Aim MIAME/Env aims to define a set of guidelines for the capture

and exchange of microarray data, common to most environmental genomic experiments.

Motivation

Environmental genomics is a diverse, heterogeneous discipline, often involving multi-factorial experiments that can have an almost infinite number of experimental parameters.

Data of this nature is inherently more complex than that

generated by other disciplines.

The Natural Environment Research Council[1] (NERC) has made a commitment to making MIAME compliance a de-facto standard within the Environmental Genomics Science Programme.

MIAME/Env – Background MIAME/Env defines a set of common categories typically used in conjunction to describe an environmental genomics experiment:

Wild / Laboratory Organisms Natural / Controlled Environment Communities / Populations / Individuals Pre-conditioning / Conditioning

For example, three frequently occuring types of experimental design are defined below:

Field Trials – Experiments in which a wild organism/biosource is taken directly from the natural environment, samples and extracts are prepared and the hybridisations then performed.

Conditioned field trials – Experiments in which a wild organism/biosource is taken from the natural environment and conditioned in the lab under specified animal husbandry conditions (preconditioning) and where “treatments” (conditioning) may be performed pre- or post-sample/extract preparation

Lab experiments – Experiments in which an organism/biosource is lab reared or obtained from a standard provider and where “treatments” (conditioning) are usually performed pre- or post-sample/extract preparation.

MIAME/Env – Overview MIAME/Env is based on the MIAME/Tox[2] draft proposed by the EMBL-EBI, NIEHS National Center for Toxicogenomics (NCT) and ILSI-HESI. As with MIAME[3] and MIAME/Tox, MIAME/Env has two major sections:

Array design description. This section remains virtually identical to the MIAME and MIAME/Tox documents.

Experiment description. The major changes to this section are detailed below.

Note: The focus of examples presented here is on those from environmental genomics. However, they are not intended to be fully comprehensive. Authors are encouraged to refer to the MGED ontology for a more exhaustive list when providing specific instances.

Experiment description This section is common to all hybridizations performed in the environmental genomic experiment, such as the goal, brief description, experimental factors tested.

Authors, laboratory, contact Type of the experiment. For instance:

o challenged vs. unchallenged o geographical location comparison o fitness o competition o lab vs. field comparison o strain/line or ecotype comparison

Experimental factors, For instance:

o Species, strain, ecotype o Morphology, behaviour, fitness o Housing, feeding regime, native location o Treatment, dose, vehicle

Biological materials used, extract preparation and labelling Biosource properties

This section is especially important for experiments such as field studies where an organism is taken directly from the “wild” for experimentation. In this case as much detail as possible should be provided in the sample source location and environmental history sections:

Organism (NCBI taxonomy, if known. Where the identity of the organism is unknown or a mixed population is under study a unique identifier plus description should be provided.)

Sample source provider Sample source location (If the biological material is taken from

the “wild”, a GPS coordinate and associated time stamp, to indicate where and when the organism was harvested, should be provided. For some samples a more detailed topography may also be necessary)

Environmental history, if known. Information on the environmental history of the biosource prior to sample manipulation. Potentially, including growth conditions and any pertinent environmental parameters.

Descriptors relevant to the particular sample (if known). For instance:

o sex o age o weights o morphology (size, colour etc) o organism behavioural characteristics o fitness o development stage (of the organism or the host

organism) o animal/plant strain, line or ecotype o genetic variation (e.g., gene knockout, transgenic

variation) o individual genetic characteristics (e.g., disease alleles,

polymorphisms) o disease state or normal o an individual identifier (for interrelation of the biological

materials in the experiment)

Sample manipulations: laboratory protocols and relevant parameters. Pre-conditioning This section attempts to capture the animal husbandry or cell culture conditions that are common to all samples within the experiment. For field studies this may be irrelevant or sparsely populated.

