March 13, 2007

External Review Draft

U.S. Environmental Protection Agency

DRAFT

Interim Guidance

for Microarray-Based Assays:

Data Submission, Quality, Analysis, Management, and Training
Considerations

Prepared for the U.S. Environmental Protection Agency

by Members of the Genomics Workgroup,

a Group Tasked by EPA’s Science Policy Council

Science Policy Council

U.S. Environmental Protection Agency

Washington, DC 20460

NOTICE

This document is an External Review draft.  It has not been formally
released by the U.S. Environmental Protection Agency and should not at
this stage be construed to represent Agency position

DISCLAIMER

This draft interim guidance, when finalized, will represent EPA’s
current thinking on this topic.  It does not create or confer any legal
rights for or on any person or operate to bind the public.  The use of
any mandatory language in this document is intended to describe laws of
nature, scientific principles, or technical requirements and is not
intended to impose any legally enforceable rights or obligations.
Alternative approaches may be used if the approach satisfies the
requirements of the applicable statutes and regulations.  If you would
like to discuss an alternative approach (you are not required to do so),
you may contact the EPA staff responsible for implementing this
guidance.  Mention of trade names or commercial products does not
constitute endorsement of recommendation for use.

Note: This is an external review draft, and is not approved for final
publication.

Genomics Microarray WorkgroupCo-Chairs

Science Policy Council Staff

Jeremy Johnson

(2004)

Subgroup Co-Chairs

Additional Coordinating Committee Members

Wafa Harrouk, US FDA

Lee Hofmann, OSWER

Robert Kavlock, ORD

Rita Schoeny, OW

Genomics Workgroup Lead for the Science Policy Council

Larry Reiter

Office of Research and DevelopmentGenomics Microarray Workgroup Members

Gregory Akerman, OPPTS

Wenjun Bao, ORD

David Bencic, ORD

Lynn Bradley, OEI

Kevin Cavanaugh, ORD

Barbara Collins, ORD

Brion Cook, OPPTS

Don Delker, ORD

Michelle Embry, OPPTS

Robin Gonzalez, OEI

Susan Griffin, Region 8

Stephanie Harris, Region 10

Belinda Hawkins, ORD

Kenneth Haymes, OPPTS

Michael Hemmer, ORD

Todd Holdermann, OPPTS

Gene Hsu, ORD

Margo Hunt, OEI

Sid Hunter, ORD

Channa Keshava, ORD

Steven Kueberuwa, OW

Mitch Kostich, ORD

Richard Leukroth, OPPTS

Nancy McCarroll, OPPTS

Jesse Meiller, OPPTS

Elizabeth Mendez, OPPTS

Ann Miracle, ORD

Ines Pagan, ORD

Santhini Ramasamy, OPPTS

Ann Richard, ORD

Mitch Rosen, ORD

Phil Sayre, OPPTS

Judy Schmid, ORD

John Sykes, ORD

Freshteh Toghrol, OPPTS

Mark Townsend, OPPTS

Nancy Wentworth, OEI

Lori White, ORD

Witold Winnik, ORD

Steve Young, OEIAdditional Genomics Resources

Genomics Training Workgroup Members

Barbara Abbott, ORD

Gilberto Alvarez, Region 5

Michele Burgess, OSWER

Michelle Embry, OPPTS

Audrey Galizia, ORD

Karen Hamernik, OPPTS

Steven Kueberuwa, OW

David Lattier, ORD

David Lee, ORD

Roseanne Lorenzana, Region 10

Marian Olsen, Region 2

Jennifer Seed, OPPTS

Microbial Source Tracking Workgroup Members

Bobbye Smith, Region 9

Jafrul Hasan, OW

James Goodrich, ORD

Rita Schoeny, OW

Robin Oshiro, OW

Roland Hemmett, Region 2

Sally Gutierrez, ORD

Table of Contents

  TOC \o "1-2" \h \z \u    HYPERLINK \l "_Toc131395364"  ACRONYMS
PAGEREF _Toc131395364 \h  viii

  HYPERLINK \l "_Toc131395365"  EXECUTIVE SUMMARY	  PAGEREF
_Toc131395365 \h  1

  HYPERLINK \l "_Toc131395366"  1.0	Introduction	  PAGEREF _Toc131395366
\h  5

  HYPERLINK \l "_Toc131395367"  1.1	Background	  PAGEREF _Toc131395367
\h  5

  HYPERLINK \l "_Toc131395368"  1.2	Overview of Genomic Science
PAGEREF _Toc131395368 \h  6

  HYPERLINK \l "_Toc131395369"  1.3	Emerging Impacts of Genomics
Technologies	  PAGEREF _Toc131395369 \h  8

  HYPERLINK \l "_Toc131395370"  1.4	Purpose and Intent of this Document
 PAGEREF _Toc131395370 \h  11

  HYPERLINK \l "_Toc131395371"  2.0	The Performance Approach to Quality
Assurance for Microarrays	  PAGEREF _Toc131395371 \h  12

  HYPERLINK \l "_Toc131395372"  3.0	Data Submission Guidance	  PAGEREF
_Toc131395372 \h  14

  HYPERLINK \l "_Toc131395373"  3.1	Introduction	  PAGEREF _Toc131395373
\h  14

  HYPERLINK \l "_Toc131395374"  3.2	Abstract	  PAGEREF _Toc131395374 \h
14

  HYPERLINK \l "_Toc131395375"  3.3	Experimental Design	  PAGEREF
_Toc131395375 \h  15

  HYPERLINK \l "_Toc131395376"  3.4	Array Design	  PAGEREF _Toc131395376
\h  16

  HYPERLINK \l "_Toc131395377"  3.5	Biomaterials	  PAGEREF _Toc131395377
\h  16

  HYPERLINK \l "_Toc131395378"  3.6	Hybridization	  PAGEREF
_Toc131395378 \h  17

  HYPERLINK \l "_Toc131395379"  3.7	Measurements	  PAGEREF _Toc131395379
\h  17

  HYPERLINK \l "_Toc131395380"  4.0	Data Analysis Guidance	  PAGEREF
_Toc131395380 \h  19

  HYPERLINK \l "_Toc131395381"  4.1	Introduction	  PAGEREF _Toc131395381
\h  19

  HYPERLINK \l "_Toc131395382"  4.2	Data Analysis	  PAGEREF
_Toc131395382 \h  20

  HYPERLINK \l "_Toc131395383"  4.3	Data Evaluation	  PAGEREF
_Toc131395383 \h  23

  HYPERLINK \l "_Toc131395384"  4.4	Data Analysis Conclusions	  PAGEREF
_Toc131395384 \h  24

  HYPERLINK \l "_Toc131395385"  5.0	Data Management	  PAGEREF
_Toc131395385 \h  25

  HYPERLINK \l "_Toc131395386"  6.0  Recommendations	  PAGEREF
_Toc131395386 \h  28

  HYPERLINK \l "_Toc131395387"  6.1	Training Needs and Recommendations
PAGEREF _Toc131395387 \h  28

  HYPERLINK \l "_Toc131395388"  6.2	Collaborative Development of Genomic
Tools for Data Analysis and Data Management	  PAGEREF _Toc131395388 \h
31

  HYPERLINK \l "_Toc131395389"  6.3	Applying this Interim Guidance for
Microarray-Based Assays to Case Studies	  PAGEREF _Toc131395389 \h  32

  HYPERLINK \l "_Toc131395390"  6.4	Updating Genomics Guidance as Needed
  PAGEREF _Toc131395390 \h  32

  HYPERLINK \l "_Toc131395391"  References	  PAGEREF _Toc131395391 \h
33

  HYPERLINK \l "_Toc131395392"  Appendix A:  EPA Quality System and the
Performance Approach to Quality Measurement Systems	  PAGEREF
_Toc131395392 \h  35

  HYPERLINK \l "_Toc131395393"  APPENDIX B:  MIAME-Based Data Submission
Tables	  PAGEREF _Toc131395393 \h  51

  HYPERLINK \l "_Toc131395394"  Table B.1  Abstract	  PAGEREF
_Toc131395394 \h  51

  HYPERLINK \l "_Toc131395395"  Table B.2  Experimental Design	  PAGEREF
_Toc131395395 \h  51

  HYPERLINK \l "_Toc131395396"  Table B.3  Array Design	  PAGEREF
_Toc131395396 \h  53

  HYPERLINK \l "_Toc131395397"  Table B.4  Biomaterials	  PAGEREF
_Toc131395397 \h  56

  HYPERLINK \l "_Toc131395398"  Table B.5  Hybridization	  PAGEREF
_Toc131395398 \h  61

  HYPERLINK \l "_Toc131395399"  Table B.6  Measurements	  PAGEREF
_Toc131395399 \h  62

  HYPERLINK \l "_Toc131395400"  Appendix C:  Genomics Data Evaluation
Record (gDER) Template	  PAGEREF _Toc131395400 \h  64

  HYPERLINK \l "_Toc131395401"  Appendix D:  Genomics Data Evaluation
Record (gDER) for Alachlor (Sample)	  PAGEREF _Toc131395401 \h  67

  HYPERLINK \l "_Toc131395402"  Appendix E: MIAME Glossary	  PAGEREF
_Toc131395402 \h  77

  HYPERLINK \l "_Toc131395403"  Appendix F:  Additional Glossary from
Genomics White Paper	  PAGEREF _Toc131395403 \h  82

  HYPERLINK \l "_Toc131395404"  Appendix G:  Content and Instructional
Goals for the Three Levels of Genomics Technical  Training:	  PAGEREF
_Toc131395404 \h  88

ACRONYMS

CBI 		Confidential Business Information

cDNA 		Complementary Deoxyribonucleic Acid

CEBS		Chemical Effects in Biological Systems knowledgebase

cRNA		Complementary Ribonucleic Acid

CWA		Clean Water Act

DER		Data Evaluation Record

DNA		Deoxyribonucleic Acid

DQO		Data Quality Objective

EPA		Environmental Protection Agency

FACS		Fluorescence Activated Cell Sorter

FDA		Food and Drug Administration

FNR		False Negative Rate

FPR 		False Positive Rate

gDER		Genomics Data Evaluation Record

HPV		High Production Volume

IRB		Institutional Review Board

IVT 		In Vitro Transcription

JPEG		Joint Photographic Experts Group

MAGE		Microarray And Gene Expression

MAGE-OM 	Microarray And Gene Expression - Object Model

MGED		Microarray Gene Expression Data

MIAME	Minimal Information About Microarray Experiments

MOA		Mode of Action

MOPS-EDTA 	[MOPS] 3-(N-Morpholino) propanesulfonic acid],

		[EDTA] ethylenediaminetetraacetic acid

MPSS		Massively Parallel Signature Sequencing

mRNA		Messenger RNA

MQO		Measurement Quality Objective

MST		Microbial Source Tracking

NHEERL	National Health and Environmental Effects Research Laboratory

NIEHS		National Institute of Environmental Health Sciences

NPDES	National Pollutant Discharge Elimination System

OEI		Office of Environmental Information

OPPTS	Office of Prevention, Pesticides and Toxic Substances

ORD		Office of Research and Development

OSWER	Office of Solid Waste and Emergency Response

OW		Office of Water

PCR		Polymerase Chain Reaction

PMN		Pre-Manufacture Notification

PMT		Photomultiplier Tube

QA		Quality Assurance

QAARWP	Quality Assurance Annual Report and Work Plan

QC		Quality Control

QMP		Quality Management Plan

qPCR 		Quantitative Polymerase Chain Reaction

qRT-PCR	Quantitative Reverse Transcriptase PCR

RFU		Relative Fluorescent Unit

RNA		Ribonucleic Acid

RNase		Ribonuclease

RTP		Research Triangle Park

RT-PCR	Reverse-Transcription Polymerase Chain Reaction

SAGE	 	Serial Analysis of Gene Expression

SNP		Single Nucleotide Polymorphism

SOPs		Standard Operating Procedures

SPC		Science Policy Council

TIFF		Tagged Image File Format

TMDL		Total Maximum Daily Load

U.S. EPA	U.S. Environmental Protection Agency

EXECUTIVE SUMMARY

	The mapping of diverse animal, plant, and microbial species genomes
using molecular technologies has significantly affected research across
all areas of the life sciences.  The current understanding of biological
systems is rapidly changing in ways previously unimagined and novel
applications of this technology have already been commercialized.  These
advances in genomics will have significant implications for risk
assessment policies and regulatory decision making.  In 2002, the U.S.
Environmental Protection Agency (EPA or “the Agency”) issued its
Interim Policy on Genomics (U.S. EPA, 2002a) that communicated the
Agency’s initial approach to using genomics information in risk
assessment and decision making.  The Interim Policy described genomics
as the study of all the genes of a cell or tissue, at the DNA
(genotype), mRNA (transcriptome), or protein (proteome) level.  While
noting that the understanding of genomics is far from established, the
Agency stated that such data may be considered in the decision making
process, but that these data alone are insufficient as a basis for
decisions.

