The more things change, the more they stay the same

…also known as Day 1 of the BBSRC Synthetic Biology Standards Workshop at Newcastle University, and musings arising from the day’s experiences.

In my relatively short career (approximately 12 years – wait, how long?) in bioinformatics, I have been involved to a greater or lesser degree in a number of standards efforts. It started in 1999 at the EBI, where I worked on the production of the protein sequence database UniProt. Now, I’m working with systems biology data and beginning to look into synthetic biology. I’ve been involved in the development (or maintenance) of a standard syntax for protein sequence data; standardized biological investigation semantics and syntax; standardized content for genomics and metagenomics information; and standardized systems biology modelling and simulation semantics.

(Bear with me – the reason for this wander through memory lane becomes apparent soon.)

How many standards have you worked on? How can there be multiple standards, and why do we insist on creating new ones? Doesn’t the definition of a standard mean that we would only need one? Not exactly. Take the field of systems biology as an example. Some people are interested in describing a mathematical model, but have no need for storing either the details of how to simulate that model or the results of multiple simulation runs. These are logically separate activities, yet they fall within a single community (systems biology) and are broadly connected. A model is used in a simulation, which then produces results. So, when building a standard, you end up with the same separation: have one standard for the modelling, another for describing a simulation, and a third for structuring the results of a simulation. All that information does not need to be stored in a single location all the time. The separation becomes even more clear when you move across fields.

But this isn’t completely clear cut. Some types of information overlap within standards of a single domain and even among domains, and this is where it gets interesting. Not only do you need a single community talking to each other about standard ways of doing things, but you also need cross-community participation. Such efforts result in even more high-level standards which many different communities can utilize. This is where work such as OBI and FuGE sit: with such standards, you can describe virtually any experiment. The interconnectedness of standards is a whole job (or jobs) in itself – just look at the BioSharing and MIBBI projects. And sometimes standards that seem (at least mostly) orthogonal do share a common ground. Just today, Oliver Ruebenacker posted some thoughts on the biopax-discuss mailing list where he suggests that at least some of BioPAX and SBML share a common ground and might be usefully “COMBINE“d more formally (yes, I’d like to go to COMBINE; no, I don’t think I’ll be able to this year!). (Scroll down that thread for a response by Nicolas Le Novère as to why that isn’t necessarily correct.) So, orthogonality, or the extent to which two or more standards overlap, is sometimes a hard thing to determine.

So, what have I learnt? As always, we must be practical. We should try to develop an elegant solution, but it really, really should be one which is easy to use and intuitive to understand. It’s hard to get to that point, especially as I think that point is (and should be) a moving target. From my perspective, group standards begin with islands of initial research in a field, which then gradually develop into a nascent community. As a field evolves, ‘just-enough’ strategies for storing and structuring data become ‘nowhere-near-enough’. Communication with your peers becomes more and more important, and it becomes imperative that standards are developed.

This may sound obvious, but the practicalities of creating a community standard means such work requires a large amount of effort and continued goodwill. Even with the best of intentions, with every participant working towards the same goal, it can take months – or years – of meetings, document revisions and conference calls to hash out a working standard. This isn’t necessarily a bad thing, though. All voices do need to be heard, and you cannot have a viable standard without input from the community you are creating that standard for. You can have the best structure or semantics in the world, but if it’s been developed without the input of others, you’ll find people strangely reluctant to use it.

Every time I take part in a new standard, I see others like me who have themselves been involved in the creation of standards. It’s refreshing and encouraging. Hopefully the time it takes to create standards will drop as the science community as a whole gets more used to the idea. When I started, the only real standards in biological data (at least that I had heard of) were the structures defined by SWISS-PROT and EMBL/GenBank/DDBJ. By the time I left the EBI in 2006, I could have given you a list a foot long (GO, PSI, and many others), and that list continues to grow. Community engagement and cross-community discussions continue to be popular.

In this context, I can now add synthetic biology standards to my list of standards I’ve been involved in. And, as much as I’ve seen new communities and new standards, I’ve also seen a large overlap in the standardization efforts and an even greater willingness for lots of different researchers to work together, even taking into account the sometimes violent disagreements I’ve witnessed! The more things change, the more they stay the same…

At this stage, it is just a limited involvement, but the BBSRC Synthetic Biology Standards Workshop I’m involved in today and tomorrow is a good place to start with synthetic biology. I describe most of today’s talks in this post, and will continue with another blog post tomorrow. Enjoy!

