Reading the Tea Leaves of 2011 – Data and Technology Predictions for the Year Ahead

The beginning of a new year usually affords the opportunity to join in the predication game and to think about which topics will not only be on our radar screens on the next year, but may dominate it. I couldn’t help myself but to attempt to do the same in my particular line of work – if for no other reason, than to see how wrong I was when I will look at this again at the beginning of 2012. Here are what I think will be at least some of the big technology and data topics in 2011:

1. Big, big, big Data
2010 has been an extraordinary year when it comes to data availability. Traditional big data producers such as biology continue to generate vast amounts of sequencing and other data. Government data is pouring in from countries all over the world, be it here in the United Kingdom, in the United States and efforts to liberate and obtain government data are also starting in other countries. The Linked Open Data Cloud is growing steadily:

Linked Open Data October 2007 - Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/

Linked Open Data September 2010 - Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/

the current linked data cloud has about 20 billion triples in it. Britain now has, thanks to the Open Knowledge Foundation, an open bibliography. The Guardian’s Datastore is a wonderful example of a commercial company making data available. The New York Times is making an annotated corpus available. Twitter and other user-generated content also provide significant data firehoses from which one can drink and build interesting mashups and applications, such as Ben Marsh’s UK Snow Map. So that are just some examples of big data and there are several issues associated with it, that will occupy us in 2011.

2. Curation and Scalability
A lot of this big data we are talking about is “real-world” and messy. There is no nice underlying ontological model (the stuff that I am so fond of) and by necessity it is exceptionally noisy. Extracting a signal out of clean data is hard enough, but getting one out of messy data requires a great deal of effort and an even greater deal of care. And therefore the development of curation tools and methodologies will continue to be high up on the agenda of the data scientist. The development of both automated and social curation tools will be high up on the agenda. And yes, I do believe that this effort is going to become a lot more social – there are signs of this starting to happen everywhere.
However, we are now generating so much data, that the sheer amount is starting to outstrip our ability to compute it – and therefore scalability will become an issue. The fact that service providers such as Amazon are offering Cluster GPU Instances as part of the EC2 offering is highly significant in this respect. MapReduce technologies seem to be extremely popular in “Web 2.0” companies and the Hadoop ecosystem is growing extremely fast – and the ability to “make Hadoop your bitch” as an acquaintance of mine recently put it, seems to be an in-demand skill at the moment and I think for the forseeable future. And – needless to say – successful automated curation of big data,, too, requires scalable computing.

3. Discovery
Having a lot of datasets available to play with is wonderful, but what if nobody knows they are there. Even in science, it is still much much harder to discover datasets than ought to be the case. And even once you have found what you may have been looking for, it is hard to decide whether that really was what you were looking for – describing metadata is often extremely poor or not available. There is currently little collaboration between information and data providers. Data marketplaces such as Infochimps, Factual, Public Datasets on Amazon AWS or the Talis Connected Commons (to name but a few) are springing up, but there is a lot of work to do still. And is it just me or is science – the very people whose primary product is data and knowledge – is lagging far behind in developing these market places. Maybe they will develop as part of a change in the scholarly pulication landscape (journals such as Open Research Computation have a chance of leading the way here), but it is too early to tell. The increasing availablity of data will push this topic further onto the agenda in 2011.

4. An Impassioned Plea for Small Data
One thing, that will unfortunately not be on the agenda much is small data. Of course it won’t matter to you when you do stuff either at web scale or if you are someone working in Genomics. However, looking at my past existence as a laboratory-based chemist in an academic lab, a significant amount of valuable data is being produced by the lone research student who is the only one working on his project or by a small research group in a much larger department. Although there is a trend to large-scale projects in academia and away from individual small grants, small-scale data production on small scale research projects is still the reality in a significant number laboratories the world over. And the only time, this data will get published, is as a mangled PDF document in some journal supplementary – and as such is dead. And sometimes it is perfectly good data, which never gets published at all: in my previous woworkplace we found that our in-house crystallographer was sitting on several thousand structures, which were perfectly good and publishable, but had, for various reasons, never been published. And usually it is data that has been produced at great cost to both the funder as well as the student. Now small data like this is not sexy per se. But if you manage to collect lots of small data from lots of small laboratories, it becomes big data. So my plea would simply be not to forget small data, to build systems, which collect, curate and publish it and make it available to the world. It’ll be harder to convince both funders and institutions and often researchers to engage with it. But please let’s not forget it – it’s valuable.