	  SEQ CHAPTER \h \r 1 Following the release of the Interim Policy, the
Science Policy Council (SPC) created a cross-EPA Genomics Task Force and
charged it with examining the broader implications genomics is likely to
have on Agency programs and policies.  The Genomics Task Force developed
a Genomics White Paper entitled “Potential Implications of Genomics
for Regulatory and Risk Assessment Applications at EPA” (U.S. EPA,
2004).  That document identified four areas likely to be influenced by
the generation of genomics information within EPA and the submission of
such information to EPA: 1) prioritization of contaminants and
contaminated sites, 2) monitoring, 3) reporting provisions; and 4) risk
assessment.  One critical need in the area of technical development was
identified: the need to establish a framework for analysis and
acceptance criteria for genomics information for scientific and
regulatory purposes.  The Task Force recommended that the Agency charge
a workgroup to establish such a framework and in doing so consider the
performance of assays across genomic platforms (e.g., reproducibility,
sensitivity, pathway analysis tools) and the criteria for accepting
genomics data for use in a risk assessment (e.g., assay validity,
biologically meaningful response).

	In 2004, the Genomics Technical Framework and Training Workgroup was
formed with the responsibility to ensure that the technical framework
and training activities build upon the Agency’s Interim Policy on
Genomics while continuing to engage other interested parties.
Information developed by these workgroups will be used by EPA program
offices and regions to determine the applicability of specific genomics
information to the evaluation of risks under various statutes.

	To this end, the Genomics Technical Workgroup considered all of the
“omics” technologies and applications and decided that an interim
guidance document on the use of data generated by DNA microarray
technology would be most beneficial to the Agency and regulated
community at this time.  Consequently, this document provides
recommendations regarding: 1) data that should be considered for
submission to the Agency for microarray studies, 2) the use of a
performance approach to microarray quality assessment parameters, 3)
data analysis approaches for microarrays, and 4) data management and
storage issues for microarray data submitted to or used by the Agency.
The guidance applies to both human health and ecological DNA microarray
data.

With respect to experimental performance considerations, the Genomics
Workgroup concluded that quality issues are critical considerations in
the application of new technologies such as genomics.  The Genomics
Workgroup recommends that the Agency not prescribe specific methods to
be used in microarray experiments at this time, but instead provide
general guidance on the recommended performance of microarray
experiments in order to obtain data of the quality required for a
specific use; this guidance is provided herein.  Investigators
submitting data to the Agency in support of regulatory decision making,
methods development, and technical transfer, may want to consider, in
addition to compliance with MIAME (Minimal Information About Microarray
Experiments) Workgroup standards (  HYPERLINK
"http://www.mged.org/Workgroups/MIAME/miame.html"
http://www.mged.org/Workgroups/MIAME/miame.html ), the
performance-related experimental and system factors outlined in this
document (Appendix A).  Further activities on the part of investigators
to address experimental performance issues will serve to strengthen
scientific arguments and experimental claims.

	This document also provides information regarding submission of
microarray data to EPA to ensure appropriate review and consistent
evaluation of data from multiple sources.  In accordance with accepted
practice, it is recommended that submissions include sufficient
information to allow an independent reviewer to reconstruct how the data
were collected and analyzed.  This approach allows reviewers to judge
the quality of the data and the strength of any conclusions.  Many
scientific journal editors grappling with these issues have adopted the
MIAME guidelines as a standard for submission of microarray data as part
of a submitted publication.  A slightly modified version of MIAME is
proposed as the microarray data submission template for EPA; this
submission template will be subject to change as the technology evolves.

	With regard to data analysis, the Genomics Workgroup concluded that a
systematic approach for genomics data evaluation is necessary for the
further use of such data in risk assessments.  A genomics Data
Evaluation Record template is provided herein as a way to present and
organize data from genomics studies in order to derive information
necessary for a regulatory application (see Appendix C for the Genomics
Data Evaluation Record [DER]).  A completed sample DER is also provided
in Appendix D to facilitate the use of the template.  An overview of
issues to be considered in analyzing microarray data is also provided.
The transfer of these evaluations, and the underlying genomics data,
into searchable, electronic databases will be essential to making the
data useful in risk assessments.  Furthermore, development of databases
containing gene expression profiles for a wide variety of chemicals
should facilitate creation of statistical/computational methods that
will help predict the toxic potential of a chemical.

	Due to potentially large volumes of genomic and associated
toxicological data, it is essential that the Agency consider the
development of a complete data management solution.  The functional
needs of a solution of this magnitude would minimally include items
listed in the section on data management.  In addition, this Agency data
management solution should address needs unique to scientifically-based
risk assessments, confidential and proprietary data security, public
access, and other aspects of regulatory application.  It should be noted
that consistency, scientific and operational robustness, common access,
and availability in a scalable environment are data management needs for
an Agency data management solution.  While the Agency has begun to
utilize bioinformatics research approaches, both intramurally (e.g., the
National Center for Computational Toxicology in EPA’s Office of
Research and Development [ORD]) and extramurally (Environmental
Bioinformatics Centers in North Carolina and New Jersey funded by
EPA’s Science to Achieve Results (STAR) Program), an Agency-wide data
management solution integrating genomics, toxicological, and other key
data required for regulatory applications is now necessary.

	The document concludes with the Genomics Workgroup’s  recommendations
to the Agency for follow-up activities to this interim guidance
including: 1) further development of the outlined training materials and
modules, to be offered throughout the Agency to risk assessors and
decision makers who will be faced with the challenge of interpreting and
applying genomics information, 2) continued collaboration of EPA
personnel with staff from other federal agencies and stakeholders in the
development of tools for the analysis of genomics data, 3) application
of this guidance to a series of case studies to evaluate its utility in
risk assessment and regulatory applications; and 4) the updating of this
guidance as needed as the technology evolves.

	This document is intended to provide information to the regulated
community and other interested parties regarding submitting microarray
data to the Agency and to provide guidance for EPA reviewers in
evaluating such data and/or information.  This interim guidance can be
used by EPA program offices to determine the applicability of specific
genomics information to the evaluation of chemical risks.

1.0	Introduction

1.1	Background

	The mapping of diverse animal, plant, and microbial species genomes
using molecular technologies has significantly affected research across
all areas of the life sciences.  The current understanding of biological
systems is rapidly changing in ways previously unimagined and novel
applications of this technology have already been commercialized.  These
scientific and technological advances have spurred many federal agencies
to consider the far-reaching implications for policy, regulation, and
society as a whole.

	In 2002, EPA released the Interim Policy on Genomics (U.S. EPA, 2002a)
communicating its initial approach to using genomics information in risk
assessment and decision making (  HYPERLINK
"http://www.epa.gov/osa/spc/genomics.htm"
http://www.epa.gov/osa/spc/genomics.htm ).  This policy describes
genomics as the study of all the genes of a cell or tissue, at the DNA
(genotype), mRNA (transcriptome), or protein (proteome) level.  The
Interim Policy notes that while genomics offers the opportunity to
understand how an organism responds at the gene expression level to
stressors in the environment, understanding such molecular events with
respect to adverse ecological and/or human health outcomes is far from
established.  This policy states that while genomics data may be
considered in the decision making process at this time, these data alone
are insufficient as a basis for decisions.  Consequently, currently EPA
will only consider genomics information for assessment purposes on a
case-by-case basis.

	  SEQ CHAPTER \h \r 1 Following the release of the Interim Policy, the
Science Policy Council (SPC) created a cross-EPA Genomics Task Force and
charged it with examining the broader implications genomics is likely to
have on Agency programs and policies.  To that end, the Genomics Task
Force developed a Genomics White Paper entitled “Potential
Implications of Genomics for Regulatory and Risk Assessment Applications
at EPA” (USEPA, 2004,   HYPERLINK
"http://www.epa.gov/osa/genomics.htm"  www.epa.gov/osa/genomics.htm ).
The Task Force identified scenarios to describe various circumstances
under which EPA might receive these data.  Four areas were identified as
those likely to be influenced by the generation of genomics information
within EPA and the submission of such information to EPA: 1)
prioritization of contaminants and contaminated sites, 2) monitoring, 3)
reporting provisions; and 4) risk assessment.  The Task Force also
identified several challenges and/or critical needs that included
research, technical development, and capacity (i.e., strategic hiring
practices and training).

	The Genomics Task Force recommended that the Agency charge a workgroup
with developing a technical framework for analysis and acceptance
criteria for genomics information for scientific and regulatory
purposes.  The Genomics White Paper identified issues that need to be
considered in developing such a framework including the performance of
assays across genomic platforms (e.g., reproducibility,  sensitivity,
pathway analysis tools) and the criteria for accepting genomics data for
use in a risk assessment (e.g., assay validity, biologically meaningful
response).

	In June, 2004, the Genomics Technical Framework and Training Workgroup
was established with representatives from ORD, numerous program offices
(OPPTS, OSWER, OW, OEI, OPEI) and regional offices (2, 3, 5, 7, 8, and
9).  The Genomics Workgroup was comprised of a Coordinating Committee,
several technical genomics guidance workgroups (Performance Approach
Quality Assurance Workgroup, Data Submission Workgroup, Data Analysis
Workgroup, and a Data Management and Storage Workgroup), a Training
Workgroup, and a Microbial Source Tracking Workgroup.  The Genomics
Workgroup’s responsibility was to ensure that the technical framework
and training activities build upon the Agency’s Interim Policy on
Genomics while continuing to engage other interested parties.  This
document will be used by EPA program offices and regions to determine
the applicability of specific genomics information to the evaluation of
risks under various statutes.

1.2	Overview of Genomic Science

	As a means of introduction to genomics and its potential impact on
regulatory decision making, it is important to understand the basic
principles behind genomic technology.  Only about 1-2% of the human DNA
actually codes for RNA that can be translated into proteins.  This 1-2%
is considered to be the theoretical functional genome.  Any particular
cell type (i.e., from various organs or species) will have its own
practical functional genome, which is a subset of the entire functional
genome that encodes for functional proteins in that cell.  The
functional genome for any cell type can be assessed by determining the
messenger RNA (mRNA) profile of the cell, tissue, or organ.  The mRNA
copies the necessary portion of the cell’s DNA code and transports
this information to the ribosomes where protein synthesis occurs.  Thus,
the assessment of mRNA profiles is called functional genomics.  Such
profiles are constructed using microarrays that contain all (or a
sampling) of a cell’s functional genome.  Hybridization of a DNA copy
(cDNA) of the mRNA that is being actively produced by the cell to these
microarrays demonstrates which genes are currently active in that cell.
Within the 98-99% of DNA not coding for RNA message is information that
affects the activity of the functional genome by influencing where and
when genes are active in an organism.  Thus both coding and noncoding
DNA are important in organismal function and response to perturbations.