For those with less time, here is a single sentence for each talk that most resounded with me:

  1. Mike Cooling: Emphasising the ‘re’ in reusable, and make it easier to build and understand large models from reusable components.
  2. Neil Wipat: For a standard to be useful, it must be computationally amenable as well as useful for humans.
  3. Herbert Sauro: Currently there is no formal ontology for synthetic biology, but one will need to be developed.

This meeting is organized by Jen Hallinan and Neil Wipat of Newcastle University. Its purpose is to set up key relationships in the synthetic biology community to aid the development of a standard for that community. Today, I listened to talks by Mike Cooling, Neil Wipat, and Herbert Sauro. I was – unfortunately – unable to be present for the last couple of talks, but will be around again for the second – and final – day of the workshop tomorrow.

Mike Cooling – Bioengineering Institute Auckland, New Zealand

Mike uses CellML (it’s made where he works, but that’s not the only reason…) in his work with systems and synthetic biology models. Among other things, it wraps MathML and partitions the maths, variables and units into reusable pieces. Although many of the parts seem domain specific, CellML itself is actually not domain specific. Further, unlike other modelling languages such as SBML, components in CellML are reusable and can be imported into other models. (Yes, a new package called comp in SBML Level 3 is being created to allow the importing of models into other models, but it isn’t mature – yet.)

How are models stored? There is the CellML repository, but what is out there for synthetic biology? The Registry of Standard Biological Parts was available, but only described physical parts. Therefore they created a Registry of Standard Virtual Parts (SVPs) to complement the original registry. This was developed as a group effort with a number of people including Neil Wipat and Goksel Misirli at Newcastle University.

They start with template mathematical structures (which are little parts of CellML), and then use the import functionality available as part of CellML to combine the templates into larger physical things/processes (‘SVPs’) and ultimately to combine things into system models.

They extended the CellMLRepository to hold the resulting larger multi-file models, which included adding a method of distributed version control and allow the sharing of models between projects through embedded workspaces.

What can these pieces be used for? Some of this work included the creation of a CellML model of the biology represented in Levskaya et al. 2005 and deposit all of the pieces of the model in the CellML repository. Another example is a model he’s working on about shear stress and multi-scale modelling for aneurysms.

Modules are being used and are growing in number, which is great, but he wants to concentrate more at the moment on the ‘re’ of the reusable goal, and make it easier to build and understand large models from reusable components. Some of the integrated services he’d like to have: search and retrieval, (semi-automated) visualization, semantically-meaningful metadata and annotations, and semi-automated composition.

All this work above converges on the importance of metadata. With the CellML Metadata Framework 1.0, not many used it. With version 2.0 they have developed a core specification with is very simple and then provide many additional satellite specifications. For example, there is a biological information satellite, where you use the biomodels qualifiers as relationships between your data and MIRIAM URNs. The main challenge is to find a database that is at the right level of abstraction (e.g. canonical forms of your concept of interest).

Neil Wipat – Newcastle University

Please note Neil Wipat is my PhD supervisor.

Speaking about data standards, tool interoperability, data integration and synthetic biology, a.k.a “Why we need standards”. They would like to promote interoperability and data exchange between their own tools (important!) as well as other tools. They’d also like to facilitate data integration to inform the design of biological systems both from a manual designer’s perspective and from the POV of what is necessary for computational tool use. They’d also like to enable the iterative exchange of data and experimental protocols in the synthetic biology life cycle.

A description of some of the tools developed in Neil’s group (and elsewhere) exemplify the differences in data structures present within synthetic biology. BacilloBricks was created to help get, filter and understand the information from the MIT registry of standard parts. They also created the Repository of Standard Virtual Biological Parts. This SVP repository was then extended with parts from Bacillus and was extended to make use of SBML as well as CellML. This project is called BacilloBricks Virtual. All of these tools use different formats.