Enough soothsaying for one blog post. But let’s get the discussion going – what are your data and technology predictions for 2011?

Visualisation of Ontologies and Large Scale Graphs

{{en|A phylogenetic tree of life, showing the ...
Image via Wikipedia

For a whole number of reasons, I am currently looking into the visualisation of large-scale graphs and ontologies and to that end, I have made some notes concerning tools and concepts which might be useful for others. Here they are:

Visualisation by Node-Link and Tree

jOWL: jQuery Plugin for the navigation and visualisation of OWL ontologies and RDFS documents. Visualisations mainly as trees, navigation bars.

OntoViz: Plugin into Protege…at the moment supports Protege 3.4 and doesn’t seem to work with Protege 4.

IsaViz: Much the same as OntoViz really. Last stable version 2004 and does not seem to see active development.

NeOn Toolkit: The Neon toolkit also has some visualisation capability, but not independent of the editor. Under active development with a growing user base.

OntoTrack: OntoTrack is a graphical OWL editor and as such has visualisation capabilities. Meager though and it does not seem to be supported or developed anymore either…the current version seems about 5 years old.

Cone Trees: Cone trees are three-dimensional extensions of 2D tree structures and have been designed to allow for a greater amount odf information to be visualised and navigated. Not found any software for download at the moment, but the idea is so interesting that we should bear it in mind. Examples are here, here and the key reference is Robertson, George G. and Mackinlay, Jock D. and Card, Stuart K., Cone Trees: animated 3D visualizations of hierarchical information, CHI ’91: Proceedings of the SIGCHI conference on Human factors in computing systems, 1991, ISBN = 0-89791-383-3, pp.189-194. (DOI here)

PhyloWidget: PhyloWidget is software for the visualisation of phylogenetic trees, but should be repurposable for ontology trees. Javascript – so appropriate for websites. Student project as part of the Phyloinformatics Summer of Code 2007.

The JavaScript Information Visualization Toolkit: Extremely pretty JS toolkit for the visualisation of graphs etc…..Dynamic and interactive visualisations too…just pretty. Have spent some time hacking with it and I am becoming a fan.

Welkin: Standalone application for the visualisation of RDF graphs. Allows dynamic filtering, colour coding of resources etc…

Three-Dimensional Visualisation

Ontosphere3D: Visualisation of ontologies on 3D spheres. Does not seem to be supported anymore and requires Java 3D, which is just a bad nightmare in itself.

Cone Trees (see above) with their extension of Disc Trees (for an example of disc trees, see here

3D Hyperbolic Tree as exemplified by the Walrus software. Originally developed for website visualisation, results in stunnign images. Not under active development anymore, but source code available for download.

Cytoscape: The 1000 pound gorilla in the room of large-scale graph visualization. There are several plugins available for interaction with the Gene Ontology, such as BiNGO and ClueGO. Both tools consider the ontologies as annotation rather than a knowledgebase of its own and can be used for the identification of GO terms, which are overrepresented in a cluster/network. In terms of visualisation of ontologies themselves, there is there is the RDFScape plugin, which can visualize ontologies.

Zoomable Visualisations

Jamabalaya – Protege Plugin, but can also run as a browser applet. Uses Shrimp to visualise class hierarchies in ontologies and arrows between boxes to represent relationships.

CropCircles (link is to the paper describing it): CropCircles have been implemented in the SWOOP ontology editor which is not under active development anymore, but where the source code is available.

Information Landscapes – again, no software, just papers.