Organism husbandry and housing details or cell culture conditions. For example:

o Housing details o Feeding/cleaning regime o Growth conditions o Pre-Conditioning period (i.e. length of time)

Conditioning (Either field or lab based). MIAME/Env classifies any change to the animal husbandry regime detailed above as a treatment or conditioning. Where conditioning is performed in uncontrolled “field” environments, details of the environmental parameters found at the conditioning site should be recorded in a similar fashion to the environmental history captured in biosource properties (section 2.1)

Exposure to chemical stressors. For example: o Complex mixtures. Such as: Environmental Air / Water

Samples. o Compound / Small molecule. Including information on:

Treatment compound name, Type of compound, CASRN, dose (and unit).

Exposure to physical stressors. For example:

o Electromagnetic radiation. Including information on: Type, Wavelength, Duration, Intensity.

o Pressure (including depth or height) o Heat / Cold – shock. Including information on: Duration,

Temperature. Exposure to biological stressors. For example:

o Parasitic infection o Introduction of competing organism/s

Perturbation. For example, changes to: o Housing and or animal husbandry o Organism density o Circadian-rhythmicity o Tidal-cycle

Sample Processing

Separation technique: Of tissues or cells from a heterogeneous sample. Or of a specific organism sub-sample from a mixed population. For example:

o For tissues and cells: trimming, microdissection, FACS, other (including description/protocol)

o For populations: size exclusion, centrifugation, manual separation (including description/protocol)

Method of sacrifice. Including information on: o Date, time

Making use of MIAME/Env The NERC – Environmental Genomics Thematic Programme Data Centre[4] (EGTDC) is committed to delivering a combination of open-source and commercial bioinformatics packages to EG awardees. It does this through Bio-Linux, an integrated, bioinformatics-centred, computer system. PEDRo Available as a stand-alone package or as part of the Bio-Linux system, PEDRo[6] is an application that produces data entry forms for a data model specified in a particular style of XML-Schema.

An XML based MIAME/ Env model is currently under development for use with PEDRo.

Makes full use of PEDRO’s ability to integrate with multiple ontology services, such as the MGED ontology[5].

Some of the benefits of using PEDRo are:

It is an XML editor that changes its data entry forms in response to changes in a data model.

Domain independence; PEDRo doesn't care if your model is about immunology, proteomics, botany or your record collection.

The application is FREE and open-source in accordance with a generous license.

References [1] NERC – http://www.nerc.ac.uk [2] MIAME/Tox – http://www.mged.org/MIAME1.1-DenverDraft.doc [3] MIAME – http://www.mged.org/Workgroups/MIAME/miame_1.1.html [4] EDTDC – http://envgen.nox.ac.uk [5] MGED Society: Ontology – http://www.mged.org/ontology [6] PEDRo – http://pedro.man.ac.uk

MIAME/EnvMIAME/EnvMinimum Information About a Microarray Experiment – MIAME for Environmental Minimum Information About a Microarray Experiment – MIAME for Environmental

GenomicsGenomics Norman Morrison, Luke Hakes, Andy Brass, Andrew Cossins, Dawn Field, Kevin Garwood, Andy Hayes, Matthew Hegarty, Peter Kille, Karim Nashar, Helen Parkinson, Susanna Sansonne, Robert Stevens, Bela Tiwari, Mike Waters, Joe Wood,

Jason Snape. All members of the MIAME/Env Working Group – See: http://envgen.nox.ac.uk

Bio-Linux.Bio-Linux. By By providing both providing both standard favourite standard favourite and cutting edge and cutting edge bioinformatics tools bioinformatics tools on a Linux-based on a Linux-based system, it combines system, it combines the benefits of being the benefits of being powerful, powerful, configurable, and configurable, and easily updateable, easily updateable, with the ease of use with the ease of use and potential for and potential for software integration software integration required for the required for the handling and handling and analysis of biological analysis of biological data being produced data being produced by EG-funded labsby EG-funded labs

PEDRo.PEDRo. Making Making use of PEDRos use of PEDRos ability to integrate ability to integrate with multiple with multiple ontology services.ontology services.

Here the experiment Here the experiment type field can be type field can be seen anchored to seen anchored to the appropriate the appropriate section in the MGED section in the MGED ontology.ontology.

For further information and to keep up to date with current developments, please visit the MIAME/Env website: For further information and to keep up to date with current developments, please visit the MIAME/Env website: http://envgen.nox.ac.ukhttp://envgen.nox.ac.uk