It’s great having a database of SVPs, but you need a way of accessing and utilizing the database. Hallinan and Wipat have started a collaboration with Microsoft Research with the people who created a programming language for genetic engineering of living cells called the genetic engineering of cells (GEC) simulator. Some work a summer student did created a GEC compiler for SVPs from BacilloBricks virtual. Goksel has also created the MoSeC system where you can automatically go from a model to a graph to a EMBL file.

They also have BacillusRegNet, which is an information repository about transcription factors for Bacillus spp. It is also a source of orthogonal transcription factors for use in B. subtilis and Geobacillus. Again, it is very important to allow these tools to communicate efficiently.

The data warehouse they’re using is ONDEX. They feed information from the ONDEX data store to the biological parts database. ONDEX was created for systems biology to combine large experimental datasets. ONDEX views everything as a network, and is therefore a graph-based data warehouse. ONDEX has a “mini-ontology” to describe the nodes and edges within it, which makes querying the data (and understanding how the data is structured) much easier. However, it doesn’t include any information about the synthetic biology side of things. Ultimately, they’d like an integrated knowledgebase using ONDEX to provide information about biological virtual parts. Therefore they need a rich data model for synthetic biology data integration (perhaps including an RDF triplestore).

Interoperabiligy, Design and Automation: why we need standards.

Requirement 1. There needs to be interoperability and data exchange among these tools as well as among these tools and other external tools. Requirement 2. Standards for data integration aid the design of synthetic systems. The format must be both computationally amenable and useful for humans. Requirement 3. Automation of the design and characterization of synthetic systems, and this also requires standards.

The requirements of synthetic biology research labs such as Neil Wipat’s make it clear that standards are needed.

KEYNOTE: Herbert Sauro – University of Washington, US

Herbert Sauro described the developing community within synthetic biology, the work on standards that has already begun, and the Synthetic Biology Open Language (SBOL).

He asks us to remember that Synthetic Biology is not biology – it’s engineering! Beware of sending synthetic biology grant proposals to a biology panel! It is a workflow of design-build-test. He’s mainly interested in the bit between building and testing, where verification and debugging happens.

What’s so important about standards? It’s critical in engineering, where if increases productivity and lowers costs. In order to identify the requirement you must describe a need. There is one immediate need: store everything you need to reconstruct an experiment within a paper (for more on this see the Nature Biotech paper by Peccoud et al. 2011: Essential information for synthetic DNA sequences). Currently, it’s almost impossible to reconstruct a synthetic biology experiment from a paper.

There are many areas requiring standards to support the synthetic biology workflow: assembly, design, distributed repositories, laboratory parts management, and simulation/analysis. From a practical POV, the standards effort needs to allow researchers to electronically exchange designs with round tripping, and much more.

The standardization effort for synthetic biology began with a grant from Microsoft in 2008 and the first meeting was in Seattle. The first draft proposal was called PoBoL but was renamed to SBOL. It is a largely unfunded project. In this way, it is very similar to other standardization projects such as OBI.

DARPA mandated 2 weeks ago that all projects funded from Living Foundries must use SBOL.

SBOL is involved in the specification, design and build part of the synthetic biology life cycle (but not in the analysis stage). There are a lot of tools and information resources in the community where communication is desperately needed.

SBOL Semantic, SBOL Visual, and SBOL Script. SBOL Semantic is the one that’s going to be doing all of the exchange between people and tools. SBOL Visual is a controlled vocabulary and symbols for sequence features.

Have you been able to learn anything from SBML/SBGN, as you have a foot in both worlds? SBGN doesn’t address any of the genetic side, and is pretty complicated. You ideally want a very minimalistic design. SBOL semantic is written in UML and is relatively small, though has taken three years to get to this point. But you need host context above and beyond what’s modelled in SBOL Semantic. Without it, you cannot recreate the experiment.

Feature types such as operator sites, promoter sites, terminators, restriction sites etc can go into the sequence ontology (SO). The SO people are quite happy to add these things into their ontology.

SBOLr is a web front end for a knowledgebase of standard biological parts that they used for testing (not publicly accessible yet). TinkerCell is a drag and drop CAD tool for design and simulation. There is a lot of semantic information underneath to determine what is/isn’t possible, though there is no formal ontology. However, you can semantically-annotate all parts within TinkerCell, allowing the plugins to interpret a given design. A TinkerCell model can be composed of sub-models. Makes it easy to swap in new bits of models to see what happens.