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Exploring Chemical Space with GDB – Jean Louis Raymond (University of Bern)

Three molecules. This image was originally upl...
Image via Wikipedia

(These are live notes from a talk Prof Reymond gave at EBI today)

The GDB Database

GDB = Generated Database (of Molecules)

The Chemical Universe Project – how many small molecules are possible?

GDB was put together by starting from graphs –  in this case the graphs were hydrocarbons and used GENG software to elaborate all possible graphs (after predefining which graphs are chemically reasonable and incorporating bonding informatation etc.) Then place atoms, enumerate, get combinatorial explosion of compounds and apply filters to remove chemical immpossibility: result couple of billion compounds.

 

Some choices restricting diversity: no allenes, no DB at bridgeheads etc, problematic heteroatom constellations (did not consider peroxides), hydrolytically labile functional groups.

In general – number of possible molecules increases exponentially with increasing number of nodes.

Showing that the molecular diversity increases with linear open carbon skeletons – cyclic graphs have fewer substitution possibilities. Chiral compounds offer more diversity than non-chiral ones.

 

GDB Website

 

Now talking about GDB13:

removed fluorine, introduced sulphur, filtered for molecules with “too many” heteroatoms – due to synthetic difficulties and the fact they may be of lesser interest to medchem.

Now showing statistical analysis of molecular types in GDB. 95% of all marketed drugs violate at least two Lipinski Rules. All molecules in the GDB13 are Lipinski conformant.

Use case: take known drug and find isomers. Aspirin has approx 180 compounds similar to Aspirin by Tanimoto score > 0.7 similarity. Points out that any of these molecules may not have been imagined by chemists.

 

GDB15 is just out – corrected some bugs, eliminated enol ethers (due to quick hydrolysis), optimized CPU usage…approx 26 billion molecules, 1.4 Tb – counting them takes a day)

 

Applications of the Database – mainly GDB 11

Use case: Glutamatergic Synapse Binding

used Bayesian classifier trained with known actives and then used that to retrieve about 11000 molecules from GDB11. This was followed by high throughput docking – selected 22 compounds for lab testing. Enrichment of glycine-containing compounds. Now showing some activity data for selected compounds.

Use case: Glutamate Transporter: applied certain structural selection criteria to database molecules to obtain a subset of approx 250 k compounds. Again followed by HT docking. Now showing syntheses of some selected candidate structures together with screening data.

 

“Molecular Quantum Numbers”

Classification system for large compound databases. Draws analogy to periodic table: classification system for elements. We do not have something like this for molecules. Define features for molecules: atom types, bond types, polarity, topology……42 categories in total. Now examines ZINC database against these features: can show that there are common features for molecules occupying similar categories.PCA analysis: first 2 PCs cover 70% of diversity space: first PC includes molecular weight…2D representations considered to be acceptable. PCA also shows nice grouping of molecules by number of cycles

Same analysis for GDB 11: first PCs now mainly account for molecular flexibility, polarity (doesn’t contain many rings due to atom limitation).

Analysis for PubChem – difficult to discover information at the moment.

Was on the cover of ChemMedChem this November.

Shows examples of fishing our structural motive analogies for given molecular motives.

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SWAT4LS2009 – Keynote Alan Ruttenberg: Semantic Web Technology to Support Studying the Relation of HLA Structure Variation to Disease

(These are live-blogging notes from Alan’s keynote…so don’t expect any coherent text….use them as bullt points to follow the gist of the argument.)

The Science Commons:

  • a project of the Creative Commons
  • 6 people
  • CC specializes CC to science
  • information discovery and re-use
  • establish legal clarity around data sharing and encourage automated attribution and provenance

Semantic Web for Biologist because it maximizes value o scientific work by removing repeat experimentation.