	The study of a cell’s protein composition is called proteomics.
Currently, it is possible to analyze only a fraction of a cell’s
proteins, but rapid advances in this field will allow more complete
profiling in the near future.  Another discipline of biology analyzes
biofluids and tissues to determine the profiles of endogenous
metabolites present under normal conditions or when the organism has
been affected by factors such as exposure to environmental chemicals.
This type of whole cell analysis is called metabolomics (or metabolic
profiling).  In order to understand how a cell functions under normal or
stressed circumstances, it is necessary to characterize the proteins
that are manufactured by the cell, as well as endogenous metabolites.
This facilitates an understanding of global metabolism and how proteins
interact along biochemical pathways.  This approach describes the area
of systems biology, in which the cell, tissue, or organism is considered
as a complete, albeit complex, system.

	Broadly defined, genomics tools provide the means to examine changes in
gene expression, protein, and metabolite profiles within the cells and
tissues, in contrast to current risk assessment methods which are
restricted to whole organism effects or changes in single biochemical
pathways.  Genomics tools have the potential to provide detailed data
about the underlying biochemical mechanisms of disease or toxicity
(i.e., disease etiology, biochemical pathways), sensitive measures of
exposures to chemicals, new approaches to detecting effects of such
exposures, and methods for predicting genetic predispositions that may
possibly lead to disease or higher sensitivity to particular stressors
in the environment.

	Another type of application is chemical identification.  By utilizing
genomic expression profiles it is possible to identify and classify
environmental contaminants.  For example, Hamadeh et al. (2002a,b) found
chemical-specific gene expression profiles in liver tissue of exposed
rats.  The authors demonstrated that 24-hour exposure to compounds from
the same chemical class (peroxisome proliferators) resulted in gene
expression profiles that were unique but more similar to each other than
to patterns corresponding to exposure to a chemical of a different class
(enzyme inducers).  These gene expression profiles were associated with
differences in histopathology between the different chemical classes
following longer durations  These and other published works indicate the
utility of genomic approaches in chemical identification and in
investigations of mode-of-action of chemical hazards.

1.3	Emerging Impacts of Genomics Technologies

	Toxicology has been moving from observation of changes in tissue
histology, physiology, and chemistry to a mechanistic understanding
through assessment of large scale changes of gene activity within those
tissues.  Identification of changes in gene expression using microarrays
is becoming an important tool for informing our understanding of
toxicological processes as well as informing the hazard identification
process and mode of action analysis as part of safety and risk
assessment.  As the price of conducting microarray experiments declines
and an appreciation of their value increases, their use for basic
research and as part of the environmental regulatory process is likely
to increase.

	The use of data generated by microarray technology in peer reviewed
scientific publications has grown exponentially over the last few years.
 Microarray technology allows monitoring of changes in gene expression
across thousands of genes, or even entire genomes or proteomes in
response to experimentally manipulated or natural conditions.  We are
now beginning to understand several important toxicological processes in
terms of changes in the activity of single genes or ensembles of genes
acting in concert.  The identification of these changes is increasingly
the product of the use of microarray technology.  As a result of these
research trends, EPA anticipates receiving increasing volumes of
microarray data from environmental researchers, and as a part of the
regulatory process.  In order to ensure optimal utilization of these
data, EPA has developed this guidance to address the quality,
submission, analysis, and storage of microarray data.

	While many new genomic technologies do exist, most are not as yet ready
for application in risk/safety assessment and decision making.
Therefore, it is important for the Agency to consider how these genomic
technologies might be incorporated into existing programs.  It should be
noted that genomics will not fundamentally alter the risk assessment
process, but is expected to serve as a powerful tool for evaluating the
exposure to and effects of environmental stressors and will offer a
means to simultaneously examine a number of response pathways.  EPA and
other regulatory agencies are beginning to address the use of genomics
data for various risk assessment applications, including the need to
establish a link between genomic alterations and adverse outcomes of
regulatory concern.  Given the rapidly evolving nature of genomics
technologies, care should be taken to develop an acceptable scheme to
simplify and refine the risk-related information and to distinguish it
from the large amount of complex scientific and statistical data
available.  This strategy should remain dynamic and fluid in
anticipation of continuing technical evolution at the molecular levels
(e.g., DNA, RNA, and protein levels).  Furthermore, bioinformatic
approaches for data acquisition and analysis, including technologies
designed to store and analyze the profusion of data generated from
microarray analyses, should be considered in parallel with the data
generating methods.  Finally, many scientific, policy, ethical, and
legal concerns developing along with the emergence of this science will
need to be addressed.

	The Interim Policy on Genomics provides guidance concerning how and
when genomics information should be used to assess the risks of
environmental contaminants under the various regulatory programs
implemented by the Agency at the present time.  The standardization of
experimental design, the selection of informative biomarkers, and data
analysis for genomics is important for the utility of genomics
information in future risk assessment and regulatory decisions.  Such
standardization will enhance the reproducibility of results obtained and
the reliability of conclusions drawn from microarray data.  Furthermore,
EPA is considering the development of data quality standards based on
performance of microarrays, as well as other genomics technologies
(e.g., functional genomics).  This in turn will help to ensure the
integrity of EPA’s approach to assessing the genomics information
submitted to the Agency.

	Genomics issues have already arisen in environmental decision-making.
For example, a pesticide registrant has cited a published genomic
article   SEQ CHAPTER \h \r 1 (Genter et al., 2002) as part of the data
package submission for product registration to EPA’s Office of
Pesticide Programs.  The data were submitted in support of an
alternative mode of action that would affect human health assessment
conclusions.  Similar submissions are quite likely to be made by other
pesticide registrants.

	Although this document focuses on the use of microarrays for
toxicological studies as they pertain to macroorganisms, it should be
noted that the impact of microarray technologies goes beyond the
exploration of toxicological effects in eukaryotic systems.  For
example, the use of microarray techniques in environmental and clinical
microbiology has increased significantly in the last few years.
Microarrays can also be used to screen for host specific markers that
can be used in microbial source tracking (MST).  As an example of the
application of genomics to MST, a research consortium including State of
California regulatory agencies, public utilities, and EPA recently
participated in a study comparing the performance of various
genomics-based methods designed to identify the source of fecal material
in ambient waters in an MST approach (Griffith et al., 2003).  Moreover,
genomics methods are being evaluated to assist dischargers in complying
with Clean Water Act (CWA) requirements to develop Total Maximum Daily
Loads (TMDLs) for water bodies that are listed as impaired due to the
presence of fecal coliforms.  This MST work will also address the issue
of beach closures; current microbial methods require several days to
complete and do not distinguish between bacteria from humans and other
sources such as sea gulls or seals.  Further details on these MST
efforts are described in Microbial Source Tracking Guide Document
(available at:   HYPERLINK
"http://www.epa.gov/ORD/NRMRL/pubs/600r05064/600r05064.htm%20"
http://www.epa.gov/ORD/NRMRL/pubs/600r05064/600r05064.htm  ; U.S. EPA,
2005) .

	These examples indicate the need to make proactive policy decisions and
to develop processes to address how genomics data will be used in Agency
decision-making.

1.4	Purpose and Intent of this Document

	As a result of research trends, EPA anticipates receiving increasing
volumes of microarray data from environmental researchers, and as a part
of the regulatory process.  The Genomics Technical Workgroup considered
all of the “omics” technologies and applications and decided that a
guidance document on the use of data generated by DNA microarray
analysis would be most beneficial to the Agency and regulated community
at this time.  This guidance applies to microarray data relevant to
human health and ecological risk assessment and decision making.  This
guidance is provided in order to facilitate appropriate submission,
consistent review, and optimal utilization of these data.  Consequently,
this document provides recommendations regarding: 1) data that should be
considered for submission to the Agency for microarray studies, 2) the
use of a performance approach to microarray quality assessment
parameters, 3) data analysis approaches for microarrays, and 4) data
management and storage issues for microarray data submitted to or used
by the Agency.

	The purpose of this document is to provide information to the regulated
community and other interested parties regarding submitting microarray
data to the Agency and to provide guidance for reviewers in evaluating
and utilizing such data and/or information.  This interim guidance can
be used by EPA program offices to determine the applicability of
specific genomics information to the evaluation of chemical risks.  It
is important to note that microarray technology is rapidly changing,
such that methodologies for generating such data and ensuring its
quality will likely change; however the need to ensure consistency and
quality in generating, analyzing and using the data will not.  As the
state of the science develops, EPA plans to revisit the guidance as
necessary.

2.0	The Performance Approach to Quality Assurance for Microarrays

Quality issues are critical considerations in the application of new
technologies or approaches, such as genomics.  The Workgroup recommends
that the Agency not prescribe specific methods to be used in microarray
experiments at this time.  This section instead provides general
guidance on the recommended performance of microarray experiments in
order to obtain data of the quality needed for a specific use.

	The Agency acknowledges that continued advancement of tools and
platforms for describing biological phenomena will be pivotal in
supporting claims for regulatory decision making.  It is also noted that
at this time there exist numerous approaches, investigator fabricated
and commercially available platforms, hardware and other peripheral
equipment by which to measure biologic trends and changes at the level
of tissues and cells.  The following technical statements relate
primarily to “expression” measurements (up- and down-regulation of
macromolecules) and certain other multiplex technologies used to
generate and collect quantitative and qualitative data about changing
biologic conditions.  This guidance is also relevant to the evolving
nature of “expression” measures, particularly as recommendations for
standardization in experimental performance put forth by the combined
efforts of academic, industry and government scientists, become
universally accepted and applied.

	Although there are currently numerous means by which to observe and
acquire biological expression measurements, such as Massively Parallel
Signature Sequencing (MPSS) and Serial Analysis of Gene Expression
(SAGE), the most frequently used experimental approach to collecting
expression data is microarray-based studies.  This technology, which has
expanded well beyond the sphere of human health, is exploited to
describe changing transcriptional profiles in genes of countless species
that are important to numerous areas of biological sciences.
Unfortunately, many of these investigations are undertaken without the
benefit of explicit consensus for quality assurance and quality control
and there has yet to be firmly established criteria for
intra-experimental and cross-platform performance evaluation.

Investigators submitting data to the Agency in support of regulatory
decision-making, methods development, and technical transfer, should
also consider at a minimum the performance-related experimental and
system factors outlined in Appendix A, in addition to compliance with
MIAME (Minimal Information About Microarray Experiments) Workgroup
standards (  HYPERLINK "http://www.mged.org/Workgroups/MIAME/miame.html"
 http://www.mged.org/Workgroups/MIAME/miame.html ) discussed in Section
3 below.  Further activities on the part of investigators to address
experimental performance issues will serve to strengthen scientific
arguments and experimental claims.

Each EPA program, regional, or research and development office’s
Quality System should be defined and documented in their Quality
Management Plan (QMP).  A summary of their individual office’s Quality
System activities is detailed in a Quality Assurance Annual Report and
Work Plan (QAARWP), which also includes information on their annual
internal assessment of their Quality System.

Additional detailed discussion of the EPA Quality System and the
performance approach to quality assurance for microarrays is provided in
Appendix A.

3.0	Data Submission Guidance

3.1	Introduction

	EPA developed the following information regarding submission of
microarray data to facilitate appropriate review and consistent
evaluation of data from multiple sources.  The text that follows was
written as a preliminary template guiding the submission of microarray
data to the EPA.  As the state of the science develops, EPA plans to
revisit this submission format as necessary.  In accordance with
accepted practice, it is useful if submissions include sufficient
information to allow an independent reviewer to reconstruct how the data
were collected and analyzed.  This approach allows reviewers to judge
the quality of the data and the strength of any conclusions.  It is also
useful if the submission includes enough information in a format that
facilitates comparison or integration with similar data from other
experiments.