WikiDust is a TinkerCell plugin written in Python which searches SBPkb for design components, and ultimately uploads them to a wiki. LibSBOLj is a library for developers to help them connect software to SBOL.

The physical and host context must be modelled to make all of this useful. By using semantic web standards, SBOL becomes extensible.

Currently there is no formal ontology for synthetic biology but one will need to be developed.

Please note that the notes/talks section of this post is merely my notes on the presentation. I may have made mistakes: these notes are not guaranteed to be correct. Unless explicitly stated, they represent neither my opinions nor the opinions of my employers. Any errors you can assume to be mine and not the speaker’s. I’m happy to correct any errors you may spot – just let me know!

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Modeling and Managing Experimental Data Using FuGE

Want to share your umpteen multi-omics data sets and experimental protocols with one common format? Encourage collaboration! Speak a common language! Share your work! How, you might ask? With FuGE!

In 2007, FuGE version 1 was released (website, Nature Biotechnology paper). FuGE allows biologists and bioinformaticians to describe any life science experiment using a single format, making collaboration and repeatability of experiments easier and more efficient. However, if you wanted to start using FuGE, until now it was difficult to know where to start. Do you use FuGE as it stands? Do you create an extension of FuGE that specifically meets your needs? What do the developers of FuGE suggest when taking your first steps using it? This paper focuses on best practices for using FuGE to model and manage your experimental data. Read this paper, and you’ll be taking your first steps with confidence!

ResearchBlogging.org

Want to share your umpteen multi-omics data sets and experimental protocols with one common format? Encourage collaboration! Speak a common language! Share your work! How, you might ask? With FuGE, and this latest paper (citation at the end of the post) tells you how.

In 2007, FuGE version 1 was released (website, Nature Biotechnology paper). FuGE allows biologists and bioinformaticians to describe any life science experiment using a single format, making collaboration and repeatability of experiments easier and more efficient. However, if you wanted to start using FuGE, until now it was difficult to know where to start. Do you use FuGE as it stands? Do you create an extension of FuGE that specifically meets your needs? What do the developers of FuGE suggest when taking your first steps using it? This paper focuses on best practices for using FuGE to model and manage your experimental data. Read this paper, and you’ll be taking your first steps with confidence!

[Aside: Please note that I am one of the authors of this paper.]

What is FuGE? I’ll leave it to the authors to define:

The approach of the Functional Genomics Experiment (FuGE) model is different, in that it attempts to generalize the modeling constructs that are shared across many omics techniques. The model is designed for three purposes: (1) to represent basic laboratory workflows, (2) to supplement existing data formats with metadata to give them context within larger workflows, and (3) to facilitate the development of new technology-specific formats. To support (3), FuGE provides extension points where developers wishing to create a data format for a specific technique can add constraints or additional properties.

A number of groups have started using FuGE, including MGED, PSI (for GelML and AnalysisXML), MSI, flow cytometry, RNA interference and e-Neuroscience (full details in the paper). This paper helps you get a handle on how to use FuGE by presenting two running examples of capturing experimental metadata in the fields of flow cytometry and proteomics of flow cytometry and gel electrophoresis. Part of Figure 2 from the paper is shown on the right, and describes one section of the flow cytometry FuGE extension from FICCS.

The flow cytometry equipment created as subclasses of the FuGE equipment class.
The flow cytometry equipment created as subclasses of the FuGE equipment class.

FuGE covers many areas of experimental metadata including the investgations, the protocols, the materials and the data. The paper starts by describing how protocols are designed in FuGE and how those protocols are applied. In doing so, it describes not just the protocols but also parameterization, materials, data, conceptual molecules, and ontology usage.

Examples of each of these FuGE packages are provided in the form of either the flow cytometry or the GelML extensions. Further, clear scenarios are provided to help the user determine when it is best to extend FuGE and when it is best to re-use existing FuGE classes. For instance, it is best to extend the Protocol class with an application-specific subclass when all of the following are true: when you wish to describe a complex Protocol that references specific sub-protocols, when the Protocol must be linked to specific classes of Equipment or Software, and when specific types of Parameter must be captured. I refer you to the paper for scenarios for each of the other FuGE packages such as Material and Protocol Application.