ImmPort Semantic Integration Feasibility Project

  • Immport is an immunology database and analysis portal
  • Goals:metaanalysis
  • Question: how can ontology help data integration for data from many sources

Using semantics to help integrate sequence features of HLA with disorders
Challenges:

  • Curation of sequence features
  • Linking to disorders
  • Associating allele sequences with peptide structures with nomenclature with secondary structure with human phenotype etc etc etc…

Talks about elements of representation

  • pdb structures translated into ontology-bases respresentations
  • canonical MHC molecule instances constructed from IMGT
  • relate each residue in pdb to the canonical residue if exists
  • use existing ontologies
  • contact points between peptide and other chains computed using JMOL following IMGT. Represented as relation between residue instances.
  • Structural features have fiat parts

Connecting Allele Names to Disease Names

  • use papers as join factors: papers mention both disease and allele – noisy
  • use regex and rewrites applied to titles and abstracts to fish out links between diseases and alleles

Correspondence of molecules with allele structures is difficult.

  • use blast to fiind closest allele match between pdb and allele sequence
  • every pdb and allele residue has URI
  • relate matching molecules
  • relate each allele residue to the canonical allele
  • annotate various residoes with various coordinate systems

This creates massive map that can be navigated and queried. Example queries:

  • What autoimmune diseases can de indexed against a given allele?
  • What are the variant residues at a position?
  • Classification of amino acids
  • Show alleles perturned at contacts of 1AGB

Summary of Progress to Date:
Elements of Approach in Place: Structure, Variation, transfer of annotation via alignment, information extraction from literature etc…

Nuts and Bolts:

  • Primary source
  • Local copy of souce
  • Scripts transforms to RDF
  • Exports RDF Bundles
  • Get selected RDF Bundles and load into triple store
  • Parsers generate in memory structures (python, java)
  • Template files are instructions to fomat these into owl
  • Modeling is iteratively refined by editiing templates
  • RDF loaded into Neurocommons, some amount of reasoning

RDFHerd package management for data

neurocommons.org/bundles

Can we reduce the burden of data integration?

  • Too many people are doing data integration – wasting effort
  • Use web as platform
  • Too many ontologies…here’s the social pressure again

Challenges

  • have lawyers bless every bit of data integration
  • reasoning over triple stores
  • SPARQL over HTTP
  • Understand and exploit ontology and reasoning
  • Grow a software ecosystem like Firefox
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Tomorrow’s Giants 2 – Dataset Comparison, Data Sharing and Future Literatures

Following my first post from last week, here are more questions that the Royal Society wanted us Cambridge researchers to discuss during the peparatory Tomorrow’s Giant’s Meeting in Cambridge.

How can – and is it appropriate to – facilitate inter-laboratory dataset comparison?
Great that the question was asked. And the answer is yes of course it is. Not only is it appropriate, it is the vey essence of scientific endeavour. What else could be called science? That said, the fact that the question even had to be asked and that the answer is not self evident is disappointing. What has science/have scientists lost by way of attitude/ethics etc. that makes us even ask that question? Yes admittedly, there may be commercial reasons as to why this sort of comparison is not desirable. One of the participants in the session was at great pains to point out that there is often commercial interest tied up to data which prevents sharing and re-use and that is a fair point. However, over the past couple of years I have sat through far too many presentations where the presenter got up and talked about the development of a proprietary model/machine learning tool using a proprietary dataset and proprietary software. Now that is NOT science – at best it is a piece of local engineering which solves a particular problem for the presenter, but it does not advance human knowledge at all. I,, as a fellow scientist, could not pick up any aspect of this work and build upon it as it is all proprietary. Local engineering at best.