	Microarray technology is rapidly evolving with many competing
platforms, native data formats, and analysis tools.  As a result, a data
submission standard should not be so specific as to stifle flexibility
or innovation.  Similarly, standards should not be burdensome,
discouraging submission or slowing scientific progress.  Many scientific
journal editors grappling with these issues have adopted the Minimal
Information About Microarray Experiments (MIAME) guidelines as a
standard for submission of microarray data as part of a submitted
publication (  HYPERLINK
"http://www.mged.org/Workgroups/MIAME/miame.html"
http://www.mged.org/Workgroups/MIAME/miame.html ).  A slightly modified
version of MIAME, described below in Sections 3.2 through 3.7 and
Appendix B, is proposed as the recommended microarray data submission
template for EPA, which will be subject to change as the technology
evolves.  As genomics science and the associated technologies evolve, it
can be expected that the MIAME guidance will concomitantly evolve.  If
the MIAME guidance in this document conflicts with the most recent
changes to the MIAME guidance, the reader is directed to consider the
MIAME guidance as the most recent, correct version.

3.2	Abstract

An abstract or executive summary of the source and type of data as well
as the type of data evaluation and its final interpretation would
provide a useful introduction to the data submission.  Such a summary
would not need to be exhaustive but would optimally provide the key
highlights so that the reader will know the source of the data and how
it was interpreted.  The abstract might be written in a similar manner
as for the submission to a scientific meeting or a journal article.  It
is advantageous if the reader is able to extract the important features
of the submission and its interpretation from the abstract, although it
is understood that a thorough evaluation of the substance of the data
will involve a review of all the submitted material.

3.3	Experimental Design

It would be beneficial if voluntary submissions of genomics data to EPA
included a sufficient description of the experimental design necessary
to understand the source and nature of the data as well as the materials
used to conduct the research.  The following discussion is not an
exhaustive listing or meant to be complete but indicates the spectrum of
information on the experimental design that might be submitted for
review.  The submitter should consider providing the standard
information one would include in the materials and methods section of
any scientific article including a list of all the endpoints examined in
the study.  Such information would include information about the
biological model system, treatment methods and doses, husbandry of
animals, and cell culture information for in vitro systems.  If whole
animal models were employed, then submission of information regarding
the exposure system, exposure doses, time points, details on euthanasia,
length of time between harvesting of tissues and freezing or other
processing, numbers of samples utilized for DNA array analysis, methods
of RNA processing, and RNA quantification should be considered.  The
submitter should consider providing information on the methods employed
for hybridization and incorporation of label and the numbers of
hybridizations.  When relevant, the submission of additional information
necessary for interpretation of the data should be considered.  Such
information might include reference sample information, sample
amplification, or any additional information unique to the study.  The
submitter should also consider providing information regarding any
problems that arose during the study that could have an impact on
interpretation.

3.4	Array Design

The inclusion of a complete description the platform used for
transcriptional expression analysis such that the reviewer can assess
the appropriateness of the analysis should be considered.  The platform
might be a commercially available platform (e.g., Affymetrix, Agilent,
Clontech) such that reference may be made to the specific type of chip
used and the locations (weblink) of the source of the proprietary
information so that the reviewer may access this information to aid in
the review of the data analysis.  If the transcriptional expression
analysis was derived from a custom array designed for or by the
submitter, then a inclusion of complete description of the production of
the array would be useful.  This information would likely include but
certainly not be limited to the source of the nucleotide sequences used
on the array, how the arrays were prepared, equipment used to prepare
the arrays, description of the slides or membranes on which the arrays
were spotted, gene lists, and any supportive data which confirms the
specificity of the sequences used.  A more complete listing of the types
of data that would be useful in supporting the submission of custom
arrays can be found in Appendix B.

3.5	Biomaterials

It is advantageous if the submitted data package presents the physical
characteristics of the studied biomaterials as these will likely vary
between experiments.  Such characteristics might include age, sex, cell
type/line, and/or genetic variation.  When applicable, this information
would address the biological material from which nucleic acids (or
proteins) have been extracted for subsequent labelling and
hybridization.  It is also recommended that submitted information on
biomaterials detail the source properties, treatment, extract
preparation, and labelling of the sample.  Any pertinent information
about sample controls would also be useful in analyzing submitted data.

The exposure conditions applied to each test organism or tissue are
important parameters influencing the experimental response.  As a
result, it is useful to document the incubation and treatment conditions
applied to the studied biomaterial.  Other key submission information
might include the method of chemical or physical exposure using the
appropriate dosing units.  Furthermore, any processing of samples taking
place after exposure would be of interest.

Information on the hybridization extract preparation protocol might
include such details as the nucleic acid type and amplification method
used.  It would also be useful to record and submit the labeling
materials and technique used in the experiment.  Finally, the data
submitter should consider outlining the type and position on the array
of any external controls that may have been added to the hybridization
extract(s).  Please see Table B.4 in Appendix B for further information.

3.6	Hybridization

It would be usedul to submit a concise description of the procedures
adopted for each hybridization.  If a commercially available platform is
utilized, reference may be made to the specific type of hybridization
procedures and parameters adopted in the experiment.  Web or literature
citations describing the source of the hybridization protocol and
materials are useful.  Furthermore, information regarding the
relationship between the labelled sample extracts and their
corresponding arrays (design, batch and serial number) would be useful
for understanding the experiment.  Documentation of the steps taken in
the hybridization including information regarding the solution, blocking
agent and concentration used, wash procedure, quantity of labelled
target used, time, concentration, volume, temperature, and a description
of the hybridization instruments is encouraged.

3.7	Measurements

The submitter should consider completely describing the methods used to
acquire the image of the array, the nature of the image (e.g., TIFF),
the nature of the extraction of image data into quantified image data,
and the nature of the spreadsheets used to house the quantified data.
Submission of the original TIFF images is encouraged as is the
submission of the initial quantization matrix.  The description of the
spreadsheet normalization of the TIFF data and any subsequent data
analysis is also of value in a submission.  In addition, features of the
data used for analysis such as background correction, normalization
methods, methods used to test usability of the raw data, and types of
analytical approaches would be useful information for the reviewer.
Analytical approaches might include statistical models, graphical
models, image based displays of data, and various analytical software
packages.  Information about the software may include weblink,
proprietary information from instruction manual, or specific description
of custom analytic methods.  More complete description of information
that should be considered for a submission for review may be found in
Appendix B.

Data Analysis Guidance

	This section provides information that will assist in regulatory and
risk assessment efforts when considering the use of genomics data.
Genomics data can be used to aid in reducing the level of uncertainty in
the decision making process and provide a means to further evaluate
exposure and effects.  This guidance effort is also an attempt to
highlight the need for developing genomics data analysis tool criteria,
and the standardization of methods for the use of these tools.

4.1	Introduction

	Evaluation of qualified genomics data, which have been properly
analyzed and submitted (see Sections 2.0 and 3.0), has the potential to
dramatically improve the mechanistic understanding of toxicities and
their relevance to human health and ecological hazard identification and
risk assessments.  For example, DNA microarrays may be used to identify
gene expression profiles associated with exposure to particular
compounds, or characteristic of certain modes of action or mechanisms of
toxicity.  When a correlation has been established between a gene
expression profile and a toxic mechanism, then these genomic data
provide supportive evidence for that mechanism.  Even when the mechanism
for a particular compound is unknown, genomic data can help identify
plausible toxicity pathways that may be involved in the biological
process under study (Crosby et al., 2000) for the purposes of
prioritization or screening.

	Genomic technologies generate vast amounts of data (gigabytes) quickly
(during a single analytical session), especially when using DNA
microarrays for gene expression profiling.  This wealth of data
increases the importance of careful documentation of experimental and
analytical methods while working towards data interpretation and
evaluation.  The Minimal Information for the Analysis of Microarray
Experiments (MIAME) guidelines have helped to standardize DNA microarray
experiment documentation.  Extension of the MIAME guidelines into
toxicogenomics has provided even more applicable prerequisites for
analysis (  HYPERLINK "http://www.mged.org/MIAME1.1-DenverDraft.DOC"
http://www.mged.org/MIAME1.1-DenverDraft.DOC ; Fostel at al., 2005).
Also critical to analysis of genomics, and particularly microarray data,
is access to the raw data from published or submitted experiments, and
accompanying documentation of experimental and analysis details.
Establishment of public genomic databases such as the Gene Expression
Omnibus (GEO,   HYPERLINK "http://www.ncbi.nlm.nih.gov/geo/"
http://www.ncbi.nlm.nih.gov/geo/ ) provides limited access to microarray
data, but these are not compatible with all monitoring or regulatory
applications.

	In addition to data submission and management activities, computational
tools for genomics data analysis are another critical need for routine
application of genomics data.  Although evaluation of many of the
currently available computational tools for genomics data analysis is
underway through multiple internal and external Agency research efforts,
these tools have not been examined by the Agency in sufficient detail
that would allow for specific final recommendations to be made.
Furthermore, while the variability and complexity of microarray
experiments make prescribing a common, all-encompassing protocol
functionally problematic, general components for the successful analysis
and interpretation of all microarray approaches are discussed.  The
Agency is currently participating in several projects designed to
develop appropriate protocols and methods for microarray data analysis.
These include collaborative efforts with Food and Drug Administration
(FDA) on the Microarray Quality Control project (   HYPERLINK
"http://www.fda.gov/nctr/science/centers/toxicoinformatics/maqc/"
http://www.fda.gov/nctr/science/centers/toxicoinformatics/maqc/ ) and
National Institute of Environmental Health Sciences (NIEHS) on the
Chemical Effects in Biological Systems knowledgebase (  HYPERLINK
"http://cebs.niehs.nih.gov/"  http://cebs.niehs.nih.gov/ ).  As an
interim solution a genomics Data Evaluation Record (DER) template
(Appendix C) is proposed as a means to outline a framework for genomics
data analysis and documentation.

4.2	Data Analysis

	A few general features of genomic data analysis areas are described
below with the intent to provide a basic but broad overview.

4.2.1	Data Processing and Filtering

	Data processing covers the steps from scanning the array, to obtaining
reliable estimates for the relative abundance of each gene transcript in
all of the samples.  Generally, these steps are classified as image
analysis, quality control filtering, background correction,
transformation and normalization.  Each hybridized array has an
associated and unique image file from which individual values (pixel
intensities) can be collected.  Data can be filtered to exclude signals
that fail quality criteria.  The specifics of data filtering and the
threshold levels chosen are dependent upon the details and goals of the
experiment.  Standardization of processing and filtering criteria will
be a critical step toward intra- and inter-laboratory agreement.  The
final output of the initial processing will be data that can be analyzed
further to identify differentially-expressed genes.

4.2.2	Statistics

	A standard, or common, statistical approach, that would be appropriate
for all microarray experiments, cannot be specified because of unique
experimental variables such as differences in microarray platforms,
experimental design (reference versus matched), levels of replication
(technical versus biological), as well as within experiment sources of
variation (spot to spot, slide to slide, etc.).  Therefore, the types of
methods and tools used for statistical analyses of microarray results
often differ not only from more traditional experimental approaches, but
also from one microarray experiment to another.  Sample size strongly
affects the statistical method chosen for analysis.  For example, while
a relative balance may exist between the number of samples and data
points measured in a standard non-genomic experiment, microarrays, as
well as proteomic and metabonomic technologies, generate hundreds and
often thousands of data points from each sample.  Furthermore, a variety
of formulae exist to calculate appropriate microarray sample sizes,
depending on experimental design.  Nevertheless, the cost of conducting
such experiments prohibits large scale studies with multiple sample
sizes.  Another constraint is sample pooling, at times a necessity due
to the complex nature and paucity of biological material (i.e., tissues
and/or RNA quantities).  It is, nonetheless, important to recognize that
sample pooling may impact microarray experiments at multiple levels,
including experimental design and subsequent analyses.  Finally, data
replication should be considered.  It is important to distinguish the
two types of replication that exist in biological experiments, including
microarrays: technical (repeats of the same sample) and biological
(starting material from unique sources, such as different animals in a
test group).  For scientifically sound reasons, the latter assumes
greater significance in most biological assays including microarray
experiments.

4.2.3	Interpretation

hierarchical clustering, and κ-means clustering) can be used to better
organize the data and help identify patterns of gene expression.