The paper makes liberal use of UML diagrams to help you understand the relationship between the generic FuGE classes and the specific sub-classes generated by extensions. A large part of the paper is concerned expressly with helping the user understand how to model an experiment type using FuGE, and also to understand when FuGE on its own is enough. But it also does more than that: it discusses the current tools that are already available for developers wishing to use FuGE, and it discusses the applicability of other implementations of FuGE that might be useful but do not yet exist. Validation of FuGE-ML and the storage of version information within the format are also described. Implementations of FuGE, including SyMBA and sysFusion for the XML format and ISA-TAB for compatibility with a spreadsheet (tab-delimited) format, are also summarised.

I strongly believe that the best way to solve the challenges in data integration faced by the biological community is to constantly strive to simply use the same (or compatible) formats for data and for metadata. FuGE succeeds in providing a common format for experimental metadata that can be used in many different ways, and with many different levels of uptake. You don’t have to use one of the provided STKs in order to make use of FuGE: you can simply offer your data as a FuGE export in addition to any other omics formats you might use. You could also choose to accept FuGE files as input. No changes need to be made to the underlying infrastructure of a project in order to become FuGE compatible. Hopefully this paper will flatten the learning curve associated for developers, and get them on the road to a common format. Just one thing to remember: formats are not something that the end user should see. We developers do all this hard work, but if it works correctly, the biologist won’t know about all the underpinnings! Don’t sell your biologists on a common format by describing the intricacies of FuGE to them (unless they want to know!), just remind them of the benefits of a common metadata standard: cooperation, collaboration, and sharing.

Jones, A., Lister, A.L., Hermida, L., Wilkinson, P., Eisenacher, M., Belhajjame, K., Gibson, F., Lord, P., Pocock, M., Rosenfelder, H., Santoyo-Lopez, J., Wipat, A., & Paton, N. (2009). Modeling and Managing Experimental Data Using FuGE OMICS: A Journal of Integrative Biology, 2147483647-13 DOI: 10.1089/omi.2008.0080

SyMBA Demo causes pondering: how should a bioinformatician choose their output format(s)?

BBSRC Systems Biology Grantholder Workshop, University of Nottingham, 16 December 2008.

SyMBA Demo. The lunch hour was also the demo hour. People came to visit me at the SyMBA demo desk for the whole hour, and we had some interesting conversations. There is one particular question I would like to relate from that hour: what should a bioinformatician choose as an output/export format for multi-omics data? This post relates my thoughts about this challenge. It's not meant to be comprehensive: just some ramblings.

I solve this challenge in SyMBA by storing everything as FuGE objects, which can be exported to FuGE-ML. FuGE-ML can be converted into ISA-TAB and into an html format that mimics ISA-TAB using an XSLT. Therefore, because of this interlink between FuGE and ISA-TAB, you can leverage two complementary formats.

But to a bioinformatician who has just been tasked with building an application (and generally on a short time-scale), how do they choose what export format to use, e.g. FuGE or ISA-TAB? There are considerations of:

  • scale: lightweight or heavyweight implementation. A lightweight implementation might favor your own version of ISACreator and the use of ISA-TAB, or a FuGE-based archive (but not a full-blown LIMS) like SyMBA. A heavyweight solution might be a full LIMS such as PIMS, or another FuGE implementation in development called SysFusion.
  • intent: what is the purpose of storing this data? Is it for later analysis? For later deposition to a public database, e.g. at the EBI? Is it archiving? Is it a combination of these things? Your intent will shape what type of application you build, and what formats you focus your effort on. If your intent is storage only, choose whatever is most convenient for your users. However, these days there is always some aspect of data sharing or publishing. If you need further analysis of the data, then you probably want to be able to produce a computationally-friendly format such as XML. If your intent is submission to public databases, you need to ensure you export in a format they import.

