Absolut standards: report from the M3-2009 meeting, part 2: signature genes and big science


Some more presentations from the metagenomics, metadata, and metaanalysis (M3) meeting, Stockholm June 27, 2009

Pathway Signature Genes
 Lucas A. Brouwers, Martijn A. Huynen and Bas E. Dutilh
CMBI / NCMLS, Radboud University Nijmegen Medical Centre, The Netherlands

If we take a sample of soil, how can we know whether it is adequate for growing a certain crop? For example, does it have the necessary bacteria to provide the nutrients for that crop from raw compounds in the soil? Or when examining a person with an apparent metabolic disorder, could it be that certain characteristics of  their gut bacteria are causing this? We have already seen this happen with obesity.

Questions like this apply to the functional capacity of the microbes in their habitat. Think about a microbial community as an industrial zone with many factories that can make a range of products, and share each others products as raw material. Some of the byproducts are also products, as well as intermediate assembly stage of what are deemed to be the final products. All these products can be consumed by other microbe species / factories, as well as by plants and animals sharing the habitat.

But when we sample a metagenome, we receive a partial picture of the genes necessary to complete a product or range of products. It’s like receiving a series of partial snapshots of an industrial zone:  suppose we identify a tire factory and a body frame factory. Does this mean they are actually making cars in that industrial zone? Or maybe just certain vehicle parts?

Lucas Brouwers presented a really cool idea: how to detect the existence of pathways in metagenomic data given this partial information.  His reasoning was as follows: certain metabolic pathways — the factories that make compounds necessary for sustaining life —  have signature genes. If these genes exist, then there is a high probability the entire pathway exists. He determined which are the signature genes by examining many bacterial genomes and finding which genes indicate the presence of whole metabolic pathways, and estimating the probability for that. If we use the factory analogy, that would be the equivalent of carefully studying many industrial zones, and determining, for example that 90% of the industrial zones that have both a body frame shop and a tire shop will also make whole cars.  So when we fly quickly by an unknown industrial zone and see that these two factory exist, there is a 90% probability that this zone also makes whole cars as well. After looking at many bacterial genomes and the homologous genes that constitute their pathways, Brouwers and his colleagues built a statistical model to determine the probability of pathways in the metagenomic sequence data, provided certain signature genes are detected.

Jeffrey Grethe: Standards in the Context of a Large Scale Microbial Ecology Cyberinfrastructure
Jeffrey Grethe
Center for Research in Biological Systems, University of California San Diego

(Full disclosure: Jeff is my boss, at least for another couple of weeks before I move on to other things).  🙂

Jeffrey Grethe talked about using standards in the CAMERA project. CAMERA, like megx.net, MG-RAST and IMG/M, aims to be a serve the needs of the microbial ecology research community by creating a data repository and a bioinformatics tools resource for metagenomic analysis. Jeff discussed the use of standards in CAMERA, which is working with the Genomics Standards Consortium. Specifically, he showed some examples of the upcoming Geographic database that will enable queries and information on metagenomes, and the data input system that mandates the use of community standards when putting in the data. So for example, when someone would like to compare metagenomes from environments that have high temperature and salinity, CAMERA can help retrieve those using simple queries.

Dutilh, B., Snel, B., Ettema, T., & Huynen, M. (2008). Signature Genes as a Phylogenomic Tool Molecular Biology and Evolution, 25 (8), 1659-1667 DOI: 10.1093/molbev/msn115

Seshadri, R., Kravitz, S., Smarr, L., Gilna, P., & Frazier, M. (2007). CAMERA: A Community Resource for Metagenomics PLoS Biology, 5 (3) DOI: 10.1371/journal.pbio.0050075

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