	Various bioinformatics (mathematical and statistical) algorithms can be
used to integrate these patterns of expression with common biological
pathways and networks of co-regulated genes.  Linking these functional
and pathway analyses to concurrent and previously identified phenotypic
characteristics will significantly advance the understanding of the
biological processes involved along the source-to-outcome continuum.

4.2.4	Inference

	Integration of these various data analyses and interpretation tools can
be used to infer cause and effect relationships from these genomic data
(Freeman, 2005).  Biological inference may lead to biomarker development
as well as descriptions of dose-response relationships, mechanisms of
action, and predictive toxicity.  Biomarkers are recognized as providing
data linking exposure to internal dose and effect. The application of
biomarkers to the risk assessment process that is linked to toxic
processes or mechanisms may provide additional information for risk
assessors.  Additionally, data generated from microarray studies on
model test organisms could be 1) applied to the identification of
susceptible subpopulations, 2) used to develop surrogate species for
toxicity testing, and 3) extrapolated to additional species, once the
biomarkers and mechanism(s) of action are identified.

4.3	Data Evaluation

	The goals of the evaluation of genomics data are directed toward risk
assessment for regulatory applications.  Currently, however, decisions
cannot be made based solely upon gene expression pattern recognition,
according to EPA’s Interim Genomics Policy; this technology has not
yet come to set precedence on its own.  Currently, confirmatory studies
are useful for potential risk assessment and regulatory use.  If the
data generated from microarray assays are confirmed using other
techniques (i.e., real-time quantitative PCR, functional enzyme assays,
protein and metabolite profiles and/or linked to bioassay results),
these data will help support links between gene expression, exposure and
the resulting adverse effects in organisms.  Furthermore, interpretation
of microarray data with respect to existing toxicity profiles and
endpoints of other perhaps higher level tests (clinical chemistry,
immunochemistry, histopathology, and reproductive endpoints) should
significantly increase the diagnostic and predictive applications of
these technologies in the future.

	A genomics Data Evaluation Record is used here as a way to present and
organize data from genomics studies in order to derive information
necessary for a regulatory application (see Appendix C for the Genomics
Data Evaluation Record (DER) Template).  For monitoring applications
such information and standardization is recommended.  The sections of
the DER include the general information about a study and a brief
executive summary as well as the materials and methods used.  The test
performance section includes: treatment and sampling times, tissues and
cells examined, details of tissue harvest and storage, sample
preparation, data analysis, evaluation criteria and statistical
analysis.  The results, discussions and conclusions are also components
of the DER.  Sections of the DER are included to provide example
information to the risk assessor as a means to document the
incorporation of genomics information in the risk assessment process.
Genomic data used to support the more conventional data (e.g., limited
clastogenesis in vitro associated with cytotoxicity, DNA strand breaks,
lipid peroxidation) are presented in an example DER for rats exposed to
alachlor (see Appendix D: Draft Genomics Data Evaluation Record for
Alachlor)

4.4	Data Analysis Conclusions

	The above considerations demonstrate that a systematic approach for
genomics data evaluation is necessary for further use of genomic data in
risk assessment efforts.  Documentation methods, like those in the
proposed genomics DER (Appendix C) can help capture some requisite
information, but the transfer of these evaluations, and the underlying
genomics data, into searchable, electronic databases will be essential
to making the data useful in risk assessments.  Furthermore, development
of databases containing gene expression profiles for a wide variety of
chemicals should facilitate creation of statistical/computational
methods that predict the toxic potential of a chemical.

5.0	Data Management

	The goal of this section is to outline recommendations to EPA for an
approach to managing genomic data submitted to the Agency or developed
internally by EPA scientists.  This includes the need to consider an
Agency-wide warehouse for storage, retrieval and analysis of information
submitted for regulatory or risk assessment purposes.

	There are several major types of needs to consider in addressing the
issue of an EPA-wide database: broad scientific needs for risk
assessment purposes, program-specific regulatory needs, Agency
Information Technology (IT) security needs, and public access needs.
Although there is an overlap of issues for each of these purposes, it is
useful to think of each additional purpose adding another layer of
needs.

	For scientific risk assessment purposes the key needs include the
following items:

Standardization of data inputs as identified by the Data Submission
Workgroup.  This includes both microarray data and experiment parameters
associated with the toxicogenomics study.  It also provides for
electronic submission of data.

Provision of connectivity to external public biological databases such
as Affymetrix, Agilent, and GenBank

A quality control mechanism to ensure the fidelity of entered data

Capability for importing and exporting data by means of automatic
routines

Inclusion of a wide range of data analysis and visualization tools such
as filtering, clustering, and statistical analysis

Sufficient scalability to address large data submissions, many users,
and later addition of metabonomics and proteomics data at times in the
future

Audit trail capability.  This would provide a time line and information
on who added, changed or deleted specific data.  It would also provide
versions prior to deletions and changes.

Automatic data back up and recovery system

For security and management purposes, additional key needs include:

Database hosting, administration and management. This includes managing
data submission, database access and privileges, software and hardware
updates, back-up and storage.

 Physical and electronic security, including user authentication,
firewalls, and virus protection.

Governance structure to provide policies and procedures for submissions,
access, security, cost sharing, and priority for development of new
features.

For regulatory purposes, additional considerations may be necessary:

Electronic signature or other formal identity management capability.  If
the data are submitted electronically as part of a regulatory
submission, the system needs to ensure that the submission is linked to
the submitter.

Capability of partitioning the database to secure Confidential Business
Information (CBI) or other non-public information, if this is part of a
regulatory submission.

Workflow enabled, so that reviewer can address data in systematic steps
needed for response to submission.

For public access purposes, key needs are:

Database is Web enabled, with easy routine for export of data.

Clear policies governing the management of the public database as
opposed to an internal or staging database.

	There may also be staging considerations in building or adopting an
Agency-wide genomic database.  The first phase might include genomic
data only, and have limited analytic capability.  Eventually the
database should provide quality assessment tools, extensive analytical
capability, gene-centric queries, and encompass proteomic, metabonomic,
and conventional toxicology assay results.  Integrating these diverse
types of experimental data will support data mining as well as the
development of predictive toxicology systems.

	Currently, there is no single database at EPA for managing genomics
data each program or lab is developing its own approach.  As the needs
are currently identified above, there are several advantages to creating
and maintaining an EPA-wide genomics database:

Cost.  All of the scientific and management/security needs identified
above should be addressed by any genomic database used at EPA.
Addressing these items once in a uniform way would avoid duplication of
these costs.

Data Access.  All users in the Agency would have access to all Agency
genomic data (except CBI data), greatly enhancing our risk assessment
capabilities.

Quality Control and Consistency.  A quality control mechanism would
ensure that all Agency data passes a consistency test.

Availability of a Common Set of Analysis Tools.  As new tools are
developed, they would become available to all users.

Scaleable.  While lab or program specific databases may focus on a
narrow range of data or analysis, an EPA database would be built to
include a wider range of “omics” data and a full portfolio of
analytical tools enabling Agency scientists to pursue a wider range of
data mining and biological systems-oriented studies.

6.0  Additional Recommendations

	The Genomics Workgroup recommends that the Agency undertake  a number
of follow-up activities to this interim guidance including: 1) further
development of the training materials and modules outlined below, to be
offered throughout the Agency to risk assessors and decision makers who
will be faced with the challenge of interpreting and applying genomics
information, 2) continued collaboration of EPA personnel with staff from
other federal agencies and stakeholders in the development of tools for
the analysis of genomics data, 3) application of this guidance to a
series of case studies to evaluate its utility in risk assessment and
regulatory applications; and 4) the updating of this guidance as needed
as the technology evolves.

6.1	Training Needs and Recommendations

	The charge to the Genomics Task Force Training Workgroup was to develop
an approach and appropriate delivery mechanisms for training Agency risk
assessors and managers to understand and interpret genomics data in the
context of risk assessment.  The need for a better understanding of
molecular biology concepts, and ultimately how genomics, proteomics, and
other “omics” data may be used to support decision making, is the
primary driver for the development of such training for staff and
managers.

	In designing training genomics, the Training Workgroup considered
several issues: 1) the need to develop a modular approach that could
build on basic information and change as new information becomes
available, 2) the need to vary the level of complexity based on the
needs of a particular audience, 3) the importance of considering the
target audience, based on the recognition that different staff and
managers will have different needs, 4) the need to develop a schedule
for production of training materials, recognizing that, by taking
advantage of existing public sector resources to build the initial
version of the Genomics Training, time and resources may be saved; and
5) identifying internal capacity to provide training, such as ORD
scientists and risk assessors to save time and resources.

	Presented below is a draft outline that describes a modular training
course in molecular techniques, in general, and genomics data
interpretation, in particular.  The Genomics Training would consist of
three levels of training targeted to specific audiences, each consisting
of a series of modules devoted to a particular group of concepts and/or
techniques.  Each training level is outlined below in Table 1, with
descriptions of the content and instructional goals.  More detail on the
proposed training is provided in Appendix G.

Table 1. Overview of Genomics Training Plan (see Appendix G for more
detail)

Training Level	Number of Module	Target Audiences	Content	Goal

Level I: Introductory Modules- 	8	Non-scientists and/or technical staff
without training in biological sciences.	Molecular Biology concepts:

cell structure and function, DNA, RNA, proteins, gene arrays, risk
assessment concepts, regulatory and risk assessment communication,
EPA’s current genomics policy.	Provide basic information necessary for
understanding assessments of cellular functions at the molecular level
and how genomics data may affect risk assessments.

Level II: Intermediate Modules	3	Scientists and/or those likely to use
genomics: Intended for staff who need more in-depth understanding of
genomics data generation, but do not necessarily generate data.
Background on molecular techniques such as microarrays, DNA
amplification techniques, DNA fingerprinting, protein analysis, etc.

Modules to be targeted for specific applications. (e.g., microbial
source tracking, homeland security, field inspectors, etc.)	Provide a
general understanding of various applications that may be currently
considered by programs throughout EPA.  Intended to support human health
and ecological risk assessors.

Level III

Advanced Modules

	Dependent on specific technical needs.	Scientists and those likely to
use genomics data to generate risk assessments.	Modules would include
statistical, computational and bioinformatics approaches to analyze
genomic data, the use of molecular biology in mode-of-action
determinations, and using genomics data in hazard/risk assessments.
Flexible to account for changes in the field and to meet needs of the
different EPA programs.  As new technologies/ applications appear,
additional modules developed, enhanced and/or revised. 	Provide
advanced-level knowledge on specific technical needs that scientists
performing research or developing hazard/risk assessments associated
with chemical registrations and other regulatory activities may face.

6.2	Collaborative Development of Genomic Tools for Data Analysis and
Data Management

	The Agency, in concert with other federal agencies, has begun to
investigate and evaluate the currently available computational tools for
genomic data analysis.  EPA has been testing the toxicogenomic data
management and analysis features of the NIEHS Chemical Effects in
Biological Systems (CEBS) knowledgebase and FDA National Center for
Toxicological Research's ArrayTrack database.  EPA has also been
collaborating with FDA, National Institutes of Health (NIH), National
Institute of Standards and Technology (NIST), and other stakeholders on
the microarray quality control (MAQC) project to establish protocols for
genomic data analysis.  Further, EPA has participated in National
Academy of Sciences (NAS) workshops and International Life Sciences
Institute (ILSI) projects on the application of genomics to toxicology
and risk assessment.  Building on these prior efforts, recommendations
on the use of genomics tools should be identified recognizing that the
goal is the appropriate application of genomic data in risk assessments
and regulatory decision making.  The Agency should also consider and
identify limitations of the currently available tools.  Ultimately, the
Agency is looking for quantitative and predictive modeling tools, which
will likely call for the development of new algorithms and models.
These tools will need to provide reliable and repeatable data analyses,
and the consistent and necessary information for EPA decision making
processes.  The scientific, mathematical, and statistical methods that
are used for these models and analyses will need to be validated and
standardized.