Unfortunately, what this means is that the decision depends on the circumstances. FuGE and ISA-TAB are linked, and so you really get two for the price of one with those. I see this sort of thing as a positive – you have a choice as to the representation, storage and export of your data – a choice of formats! And many, like FuGE and ISA-TAB, are going to be easily convertable. The choice depends on your needs, but there is one easy choice: use something that's already been developed – don't reinvent the wheel!

Anyone else have any further suggestions?

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SysMO-DB and Carole Goble, BBSRC Systems Biology Workshop

BBSRC Systems Biology Grantholder Workshop, University of Nottingham, 16 December 2008.

Systems Biology of Microorganisms. 11 projects from 91 institutes, whose aim is to record and describe the dynamic molecular processes occurring in microorganisms in a comprehensive way. These projects have no one concept of experimentation or modelling, which makes it tough for information exchange. Further, there are issues of people having their own solutions, suspicions (about sharing data, for instance), data issues (many don't have data or don't store it in a standard way) and resource issues (no extra resources). SysMO-DB started in July 2008, and is a 3 year funded effort (3+3 people in 3 teams over 3 sites). Provide a web-based solution to exchange, search, and disseminate data. Need to retrofit data access, model handling and data integration platform. Because of the large number of groups and projects, they are going to aim for low-hanging fruit and early wins: be realistic, not reinvent, sustainable, and encourage standards adoption.

Just like at CISBAN, where we have implemented a web-based data integration, storage, exchange, and dissemination platform in a standards-compliant way (SyMBA), they have three users: experimentalists, bioinformaticians, and modellers. They're lucky, though, in that they have 6 people to develop SysMO-DB, when CISBAN only has 1. 🙂 And, as with CISBAN and many other data integration efforts, much of the work is social: that is, encouraging those three user types to collaborate and understand each other's work. The social solutions include questionnairs, "PALS" (postdocs and phd students), and Audits and sharing of methods, data, models. They discuss things like what people need or don't need from MIAME. (Personal opinion and question: MIAME is intended as a minimal information checklist. What kind of things, then, don't they need? And would it be worth taking this information back to the MIAME people to possibly modify the guidelines if some aspects of it aren't truly minimal? End personal questions.)

Discovery is done via SysMo-SEEK. How to catalog the metadata, and then have mechanisms for accessing the data from locations other than the host site? There is a single search point over "yellow pages" and assets catalogue. They store metadata on results, not the results themselves (again, just like SyMBA, which stores the metadata in a database, and the results in a remote file store). They use myExperiment for both linking the people and the assets. For models, they're using a local installation of JWS online, which is a database of curated models and a model simulator. There is also some links to semantic SBML from the TRANSLUCENT project.

There are two kinds of processes to store. The first is experimental processes, e.g. SOPs and protocols. They use the Nature protocols format, with the addition of high-level classification through tags. (Personal note: What is the underlying format for storing protocols?) The second type of process is Bioinformatics processes, which are stored as workflows. (Question: Why don't you store protocols as workflows? They can be chained in the same way.)  Taverna is used for this work. One bit of work was using libSBML inside taverna for collaborative model development (Peter Li et al). Another automated (definition of automated in this context?) workflow goes from microarray to pathways and published abstracts. Their consortium wants to exchange information from public data sources, SysMO itself, and excel spreadsheets.

(Another personal aside. FuGE (object model for experimental metadata) and ISA-TAB (tabular format, e.g. spreadsheets) are becoming interchangeable – work is going on between FuGE and ISA-TAB people right now – most recent workshop was last week. This is important, as it was mentioned that bioinformaticians have to deal with spreadsheets (which is true enough!). So, you get the best of both worlds with FuGE / ISA-TAB, without having to define yet another schema. A personal question would be: Why build these various metadata schemas and parsers for spreadsheets (e.g. whatever is used for the Assets catalogue and JERM parsing of spreadsheets) rather than use pre-existing models such as FuGE and formats such as ISA-TAB? Using the FuGE object model does not mean that you have to use all aspects of it – you can just take what you need.Perhaps it was due to the maturity of ISA-TAB at the time the project started, though the specification is now in version 1.0. Will SysMO-DB export and import these formats? There was no time for questions at the end of the talk, so I will try to find out during the lunch period. End aside.)

