Bioimage Informatics

It is no secret that imaging, and microscopy in particular, represents a substantial fraction of the manuscripts published in Nature Methods. Our very first focus issue, in fact, was on fluorescence imaging. When that focus was published in 2005 the term ‘bioimage informatics’ didn’t even exist. Even today, the term isn’t widely used and, unlike many other bioinformaticians, those who work on the development of algorithms and software tools for analysis of biological image data have few dedicated venues for discussing or publishing their work.

But computational techniques are becoming increasingly important in biological imaging and the people developing these tools increasingly see themselves as a distinct community. When we approached the community about publishing a focus issue on bioimage informatics there was an enthusiastic response and the results can be seen in our July issue and focus that went live today.

We hope that biologists using microscopy in their research find the information in the focus useful and that it stimulates them to try some of the tools now available and in development. Many of these tools have functionality designed to encourage community participation and aid in both the creation of new analysis methods and the communication of methods and protocols to other users.

Although these tools and the community developing them have come a long way since Wayne Rasband first released NIH Image, bioimage informatics is still in its relative infancy. As discussed in the focus editorial, algorithm development and usage will become even more important for biological microscopy and will change the way biologists perform and report their research.

Where’s your ground truth?

When using or developing experimental and observational methods it is crucial to assess the method performance in an effort to ensure that the information it provides reflects reality. For experimental biologists this often means conducting carefully chosen control experiments with alternative methods or different experimental settings. More rigorous assessment, particularly for high-throughput or large-scale methods, often requires the use of ‘ground truth’ or ‘gold standard’ data sets. But talk to different people and you will get different answers regarding what ‘ground truth’ or ‘gold standard’ data is. This often includes a nice historical explanation of where the term ‘ground truth’ comes from.

For developers of signal processing and image analysis algorithms though, the situation is clearer; the ground truth is the signal or image you start with. But add a living system into the mix and things get far more complicated. The Editorial in the November issue of Nature Methods discusses the challenges facing developers and users of algorithms for automated analysis of biological data, with a focus on image data. In short, traditional ground truth data is often insufficient. The addition of integrated-editing and change-logging capabilities to these software tools can increase the quality of the analysis, aid further algorithm development and increase the likelihood of biologists adopting the software in the first place.

Cloud computing in biology

The sheer amount of data being generated in large-scale high-throughput biological studies is challenging current capabilities for data storage and analysis. One solution to this has been to move to cloud computing. In our editorial this month we discuss current efforts in this direction and the particular challenges of biological analysis in the cloud.

iPhones in the lab

Do you use your iPhone (or other smartphone or mobile computing device) in the lab? This month’s editorial notes how large numbers of scientists seem to have an iPhone or other mobile device capable of running quite sophisticated applications, or apps. Increasing numbers of these apps are targeted at biologists and some are even intended for use at the lab bench; and lists of recommended apps are popping up on blogs and other sites. Check out the links below for a sample.

22 iPhone Apps for Science Geeks – July 11, 2008

More iPhone apps for scientists – October 13, 2008

5 Bio-Related Apps for your iPhone/iPod Touch – November 4, 2008

iPhone and research – July 24, 2009

iPhone apps every biologist needs – October 9, 2009

10 Best iPhone Apps for Science Majors – December 23, 2009

Some recently released apps that don’t appear in the lists above are:

Bio-Rad PCR – Practical guidance for performing PCR and qPCR

NEB Tools – Double digest finder and restrictions enzyme finder tools

ChemMobi – Search for chemical information by name or ID. View selected properties, MSDS information and structure.

LabCal & LabCalPro – Various laboratory calculation functions molarity, moles, stock dilutions, pH & g-force

GeneticCode & GeneticCodePro – Reference tool the nucleic acid codon table and amino acid properties

But how likely is it that bench researchers will actually use an expensive personal mobile computing device like an iPhone in the lab environment? Since we are no longer in the lab ourselves, we wonder what the current generation of grad students and post-docs are doing. Is your iPhone useful in the lab? What about a similar portable device? What apps do you use?

Although there has been a lot of noise surrounding the new Android-based phone from Google we were unable to find an apps intended for use in the lab on that platform, with the exception of seemingly hundreds of scientific calculator apps. We would love to hear from any readers who are familiar with scientific apps available for platforms other than the iPhone.

Yesterday Apple announced the long anticipated iPad mobile computing device. This tablet computer can run iPhone apps in addition to providing a far larger screen and the capability to run more powerful applications than the iPhone. It seems unlikely that such a device would become as ubiquitous among scientists as the iPhone since it doesn’t double as a phone. However, it has definite advantages as a dedicated laboratory tool and is more suitable for reading journal articles than the iPhone.

Speaking of reading journal articles, the nature.com app should be available for download from the Apple app store on February 1. We’ll keep you posted.

Trace some neurons, get a big check

The idea of tracing the structure of stained neurons to obtain functional insights into in situ neural networks isn’t new and dates back over 100 years to Ramón y Cajal. Just recently I discovered that copies of his books have been digitized by Google and the illustrations are incredible. See for example Studien über die Hirnrinde des Menschen.

Even though software now exists to automate this process, it seems that the performance still isn’t good enough to replace manual tracing of image stacks of labeled neurons. Neuroanatomists often spend days to months manually tracing the structures rather than rely on the software that currently exists for automating the process.

Two big players in the neuroscience field have decided to do something about this. On April 9th the Allen Institute for Brain Science and the Janelia Farm Research Campus of the Howard Hughes Medical Institute announced the DIADEM Challenge. The acronym is derived from Digital Reconstruction of Axonal and Dendritic Morphology. I wonder how long it took to come up with that one.

Over the next year groups and individuals are invited to download image stacks of real data and use their algorithms to create digital reconstructions of the neurons and submit them for evaluation. Five finalists will be invited to compete in a final round at the Janelia Farm Research Campus. The organizers will award a $75,000 cash prize to the winner whose algorithm performs the best. The results will be submitted for publication in a special issue of the journal Neuroinformatics.

The hope is that the competition will encourage the advances in automated neuronal tracing that will be required for researchers to construct a functional atlas of the brain — one of the principle goals of the research at Janelia Farm.

It is encouraging to see a competition devoted to a small community like this. Nature Methods previously argued that such competitions would be valuable for improving algorithms in such specialized applications. I’m excited to see that it is happening and wish the organizers and competitors are successful.

Going for algorithm gold

In the spirit of the upcoming 2008 Olympic Games in Beijing an Editorial in the August issue of Nature Methods discusses the use of organized competition for evaluating algorithm performance. Such competitions, or “collaborative experiments”, have become very popular in some large communities.

We believe similar competitions would also be helpful for smaller communities. What do you think? Is it more trouble than it is worth or would it be a valuable way of pushing algorithm performance to new heights?

Data overload

How do you handle terabytes of data? That is a question that more and more investigators must face, on a weekly basis.

Are you one of them? Light-sheet fluorescence imaging, for example, generates so much data in each experimental run that handling and storing the raw data is a challenge. Next-generation sequencing is another, much more ubiquitous, case.

Read the July issue editorial “Byte-ing off more than you can chew” and let us know about your own experience, problems and practical (or impractical) solutions.

Social software

Don’t be mistaken, Nature Methods’ material sharing policy includes the requirement to make custom-developed software available upon publication. But there are several ways of making software available. We examine the various degrees of disclosure and the choice of formats and try to clarify our position. Let us know if we are heading in the right direction!