	Due to the potentially large volumes of genomic and associated
toxicological data, it is essential that the Agency consider the
development of a complete data management solution.  The functional
needs of a solution of this magnitude should minimally include items
listed in Section 5.0 Data Management.  In addition, this data
management solution should address needs unique to scientifically-based
risk assessments, confidential and proprietary data security, public
access, and other aspects of regulatory application.  It should be noted
that consistency, scientific and operational robustness, common access,
and availability in a scalable environment are important data management
needs.  While the Agency has begun to develop bioinformatics research
efforts, both intramurally (e.g., ORD's National Center for
Computational Toxicology) and extramurally (the STAR funded
Environmental Bioinformatics Center in NC and NJ), an Agency-wide data
management solution integrating genomics, toxicological, and other key
data for regulatory applications is now needed.

6.3	Applying this Interim Guidance for Microarray-Based Assays to Case
Studies to Verify its Utility in Risk Assessment and Regulatory
Applications

	The EPA’s Risk Assessment Forum and other appropriate groups should
apply this interim guidance to several case studies to evaluate its
utility in risk assessment and regulatory applications and to identify
potential areas for improvement.

6.4	Updating Genomics Guidance as Needed

	This interim guidance should be revised and updated as indicated
through its application to case studies (see section 6.3 above), and as
genomics technologies evolve.  Additional genomics guidances (e.g.,
proteomics, metabonomics) should be developed as needed to ensure the
Agency is prepared to receive and apply such data as the need develops.

References

American Public Health Association, American Water Works Association, &
Water Environment Federation.  Standard Methods for the Examination of
Water and Wastewater.  Revision in process.

Brooks, A.N., Pennie, W.D. 2001. Transcript profiling of the response to
environmental hormone mimics. Comments Toxicol 7:303-315.

Burczynski, M.E., McMillian, M., Ciervo, J., Li, L., Parker, J.B., Dunn,
R,T., Hicken, S., et al. 2000. Toxicogenomics-based discrimination of
toxic mechanism in HepG2 human hepatoma cells. Toxicol Sci 58:399-415.

Crosby, L.M., Hyder, K.S., DeAngelo, A.R., Kepler, T.B., Gaskill, R.,
Benavides, G.R., et al. 2000. Morphologic analysis correlates with gene
expression changes in cultured F344 rat mesothelial cells. Toxicol Appl
Pharmacol 189:205-222.

Fostel, J., Choi, D., Zwick, C., Morrison, N., Rashid, A., Hasan, A.,
Bao, W., et al. 2005. Chemical Effects in Biological Systems—Data
Dictionary (CEBS-DD): A Compendium of Terms for the Capture and
Integration of Biological Study Design Description, Conventional
Phenotypes, and ‘Omics Data. Toxicol Sci 88:585–601.

Freeman, M.R., Cinar, B., and Lu, M.L. 2005. Membrane rafts as potential
sites of nongenomic hormonal signaling in prostate cancer. Trends
Endocrinol Metab 16(6):273-9

(Freeman, 2005

  SEQ CHAPTER \h \r 1 Genter, M.B., Burman, D.M., Soundarapandian, V.,
Ebert, C.L., Aronow, B.J. 2002. Genomic analysis of alachlor-induced
oncogenesis in rat olfactory mucosa. Physiol. Genomics 12:35-45.

Hamadeh, H.K, Bushel, P.R., Jayadev, S., Martin K., DiSorbo 0., Sieber
S., et. al. 2002. Gene expression analysis reveals chemical-specific
profiles. Toxicol Sci 67:219-231.

Moreau, Y., Aerts, S., De Moor, B., De Strooper, B., Dabrowski, M.
2003.  Comparison and meta-analysis of microarray data:  from the bench
to the computer desk.  Trends Genetics  19:570-577.

U.S. Environmental Protection Agency. 2005. Microbial Source Tracking
Guide Document. Office of Research and Development, Washington, DC
EPA-600/R-05/064. 131 pp.   HYPERLINK
"http://www.epa.gov/ORD/NRMRL/pubs/600r05064/600r05064.htm"
http://www.epa.gov/ORD/NRMRL/pubs/600r05064/600r05064.htm

U.S. Environmental Protection Agency, Science Policy Council. 2004.
Potential Implications of Genomics for Regulatory and Risk Assessment
Applications at EPA. EPA 100/B-04/002. available at:   HYPERLINK
"www.epa.gov/osa/genomics.htm"  www.epa.gov/osa/genomics.htm

U.S. Environmental Protection Agency, Science Policy Council. 2002a.
Interim Policy on Genomics

U.S. Environmental Protection Agency.  2002b.  NELAC Constitution,
Bylaws and Standards EPA/600/R-03/049.

U.S. Environmental Protection Agency. 1997.  “Performance Based
Measurement System,” 62 Federal Register 52098 – 52100, October 6,
1997.

Waring, J.F., Ciurlionis, R., Jolly, R.A., Heindel, M., Ulrich, R.G.
2001. Microarray analysis of hepatotoxins in vitro reveals a correlation
between gone expression profiles and mechanisms of toxicity. Toxicol
Lett 120:359-368.

Appendix A:  EPA Quality System and the Performance Approach to Quality
Measurement Systems

The best quality data may not be technically available, affordable, or
even applicable to the exact problem at hand.  To address a variety of
circumstances, EPA has developed a Quality System by which reasonable
quality assurance (QA) guidelines or policies are offered for assuring,
documenting, and assessing data quality.  EPA’s Quality System is
defined in EPA Order 5360.1 A2, Policy and Program Requirements for the
Mandatory Agency-Wide Quality System, the EPA Quality Manual for
Environmental Programs, EPA Manual 5360 A1, the Contracts Management
Manual, and the Agency’s Website (  HYPERLINK
"http://www.epa.gov/quality"  www.epa.gov/quality ).  The requirements
for EPA-funded organizations and organizations submitting data to EPA
under applicable statutes and regulations are also found in the Code of
Federal Regulations (48 CFR Part 46), also available through
www.epa.gov/quality.  Parties submitting data under applicable statutes
and regulations are expected to document the quality of the data
submitted as well as how it was achieved.  Quality System parameters
apply to environmental data operations and measurements or information
that describe: (1) environmental processes, (2) location or conditions,
(3) ecological or health effects and consequences; and (4) performance
of environmental technology

What is a Quality System?

	As illustrated in Figure 1, a Quality System is viewed as a tiered
organizational approach for its work processes because it defines how
the work is conducted, and provides a scientific and technical basis for
EPA’s decision making process.  The Quality System is a documented
management structure to ensure the quality of an organization’s work
processes, products and services.  Adhering to the Quality System helps
to ensure that all operations, no matter where they are performed, occur
in a consistent manner and that the processes and outputs in the system
are effective, stable, and consistently followed.  Key components in a
Quality System are:  (1) Quality management, (2) Quality assurance (QA),
and (3) Quality control (QC).

Figure 1.  A Generic Quality System

What Documentation is Needed for Organizations Submitting “Genomics
Data??

	An organization documents its Quality System in a Quality Management
Plan while a laboratory may document its implementation of specific
quality policies and practices in a document entitled a Quality Manual
or Quality Assurance Plan.  However named, the document details the
efforts to produce data that are adequate for their intended use and for
assuring conformity with regulations and customer requirements for data
quality.  Examples of a Quality Management Plan are available at
HYPERLINK "http://www.EPA.gov/QARus/QMPexample"
www.epa.gov/quality/qmps.html .

What is a Performance Approach?

A Performance Approach conveys “what” needs to be accomplished, but
not prescriptively "how" to do it.  EPA defines the performance approach
as a set of processes wherein the data needs, mandates, or limitations
of a program or project are specified, and serve as criteria for
selecting appropriate methods to meet those needs in a cost-effective
manner.  The criteria may be published in regulations, technical
guidance documents, permits, work plans, or enforcement orders.  Under a
performance approach, EPA would specify the questions to be answered,
the decisions to be supported by the data, the level of uncertainty
acceptable for making decisions, and the documentation to be generated
to support this approach (see   HYPERLINK
"http://www.epa.gov/fedrgstr/EPA-WASTE/1997/October/Day-06/f26443.htm"
http://www.epa.gov/fedrgstr/EPA-WASTE/1997/October/Day-06/f26443.htm ,
or 62 FR 52098 for more details about Agency policy regarding the
performance approach)

	Performance approaches can be defined as either: (1) measurement data
that are of specified quality when demonstrating compliance (measurement
quality objective (MQO) approach), or (2) a demonstration of compliance
that achieve specified statistical confidence (the data quality
objective (DQO) approach).  Any appropriate measurement technology and
sampling frequency/thoroughness may be used as long as MQO or DQO is
documented and met.

Key components that need to be considered in a performance approach are:

a)   Sampling procedures and sample acceptance criteria, describing
procedures for collecting, handling (e.g., time and temperature),
accepting, and tracking submitted samples, and procedures for
chain-of-custody.

b)  Analytical methods, listing the laboratory’s scope for testing and
denoting accreditation/certification status for individual methods, for
non-standard methods or new methods, the laboratory’s validation
procedures.

c)  Analytical quality control measures, stating the laboratory’s
requirements for measurement assurance, e.g., method verification and
documentation, error prevention, and analytical checks such as duplicate
analyses, blanks, positive and negative culture controls, sterility
checks, and verification tests.

d)  Documentation control and record keeping specifications, identifying
recordkeeping procedures to ensure data review, acquisition,
traceability; accountability noting procedures to ensure customer
confidentiality; and other parameters such as control, security,
storage, retention, and disposal of laboratory records.

e)  Assessments, describing the laboratory’s processes to monitor the
effectiveness of its QA program.

1)  Internal audits of laboratory operations, performed on a routine
basis, minimally annually, by the QA officer and supervisor. For a small
laboratory, an outside expert may be needed.

2)  On-site evaluations by outside experts to ensure that the laboratory
and its personnel are following an acceptable QA program.

3)  Proficiency test studies, in which the laboratory participates.
These collaborative studies confirm the abilities of a laboratory to
generate acceptable data comparable to those of other laboratories and
to identify potential problems.

f)  Correction and preventative activities, identifying procedures used
to determine the causes of identified problems and to record, correct,
and prevent their re-occurrence.

Systematic Project or Experimental Planning

	In general, systematic project planning is essential before any
activity begins, whether it’s sampling or analysis.  For any project,
the scientist needs to develop the experimental study design by first
identifying and documenting what the problem is, why the new information
is needed, and the objectives for the experiment or series of
experiments.

	Once the study objectives are defined, the hypothesis is then
developed.  In systematically planning a project, the team or researcher
then needs to determine the study parameters or test variables, both
critical and the secondary (if any).  The data quality objectives or
performance criteria (i.e., how good the data should be for the intended
purpose) should be defined before the experiment starts along with all
the appropriate quality control activities.  For example, how types and
numbers of replicates will be followed in the experiment, how is the
specificity/selectivity of the analytical method to the target
determined, how will the precision be determined in terms of
repeatability and reproducibility.  In the process of determining all
these quality control activities, the experimental design can be
optimized and documented.

Parameters of Microarray Platform Performance

Most microarray gene expression experiments fall into three broad
classes, depending on outcome, that should have distinct QC reporting
needs:

The first class of microarray experiments is that for which the
investigator concludes that a treatment/exposure causes a biological
effect. This is the most common conclusion from published microarray
experiments, and the simplest from a QC point of view.

The next category of experiments is one wherein the investigator
concludes that the treatment has no observable effect. These results are
rarely reported in the literature, but might be common in regulatory
submissions. These are somewhat more complicated from a QC perspective.

The third group offers claims about the magnitude of changes in
transcription.

Examples of this last class of experiments are rare, and are the most
difficult and expensive on which to perform adequate QC.  Currently, a
cost effective microarray platform on which to perform this last class
of experiments is not available.  While the minimum QC of the experiment
may be unchanged, the extent of documentation needed to verify that an
ensuing experimental report is acceptable, may vary based on
accompanying results.