Trying to map to the relevant MIBBI standard. There is a nice feature that reads spreadsheets from specific locations and automatically loads them into the Assets catalogue. (You can still load them directly into that catalogue.) They are performing a 4-site JERM exchange pilot scheme in Spring 2009.

Great talk – thanks 🙂

These are just my notes and are not guaranteed to be correct. Please feel free to let me know about any errors, which are all my fault and not the fault of the speaker. 🙂

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Adding informative metadata to bioinformatics services

ResearchBlogging.org

Carole Goble and the other authors of “Data curation + process curation = data integration + science” have written a paper on the importance of curating not just the services used in bioinformatics, but also how they are used. Just as more and more biologists are becoming convinced of the importance of storing and annotating their data in a common format, so should bioinformaticians take a little of their own medicine and ensure that the services they produce and use are annotated properly. I personally feel that it is just as important to ensure that in silico work is properly curated as it is in the more traditional, wet-lab biological fields.

They mention a common feature of web services and workflows: namely, that they are generally badly documented. Just as the majority of programmers leave it until the last possible minute to comment their code (if they comment at all!), so also are many web services annotated very sparsely, and not necessarily in a way that is useful to either humans or computers. I remember that my first experience with C code was trying to take over a bunch of code written by a C genius, who had but one flaw: a complete lack of commenting. Yes, I learnt a lot about writing efficient C code from his files, but it took me many hours more than it would have done if there had been comments in there!

They touch briefly on how semantic web services (SWS) could help, e.g. using formats such as OWL-S and SAWSDL. I recently read an article in the Journal of Biomedical Informatics (Garcia-Sanchez et al. 2008, citation at the end of the paper) that had a good introduction to both semantic web services and, to a lesser extent, multi-agent systems that could autonomously interact with such services. While the Goble et al. paper did not go into as much detail as the Garcia-Sanchez paper did on this point, it was nice to learn a little more about what was going on in the bioinformatics word with respect to SWS.

Their summary of the pitfalls to be aware of due to the lack of curated processes was good, as was their review of currently-existing catalogues and workflow and WS aggregators. The term “Web 2.0” was used, in my opinion correctly, but I was once again left with the feeling that I haven’t seen a good definition of what Web 2.0 is. I must hear it talked about every day, and haven’t come across any better definition than Tim O’Reilly’s. Does anyone reading this want to share their “favorite” definition? This isn’t a failing of this paper – more of my own lack of understanding. It’s a bit like trying to define “gene” (this is my favorite) or “systems biology” succinctly and in a way that pleases most people – it’s a very difficult undertaking! Another thing I would have liked to have seen in this paper, but which probably wasn’t suitable for the granularity level at which this paper was written, is a description and short analysis of the traffic and usage stats for myExperiment. Not a big deal – I’m just curious.

As with anything in standards development, even though there are proposed minimal information guidelines for web services out there (see MIAOWS), the main problem will always be lack of uptake and getting a critical mass (also important in community curation efforts, by the way). In my opinion, a more important consideration for this point is that getting a MIA* guideline to be followed does not guarantee any standard format. All it guarantees is a minimal amount of information to be provided.

They announce the BioCatalogue in the discussion section of this paper, which seems to be a welcome addition to the attempts to get people to annotate and curate their services in a standard way, and store them in a single location. It isn’t up and running yet, but is described in the paper as a web interface to more easily allow people to annotate their WSDL files, whereas previous efforts have mainly focused on the registry aspects. Further information can be associated with these files once they are uploaded to the website. However, I do have some questions about this service. What format is the further information (ontology terms, mappings) stored in? Are the ontology terms somehow put back into the WSDL file? How will information about the running of a WS or workflow be stored, if at all? Does it use a SWS format? I would like to see performances of Bioinformatics workflows stored publicly, just as performances of biological workflows (eg running a microarray experiment) can be. But I suppose many of these questions would be answered once BioCatalogue is in a state suitable for publishing on its own.