Does the type of data have an impact on the ways it can be shared?
Flippantly speaking: “you betcha”. Again, great that the question was even asked. And the answer is multifaceted because the question can be read in a number of different ways. It could be read as “does the provenance of the data and context in which it was generated have an impact on the ways in which it can be shared?” The question can also be read as “Does the (technical) format the data is in have an impact on the way in which it can be shared? The answer in both cases is yes. Let’s tackle these two in turn. One of the participants of the workshop worked at the faculty of education and her primary research data consisted of a large collection of interviews she had conducted with children over the course of her work. She believes that this data is valuable to other researchers in her field and would dearly love to share – but finds herself in a mire of legal and ethical concerns with respect to, for example, the children’s privacy that effectively prevent her from data sharing. So yes, the context in which data is produced and the type of data that is generated can be an obstacle to sharing. If “type of data” is understood to mean “format” then the answer is also yes. A number of my colleagues have pointed out (see here, for example) the data loss that occurs when documents containing scientific data are converted from the format in which they were produced to pdf (examples are here, here and here). The production of data in vernacular or lossy dataformats obviously also have an impact on data sharing – particularly when the sharing and exchange format is lossy.
However, the fact that the question had to be asked at all and that it went straight over the heads of most scientists who were at the meeting and who do not work in the data business, is intensely disappointing. Laboratory researchers have no appreciation of what they are doing when they convert their Word documents to pdf. Data science and informatics are not part of the standard curriculum in the education of scientists – something that desperately needs to change if data loss due to ignorance in data handling is to be avoided in the future.

Future literatures in the wider sense i.e. not just how findings are published in journals, but how can interim findings be shared and accessed?
That is a great question and one, as it turns out, that many of the people present in the meeting had pondered themselves in one form or another already. Scientists should not only be assessed on the basis of the journal articles they write, but, for example, also on the (raw) data they publish. However, science has, so far, not only not evolved a technical soloution to the data publication problem (of course, there isn’t just one solution – there are many depending on the type of data as well as the specific subject/sub-subject/sub-sub-subject that is producing the data etc.) Interim findings are part of this and systems like Nature Preceedings could point the way (although even Nature Preceedings does not allow us to deal with data). Obviously, one has to be careful that these do not just become dumping grounds for lower quality science. Once we have evolved technical solutions for publishing data, the next step will be to develop an ecosystem of metrics. And those metrics should only extend to things like data quality, trust and data provenance. Data “usefulness” – e.g. things like citation indices etc for data should, I think, not be part of the mix: it is impossible to predict what data will be useful when and under which circumstances (and incidentally it is the same for papers). In that sense, data usefulness can be as flighty as fashion and should not be a criterion.

There were a few more questions – and I will blog about these in a future post.

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Tomorrow’s Giants 1 – Big Data

I recently spent an afternoon at a meeting entitled “Tomorrow’s Giants”, which was jointly organized by the Royal Society and Nature and took place here in Cambridge. The meeting was in preparation for a larger meeting, also entitled “Tomorrow’s Giants” which is to be held on the 1st July 2010 as part of the Royal Society’s 350th anniversary celebrations. The purpose of the larger event will be to bring together scientists and politicians in an effort to gather scientist’s visions for the next 5 decades and to ask questions such as

  • What will be required to enable academic achievement in the future?
  • What are the main goals and challenges facing science in the future?

In discussing this, funding considerations were to be left to one side. This is interesting, considering that the current fashion and move towards larger and larger platform grants has profound implications for some of the questions the Royal Society and nature wanted to debate.

As part of the preparatory Cambridge meeting, the Royal Society and Nature had singled out four questions they whished us to debate:

  • “Database Management”
  • “Science Organisation”
  • “Metrics”
  • “Career Security and Support”

For historical and other reasons, readers of this blog will not be surprised to know that my personal interests are centered on scientific data and I shall therefore spend a few blogposts on the question of scientific data, that we were asked to debate. In this context, “Database Management” was a very unfortunate name for a vastly important topic which had all to do how science handles its data in the future. The questions that were asked were: (a) Managing big data – what is the right infrastructure for data sharing, (b) is “big data more of a concern for some disciplines rather than others (e.g. biologists), (c) how can – and is it appropriate to – facilitate inter-laboratory dataset comparison (d) does the type of data have an impact on the ways it can be shared? (d) future literatures in the wider sense i.e. not just how findings are published in journals, but how can interim findings be shared and accessed? (e) what about the tension between transparency and data protection (f) implications for the growing use of web2.0 as a resource for sharing research findings and (g) how well organised is the current use of web 2.0 and how does this impact accessibility?

