fig. 4 from Nieuwland et al. (2012), showing the Phytotracker labels

If you have ever been frustrated by a less than clearly labeled seed stock, not knowing what the green and yellow dots mean, how long its been in that drawer, or which generation it is, GARNet Chair Jim Murray’s lab in Cardiff have worked out a system that will help.

Phytotracker is a system that organizes your seeds for you. Of course it depends on people recording the tray number and parent lines in the database, and correctly labeling the seed stocks and plasmids in the lab. However, once you’ve done that, you can forget about it because Phytotracker does the remembering for you – everything from which plasmid was used for the transformation to when to harvest the seeds.

The system is well explained in the paper, which was published in Plant Methods in October. If you want to try the system out, you’ll need Filemaker Pro (version 8 or later), or for a fully networked solution Filemaker Pro Advanced (version 8 or later: currently Filemaker Pro is version 12). Your University may already have a site licence! You’ll also need printers in your growth rooms and labs to print labels for the trays, plants, and seed stocks. Commitment from everyone in your group is essential – this system would fall apart if you have a regenade group member who insists on labeling with autoclave tape and a Sharpie. It has been successfully used in Cardiff for five years though, so it looks like a system that is worth committing to.

Highlighted article: Jeroen Nieuwland, Emily Sornay, Angela Marchbank, Barend HJ de Graaf, James AH Murray (2012) Phytotracker, an information management system for easy recording and tracking of plants, seeds and plasmids. Plant Methods 8:43

Download Phytotracker here: http://sourceforge.net/projects/phytotracker/

 

Guest post: Plantwise Knowledge Bank Map

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Published on: October 25, 2012

Tim Holmes works for CABI, a not-for-profit international organization that improves people’s lives by providing information and applying scientific expertise to solve problems in agriculture and the environment. Plantwise is an initiative, led by CABI, to improve food security and the lives of the rural poor by reducing crop losses. It is for the Plantwise Knowledge Bank, that Tim tackles the challenge of presenting species distribution data to a diverse group of users.

Plantwise is the biggest project that the whole of CABI has ever been engaged in together. It brings together all the strands of our work, from the publishing business, through scientific research, to international development. The Knowledge Bank is my bit of the programme, and something I’m immensely proud of. We’ve been developing a suite of data and information tools over the last few years, and it’s to one of these that I’d like to introduce you now.

The Plantwise Knowledge Bank Map was the first tool concept that we presented back in 2010, and straight away it was our number one priority to make it a reality. The genesis was a crude, but cool looking, Google Earth presentation of CABI’s plant pest distribution data. The globe spun and zoomed impressively, but it wasn’t going to be the useful scientific tool that we were after. For starters you couldn’t see the whole of the Earth’s surface at once; problematic if you wanted to get a Baumgartner’s-Eye view of the worldwide range of a pest! It was problematic too if you wanted to build it into a website that would fling around large datasets AND do so for users with restricted internet bandwidth. So we trialled many different bits of mapping software and settled on something that would display a Google Maps-style projection and would let us do as much of the map production leg-work on our servers. It would be familiar and fast. (more…)

Friday Film: Automatic cell counting with ImageJ

Categories: methods, resource
Comments: No Comments
Published on: October 5, 2012

This is a video tutorial on quantifying cells using ImageJ. ImageJ is a free tool for image processing and analysis in Java. I chose to highlight this tutorial as it is clearly explained and may be of use to many plant scientists, but YouTube is a goldmine of other less professional tutorials on using ImageJ for any number of applications. I was particularly interested in this one on quantifying stained liver tissue, as I used to work on secondary cell walls and it would have been a handy tool for qualitative analysis of my many images of phloroglucinol stained tobacco stem cross-sections.

Created by Keene State College’s Center for Engagement, Learning, and Teaching.

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