In keeping with this idea of storing the applications of in silico protocols and software in a standard format, I’d like to mention one syntax standard that might be of use in storing both descriptions of services and their implementation in specific in silico experiments: FuGE. While it does not currently have the structures required to implement everything mentioned in this paper (such as operational capability and usage/popularity scores) in a completely explicit way, many of the other metadata items that this paper suggests can already be stored within the FuGE object model (e.g. provenance, curation provenance, and functional capability). Further, FuGE is built as a model that can easily be extended. There is no reason why we cannot, for example, build a variety of Web services protocols and software within the FuGE structure. One downside of this method would be that information would be stored in the FuGE objects (e.g. a FuGE database or XML file) and not in the WSDL or Taverna workflow file. Further, there is no way to “execute” FuGE XML files, as there is with taverna files or WSs. However, if your in silico experiment is stored in FuGE, you immediately have your computational data stored in a format that can also store all of the wet-lab information, protocols, and applications of the protocols. The integration of your analyses with your wet-lab metadata would be immediate.

In conclusion, this paper presents a summary of a vital area of bioinformatics research: how, in order to aid data integration, it is imperative that we annotate not just wet-lab data and how they were generated, but also our in silico data and how they were generated. Imagine storing your web services in BioCatalogue and then sharing your entire experimental workflows, data and metadata with other bioinformaticians quickly and easily (perhaps using FuGE to integrate in silico analyses with wet-lab metadata, producing a full experimental metadata file that stores all the work of an experiment from test tube to final analysis).

Goble C, Stevens R, Hull D, Wolstencroft K, Lopez R. (2008). Data curation + process curation=data integration + science. Briefings in bioinformatics DOI: 19060304

F GARCIASANCHEZ, J FERNANDEZBREIS, R VALENCIAGARCIA, J GOMEZ, R MARTINEZBEJAR (2008). Combining Semantic Web technologies with Multi-Agent Systems for integrated access to biological resources Journal of Biomedical Informatics, 41 (5), 848-859 DOI: 10.1016/j.jbi.2008.05.007

FuGE / ISA-TAB Workshop, Day 1

Today was the first day of the workshop – back at the good old EBI, though it isn't as recognizable as it used to be. Sure, there is the new EBI extension, but I am used to that now. However, they're renovating the inside of the old EBI building as well, reducing many of my friends to portakabin living over the winter months: better them than me!

Today definitely had an emphasis on the "work" part of "workshop". While a large part of the work on the XSLT for converting between FuGE and ISA-TAB is complete, some of the slightly stickier areas of the conversion are still being worked on. We spent today on trying to iron out some of the difficulties that arise from trying to convert the sort of rich tree structure that you get from the XML implementation of FuGE (FuGE-ML) into the flatter tabular format of ISA-TAB. Below are some of the more general ideas that we were throwing around as a result. (Some are more directly related to the conversion process than others – but all raise interesting points to me.)

  • One of the column names in the ISA-TAB Assay file is currently named "Raw Data File" in the 1.0 Specification. This caused a large amount of discussion as to what "raw" meant, and that many people would have a different idea of what a raw data file was. It was originally named this way to act as a foil against another (optional) column name, "Derived Data File". However, derived data files have a more precise definition in ISA-TAB – such a column can only be used to name files resulting from data transformations or processing. In the end, we are considering a name change, from "Raw Data File" to "Data File".
  • In the end, there will be a few simple ways to format your FuGE-ML files in a way that will aid the conversion into ISA-TAB. It would be useful to eventually produce a set of guidelines to aid in interoperability.
  • Some of the developers already using FuGE (myself included) are using the <Description> element within a FuGE-ML file as a way to allow our biologists to give a free-text description to both materials and data files. There is no specific element in these objects to add such information, and therefore the generic Description element is the best location. This isn't exactly as per FuGE best-practices, where the default Description elements are really only meant for private comments within a local FuGE implementation, and can normally be ignored by external bioinformaticians making use of your FuGE-ML. Such material and data descriptions can be copied into the ISA-TAB file as free text within the Comment[] columns, where what sits within the "[]" is the material or data identifier. We'll have to see if this idea turns out to be useful.
  • The main challenge in collapsing FuGE-ML into ISA-TAB is ensuring that the multi-level protocol application structures (for more information, see the GenericProtocolApplication and GenericProtocol objects within the FuGE Object Model) are correctly converted. We spent the majority of today trying to figure out an elegant way of doing this. We'll work on it again tomorrow, and will hopefully have a new version of the XSLT with a first-bash solution tomorrow evening!

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