These were all wonderful questions which must be asked in order to “future-proof” science and to which we were expected to provide answers in 20 min (!). While I was and am glad that we were to debate these issues, the devil is – as always – in the detail and the undifferentiated nature of asking made might heart sink again.

In this post, I would like to address the first two questions:

Managing big data – what is the right infrastructure for sharing
The Good: What is exciting here is the recognition by the RS that data needs infrastructure. And that infrastructure is both technical as well as sociocultural problem. Some components of that infrastructure (and by far not all) that are direly needed are

  • Data Repositories (departmental, university level, subject-specific and transinstitutional
  • Open, non-propriatary and standards-based markup (exchange formats)
  • Computable Metadata (e.g. ontologies which can be used to give data COMPUTABLE meaning
  • University librarians who think that preservation of the data generated by one’s own instritution falls WITHIN the remit of the library
  • Scholarly Societies who remember that they were founded in response to a scaling problem – namely the increasing availability of scientific data and the need to distribute it – and who start taking this reason for their existence seriously again rather than trying to lock up data in inaccessible and copyrighted/DRM’ed/pdf’ed publications
  • Academics who belive that data science should be a compulsory part of every undergraduate’s course
  • Funding agencies who mandate open access publishing and data sharing as a condition of the award of a grant
  • The availability and use of appropriate data licences, such as Creative Commons licences or Open Knowledge Foundation Licences

etc etc…..I am sure there are many more things that I should mention here and that I have forgotten. Come to think it: funding bodies and universities – don’t forget about or squeeze out the infrastructure guys. Don’t say to the infrastructure guys that the development of /institutional repositories/markup languages/models/eScience tools is not science but it engineering and has no place in a research university that “does science”. Do you detect bitterness? Yes you do – some of my colleagues – even those that call themselves “chemoinformaticians” tell me just this on a regular basis. Only thing is – without the infrastructure guys and the engineers that develop all of this stuff and develop it in a scientific manner using scientific methods, NO science will get done because there will be no infrastructure to support it. And which buttons will you push then to calculate your transition states, dock your molecules etc.? Yes – data needs infrastructure…now universities, senior academics and funding bodies….put your money and your recongnition where your mouth is.
The Bad:The focus of the question on BIG data perturbs me immensely. Because BIG data is, well, BIG data, one of the first things that people who produce/manage/exchange BIG data have to do – almost by the very nature of the thing – is to worry about infrastructure for BIG data. And while we may not have all the technical answers just yet (e.g. it is sad in a way that the fastest bandwidth we have for shuffling really BIG data, such as produced by astronomers around the world, for example, is to load it onto hard disks and to load these onto trucks and to send the trucks on their way) people who deal in BIG data are very aware that it needs infrastructure and hardly need convincing. It is not BIG data that is the problem. What is the problem, is data that is produced in the “bog-standard” long-tail research group of between 3 and 20 people. It is these guys, who usually DO NOT (unless they happen to be blessed and are biologists) have the infrastructure to make data available in such a way that it can be stored exchanged and re-used. It is the biology/chemistry/physics…PhD student that has slaved for three years to assemble data and keeps it an Excel spreadsheet that we need to worry about – how do we make it possible for him to publish his data and make it reusable? How about the departmental crystallographer who sits on thousands of publication-quality but unpublished crystal structures just because the compound never quite made it into a paper. We need to develop mechanisms and infrastucture for the small “long-tail” laboratory scientists…the big data guys have this figured out anyway.

Is Big Data more of a concern for some disciplines rather than others (e.g. biologists)?
The GoodYes of course it is. High throughput screening/ gene sequencing/radioastrononmy produce huge amount of data. Yes it is a concern for them – but they are thinking about it already.
The Bad Big data again. See above – it is not about Big data…let’s talk about the synthetic organic chemistr and the data associated with the 20 compounds he makes over 3 years too, please.

I’ll continue to address some of the other data related questions in other blog posts.

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