Although negative and positive controls are part of experimental designs
and investigative approaches, the requisite controls for
“expression” studies – particularly microarray experiments - are
not always obvious.  Investigators are encouraged to consider not only
the biological system under scrutiny, but also the nature of the
assertions about the system.  The need to have adequate controls should
be considered in an experimental scheme, in order to demonstrate that
measurements are accurate enough to support scientific claims and
assertions.  Needs for several straightforward situations are listed
below, and can be applied as a guide to more complex scenarios.  It is
useful if control samples are constructed in a way that ensures the
control and experimental samples are as similar to one another as
possible (e.g., with regard to biologic composition and complexity of
RNA) except in such cases where control sample characteristics are
unambiguously presumed to differ.

In cases where the investigator proposes that a biologic effect is
present (the first case noted above), the primary QC issues are
precision and specificity, and the use of a negative control is
encouraged demonstrate that the measurement system is not likely to
produce false positives.  Accuracy is rarely a concern, since claims are
not being made regarding the magnitude of differential expression
between experimental and control groups, but only whether a difference
exists.  Sensitivity is also not relevant, since no difference or effect
would be observed if sensitivity were too low.  It is useful if the
negative control and experimental groups include sufficient replicates,
relative to the magnitude of effect and experimental variability, in
order to show that the claimed effect, and no effect cases, can be
statistically distinguished with desired confidence.  RNA from untreated
samples is usually as adequate and readily available as a control in
this case.  Precision is accounted for by statistical procedures (e.g.,
t-test, chi squared test, and respective non-parametric analogs)
routinely used to determine whether the experimental and control groups
differ significantly.  Additional consideration should be given to
demonstrating the specificity of measurement(s) for the effect of
interest.  Demonstrating that a variety of probes exhibit binding
affinity for discrete regions of a given transcript, can provide
congruent results and is often a sound way to address the issue of
platform specificity.  The ability of probes to distinguish similar
transcripts, including splice variants, is also useful to address.  The
issue of specificity is best addressed by using complementary
“expression” measurement technologies (e.g., quantitative real-time
PCR, Northern blot analysis, RNase protection assays, S1 nuclease
protection) to confirm microarray results.  This will control for
technique-specific effects, and by using distinct set(s) of
amplification primers, help control for non-specific or unintended
hybridization to microarray probes.  Alternatively, a different
microarray platform could be used to confirm specificity if the second
microarray platform uses distinct probe sequences for detecting the
transcripts of interest.  A useful way to control systematic error is to
ensure randomization of both processing order and acquisition of
measurements for control and experimental samples.  Using blind samples
can be a useful approach to avoid operator bias.

In cases where the investigator maintains that there is no biologic
effect (class two, above), a positive control is useful to show that the
measurement system is capable of detecting the smallest effect sizes for
which the claim is being made.  In scenarios such as this, the
additional QC factor of sensitivity comes into play, while specificity
becomes less important.  It is advantageous if the positive control and
experimental groups both contain a sufficient number of replicates to
show that the two groups can be statistically distinguished with the
desired confidence level.  It is useful to avoid absolute claims to the
effect of ‘there is no effect whatsoever,’ since only effects equal
to or larger than those readily observable in the positive control can
realistically be ruled out.  Instead, conclusions might take the form of
‘there is no effect larger than X’, where ‘X’ represents the
smallest magnitude of effect readily detectable in positive controls.
Some validated positive controls, such as samples subjected to a
treatment widely recognized to produce the desired effect, are
considered the preferred source for positive controls.  However, there
are cases for which no adequate model exists for the effect being
studied.  Then, it is useful to construct a positive control using
methods such as spiking complex RNA samples with purified and quantified
RNA of interest.  Alternatively, investigators might use mixtures of
complex RNA samples in which the RNA of interest is present in varying
known concentrations (see also section on System Linearity and
Calibration).  These controls are useful for demonstrating that the
measurement system can readily detect the effect sizes for which the
negative claim is being asserted.

As always, it is beneficial to randomize the order of processing and
measurement relative to the sample group, and using blind samples should
be considered.  The same statistics, as applied to the ‘there is an
effect’ case, are generally used to control for inconsistencies in
precision, but in this case acceptable performance means that the
positive and negative controls may be reliably distinguished from one
another, while the experimental sample is statistically
indistinguishable from (appears to come from the same population as) the
negative control.

When an investigator submits a claim regarding the magnitude of an
effect, and not only the presence or absence of effects, a more complex
system of control (i.e., calibration curve) should be considered (see
also section on System Linearity and Calibration).  In cases such as
this, where quantification of differential expression is critical (e.g.,
when stating that transcription of gene X increases 1.8 fold after
exposure/treatment), accuracy becomes the foremost QC factor, and more
complex positive controls and statistics should be considered.  A
calibration curve typically demonstrates the accuracy of the measurement
system across the range of concentrations being considered.  Researchers
should consider assembling appropriate materials for constructing
calibration curves in cases where standard reference RNA is not
available.  In many cases, investigators may consider methods such as
spiking a complex RNA sample with a known series of concentrations of
the RNA species of interest, or using a mixture series of two complex
samples where the concentration of the RNA species of interest differs
by a known amount.  In the latter case, combining the two RNA
‘targets’ at different ratios produces a series of known
concentrations of the RNA species of interest.  It is useful to adjust
the range and spacing of concentrations on the calibration curve (e.g.,
log linear scale) and the number of replicates per concentration based
on the level of precision desired and amount of experimental variability
observed.  Specificity of signals of interest might be confirmed by
showing congruence with signals produced by probes that hybridize to a
different portion of the same transcript.  It is beneficial if
conclusions on the magnitude of an effect include confidence intervals
that reflect the performance of the measurement system during
calibration curve construction, as well as variability seen in the
experimental samples.

Overview of Array Technology – The Physical Platform

The current method for fabricating DNA microarrays (DNA chips) is to use
either cDNA or oligonucleotides as probes that represent specific genes
in the organism of interest, attached to a suitable solid substrate such
as a glass microscope slide.  It is acknowledged that the specificity of
these probes is limited by the current understanding of gene sequence,
among other things.  It is useful if all sequences are periodically
reevaluated based on the newest gene sequence information to ensure
valid assessments.

Microarrays populated with cDNA probes are created by ‘spotting’
amplified cDNA fragments in a desired density pattern onto a solid
medium such as a glass slide.  Arrays using oligonucleotide probes are
either mechanically ‘spotted’ or assembled by chemically
synthesizing short, unique oligonucleotide probes directly onto a glass
or silicon surface using covalent chemistry or photolithographic
technologies.  It has been well established that numerous possibilities
exist for errors to become ‘fixed’ during the manufacture of the
arrays; therefore, the fidelity of the DNA fragments immobilized to
microarray surfaces may be compromised by several different kinds of
experimental and manufacturing inconsistencies.  Given the QA/QC
challenges in manufacturing gene arrays, a trend has emerged in recent
years towards the use of gene arrays from several large vendors rather
than arrays from smaller scale manufacturers or those prepared
“in-house.”  While this may limit choice, it may also offer an
advantage to the array community when addressing issues of
cross-platform compatibility.  There are a number of sources of
technical error which can adversely impact data quality of a gene array
experiment.  These include, but are not limited to, poorly functioning
probes or probe sets, cross hybridization of related genomic sequences,
scanner settings and function, and atmospheric ozone.  Unfortunately, a
set of performance standards by which individual laboratories may be
evaluated are not currently in place, although it is anticipated that
such standards may be developed in the near future.

In many array-based studies, the investigators report microarray data
for which there is no corroborating validation for the observed
transcriptional measures.  For profile data observed on array platforms
regarding novel findings that are not readily supported in the
peer-reviewed scientific literature, it is useful to include supporting
data generated by traditional methods of evaluating gene expression,
such as PCR, Northern blot hybridization analysis and RNase protection
assays.  In addition, the quality of probe sequences selected for
particular transcribed regions incorporated onto the array is also a
critically important consideration.  For example, if probes are selected
primarily from the 3’ end of given genes, splice variants of those
genes can evade identification, if the alternative splicing events occur
5’ of a probe region.  Additionally, by microarray analysis, it is
very difficult to distinguish between two expressed genes that share a
high degree of sequence homology.  Variation in probe specificity is
also a commonly encountered problem in oligonucleotide arrays.  This
problem frequently arises in instances where nucleic acid sequences are
practically identical between two coding regions and the oligonucleotide
probes are synthesized from 3’ends of the genes.

Isolation of Nucleic Acid ‘Targets”

	Since this biological analyte comprises the molecular species that will
be measured, it is beneficial to ensure efficient isolation as well as
post-isolation stability and structural integrity.  Total RNA is
generally used for gene expression analysis, although, mRNA is also
used.  RNA isolation techniques often involve homogenization of either
fresh or frozen samples at high concentrations of guanidine
isothiocyanate followed by phenol extraction and alcohol precipitation,
although other methods can produce RNA of high quality.

Methods for determining purity (i.e., absence of contaminating reagents)
include nucleic acid analysis by spectrophotometry at absorbance ratios
of 260/280 nm, with expected values between 1.90 and 2.10 at pH 7.5.
Another conventional method for determining structural integrity is
through the use of MOPS-EDTA formaldehyde (or glyoxal) agarose gel
electrophoresis, during which either the integrity of ribosomal RNA or
the relative size distribution of mRNA can be evaluated.  Recent
advances in microfluidics and analytical equipment, (e.g., 2100
Bioanalyzer , Agilent, Inc.), allow investigators to evaluate the
integrity of nucleic acids with greater speed and accuracy than possible
with agarose gel electrophoresis.  It is anticipated that this
technology will soon replace the more frequently used methods.

Experimental Design

The importance of pre-planning the experimental design cannot be
overemphasized.  Since the critical outputs from biological
“expression” analyses are largely dependent upon experimental design
investigators should consider devoting extensive attention to performing
experiments with the appropriate design parameters.  It is advantageous
if the chosen experimental design provides sufficient statistical power
to unambiguously test the biologic argument.  The level of analytical
power needed to allow for the detection of differentially expressed
transcripts at a ratio greater or equal to ‘X-fold’ should be
considered.  In addition, it is useful if such analyses take into
account the percentage of false positives that the researcher is willing
to accept.  The false positive rate (FPR) and the false negative rate
(FNR) are necessarily dependent upon each other i.e., a decrease in one
results in an increase in the other.

When designing an experiment, adequate consideration should be given to
sample sizes, the use of controls, the use of sample randomization, and
blind sample procedures.  Specific needs will depend on a number of
factors, including the nature of the conclusion being presented, the
manner by which samples are compared with one another, the range of
measured effects, and experimental precision.  To ensure adequate
statistical power will be realized to support scientific arguments and
conclusions, it is advantageous to consult a statistician during the
experimental design phase.  In order to estimate the projected magnitude
of effect and experimental precision conducting a small scale pilot
experiment in advance of the definitive experiment might be considered.
Alternatively, one technology (e.g., DNA microarrays) might be used as
an exploratory tool for hypothesis generation, followed by the use of a
secondary technology (e.g., quantitative RT-PCR, qPCR) to generate
adequate numbers of experimental and control replicates to fulfill
hypothesis testing.  Some general issues that should be considered are
listed below, but their specific applicability will vary among
experiments.  Careful consideration of these issues should provide
sufficient information for a reliable estimate of overall experimental
performance, and the statistical strength of conclusions put forth.

	It is expected that technical variation will be introduced at each
critical laboratory step during expression analysis.  In addition,
unique sources of variation are likely to be associated with individual
laboratories and/or technicians.  It is important, therefore, that this
variation be considered during study design and statistical analysis in
order to avoid confounding of these sources of variation with treatment
effects.  For those experiments in which data are collected from
array-based studies, there are three design schemes typically used;
these are briefly described below.  Although these are certainly not all
inclusive, identification of the acceptable system is left to individual
research teams.  The three fundamental design alternatives typically
used are 1) the flexible universal reference design, which is used for
analysis of many experimental factors of equal importance, or those that
will be integral to future meta-analysis, 2) the efficient balanced
block design, for use in looking for genes that are upregulated or
downregulated between two samples, and 3) the more integral loop design,
which when comparing samples of equal interest and high quality results
in half the variance per estimate, because each sample is included two
times, rather than once, at the minimal expense of one additional chip.
There is, however, a rather large experimental cost of this latter
design, because it relies on not even one chip failing to reach the
highest quality level.

dye (Cy ™; e.g., Cy3 or Cy5) and are compared to a reference RNA
sample which is labeled with the other of the two Cy ™ dyes.  Not only
does this approach help minimize the potential for dye bias, which is a
significant concern when using the two-dye hybridization approach, but
this also allows for comparison of data across studies that use the same
reference RNA.  One practical approach may be to take advantage of
commercially available universal reference RNAs for gene expression
profiling which, at this time, are offered for use with arrays
representing a limited number of organisms (human, mouse, rat).  Another
experimental design often used to address dye bias is the ‘dye swap’
or ‘dye flip’.  In this method a second experiment is conducted by
exchanging labeling reactions such that the treated and control samples
are conversely labeled with the respective Cy ™ dyes.  The approach
entails the use of additional arrays; however, because dye bias has been
observed by numerous investigators and noted in the literature, such a
scheme should be considered when designing experiments using two-color
array systems.

Experimental Replication

	It is not possible to analyze expression data without an estimate of
variance.  Since experimental variance has both technical and biological
components, replication could be incorporated at several levels.  In the
case of a gene array experiment, technical replication could be in the
form of multiple spots per gene on the same array or, perhaps, multiple
arrays for a given sample.  While including technical replicates will
improve data analysis it is not an absolute necessity.  On the other
hand, biological replication is an important consideration.  While it is
generally accepted that in a gene array experiment an absolute minimum
of three biological replicates is needed, additional replication is
often needed to detect a treatment effect when less than robust changes
in gene expression are observed.  Pilot studies could be conducted to
estimate variance and give insight as to what level of replication may
be useful.  Although not comprehensive, additional considerations for
determination of replicate numbers are the relative quality and
integrity of samples, the range of expected effects and the method of
raw data analysis.  The optimal replicate number is affected other
factors such as the type of array technology and platform (single or
dual channel RFU capture), array platform linearity (precision), feature
density (number of representative gene probes), and the selected
percentage value of FPR.  Since replication is an asymptotic process,
even a small number of replicates will strengthen any conclusions that
can be drawn from the data, irrespective of the technological approach
used to collect these data.

Pooling of Samples

	From a theoretical perspective, most biological material used in
expression studies arises from ‘pooled’ sources because most tissues
used in such investigations contain many distinct cell types.  Pooling
of samples is primarily encouraged in those cases where the quantity of
nucleic acid ‘target’ (total RNA) is limiting to the point that this
represents the only means by which to obtain the requisite mass.  It is
recognized that, in certain studies, pooling of samples across
individuals is a logical approach in order to limit study size.  In
fact, pooling of samples can help to minimize biological variation.
However, it should be recognized that pooling will not be as effective
in controlling biological variation as increasing the number of
biological replicates in a study.  Theoretically, most total RNA samples
are pooled, since they are isolated from cells of related or different
types after having been amplified from the original source to produce
the test product.  Combining samples does have the advantage of
decreasing noise in the system.  If biological variability is not a
major concern, a pooled sample could be considered the same as a single
individual when applying an experimental design.  If biological
variability is important to the interpretation of the data, and RNA from
pooled sources is used in determination of expression measurements, it
is useful to include more than one independent pool of samples for the
purpose of estimating biological variability.  Biological replicates are
generally regarded as more critical than are technical replicates to
measures of expression in biologic systems unless otherwise indicated.

Specificity and Sensitivity

	Specificity and sensitivity of assays are affected by
sequence-dependent (length and inclusive base composition) and
sequence-independent (relative concentrations of probes and targets,
hybridization time, temperature, etc.) factors.  The specificity and
sensitivity of assays have been the subject of numerous cross platform
comparison studies recently cited in scientific literature
(Venkatasubbarao, 2004; Enders, 2004; de Longueville et al., 2004).  The
term specificity refers to the ability of an expression platform to
discriminate or select between distinct members of the same gene family,
whereas sensitivity is the potential to discriminate transcripts
expressed at low level in a complex background.  In recent years there
has been a trend in microarray design towards oligonucleotide probe sets
to improve the specificity of gene targeting.  Oligonucleotide
microarrays (25 to 70 bp) have some advantages over arrays on which cDNA
probes have been affixed.  Oligonucleotide probes are designed to be
identical with respect to the number of bases (length) and
concentration, with comparable annealing temperatures of hybridization.
These considerations account for enhanced uniformity over the entire
platform.  Oligonucleotides are also designed to reduce inadvertent
target cross-hybridization, thereby increasing specificity during
hybridization reactions.  These combined properties increase the
stability and reproducibility of hybridization signal on each feature on
the array.

	In addition to the quality of the probe sequences, the specific region
of a gene that is selected as a probe to be incorporated onto the array
is also critically important.  For example, if probes are selected
primarily from the 3’ end of given genes, as is often the case, there
is a distinct possibility that splice variants of those genes will evade
identification if the alternative splicing events occur 5’ of a probe
region.  Additionally, it is very difficult to distinguish between two
expressed genes that share a high degree of sequence homology by
microarray analysis.  Irregularity in probe specificity is also a
frequently encountered problem in oligonucleotide arrays.  This problem
frequently arises in instances where nucleic acid sequences are
practically identical between two coding regions and the oligonucleotide
probes are synthesized from 3’ ends of the genes.

	Decrease in specificity on microarray platforms generally results from
the technical limitations inherent in enzymatic labeling of the RNA
target.  One of the most widely used methods for enzymatic modification
of total RNA, for microarray analysis of gene expression, uses T7 viral
RNA polymerase in vitro transcription (IVT) to produce complementary RNA
(cRNA) that can be hybridized to gene-specific probes affixed to arrays.
 Multiple rounds of amplification are used to label a limited mass of
RNA by this IVT method, which has been shown to inadvertently introduce
errors.  Because cRNA-DNA sequence mismatches are more thermo stable
than comparable cDNA-DNA mismatches, intensity artifacts have been
observed due to increased non-specific hybridization.

System Linearity and Calibration

Linearity of signal responses and other measurable output are perhaps
among the most significant aspects of obtaining reliable gene expression
data.  Regardless of technological modes (e.g., microarray-based
studies, semi-quantitative gel based PCR, ‘real-time’ PCR, or
densitometric scanning of pixel density), usable data collected within
the linear region of the output curve for any chosen system is
essential.  Given the increased number and overall density of
gene-specific probes present on microarrays, it is particularly useful
to demonstrate linearity of relative fluorescence units (RFUs) for the
greatest number of discrete features represented on the chip.  Recent
observations from microarray workgroups suggest that specific reference
RNA is the most efficient means by which to accomplish this.  In an
attempt to measure precision (B. Aronow, personal communication), it was
determined that the greatest coverage of features was attained by
hybridizing 4-5 different ratio mixtures, on as many chips, of
species-specific RNA obtained from different sources.  For instance, the
study in question mixed RNA prepared from the colons of 8-week old
C57BL/6 8 mice and post partum day one C57BL/6 whole animals in
different proportions.  The relative fluorescent unit (RFU) value
changes for every gene probe that yielded a response to the mixture of
mouse RNA, were statistically analyzed using least-square linear
regression.  This suggested approach permits investigators to ascertain
a global perspective regarding the degree of linear response in a chosen
system.

Randomization of Samples

Technical variations or differences in “expression” measurements can
be introduced at several junctures in the experimental process
including, but not limited to, methods of RNA labeling, the choice of
microarray platform, capture methods for RFU intensity and signal
quantitation, ozone-mediated fluorescent signal degradation, humidity
and temperature, and moreover, those individuals charged with performing
the experiments.

Many experimental designs suggest that blind randomization of samples is
integral to the analyses.  This approach offers the promise of
‘flattening’ both internal and external experimental sources of
variation.  Sample randomization should be considered wherever
practical.  Numerous confounding variables have been identified that can
distort microarray results.  Some of these sources of variation are well
known (e.g., RNA degradation during tissue extraction), and others have
been more recently identified (e.g., ozone-mediated bleaching of some
florescent dyes), and some causative factors have yet to be
characterized.  If adequate care is taken to randomize the order in
which samples are processed, and operators are unaware of the nature of
each sample, known and unanticipated sources of variability are not
likely to bias the outcome of the experiments.  However, such sources of
variability can nevertheless exert influence on the observed precision
of the system.  For instance, if all the experimental group samples are
run on a given day, and all the negative control samples are run on the
following day, it is possible that experimental features can differ on
the two days (e.g., operator identity, photomultiplier drift, and/or
ozone concentration).  Such differences could systematically bias
results for the experimental samples relative to the control samples,
creating the false impression of a real difference between the two
groups.  On the other hand, if samples for the two groups are
randomized, with half the samples run on day one, and the other half on
day two, factors that differ between the two days will decrease the
precision observed in both groups (a readily detected and addressed
occurrence), without creating the false impression of systematic
differences between the groups in question.

APPENDIX B:  MIAME-Based Data Submission Tables

Table B.1  Abstract

MIAME	Description	When applicable	Notes	Values

B.1 Abstract	Brief summary of the purpose and findings of the
experiment. 	Always

Table B.2  Experimental Design

MIAME	Description	When applicable	Notes	Values

B.2 Experiment design	Design and purpose common to all hybridizations
 Always	Related hybridizations interpreted as a single experiment.

	Author, laboratory, and contact	Person(s), organization(s), names and
contacts (address, phone, FAX, email, URL).	Always

Contact details

Experiment type(s)	A controlled vocabulary that classifies an
experiment.	Always	Experimental Factor(s). 	Time course,

dose response, comparison

(disease vs normal, treated vs untreated),

temperature shock,

gene knock out,

gene knock in (transgenic), etc.

Experiment Description	Description of the experiment and relevant
electronic peer-reviewed journal publication(s)	When additional
information is available and an electronic publication exists.
Consistent with experimental design.	Text description, citation, URL.
Database entry

Experimental factor(s)	Parameter(s) or condition(s) tested in the
experiment.	Always	Experimental factor(s) consistent with Experiment
Type(s)	Time,

dose,

 compound,

temperature,

 extraction, hybridization, labelling, scanning

Number of hybridization replicates	Number of hybridization replicates
Always	Consistent with Experiment Type(s)	Single,

multiple

Common reference	A hybridization to which all the other hybridizations
have been compared.	Always

Yes, no

Quality control steps	Measures to ensure quality: replicates (number and
description), dye swap (for two channel platforms) or other 	When
appropriate

Text description.

biological,

technical

Qualifier, value, source (may use more than once)	Any further
information about the experiment .	Additional useful information

Qualifier= name

Value= value

Source= database entry or ontology entry

Table B.3  Array Design

MIAME	Description	When applicable	Notes	Values

B.3 Array design	Array layout.  Description of the common features of
the array and each array element. 	When an array design is novel and
cannot refer to manufacturer	Array design should be provided by the
array manufacturer.

	B.3.1. Array related information	Overall description of the array.

	Array design name	Unique name, that identifies a specific design 	Array
is novel and cannot refer to manufacturer	Consistent with the design
name given for the array. 	Design name,

number of features,

version (e.g.: EMBL yeast 12K ver1.1)

Platform type	Technology to place biological sequence on array.	Array is
novel and cannot refer to manufacturer

in situ synthesized, spotted cDNA,

etc.

Surface and coating specification	Surface coating type and name	Array is
novel and cannot refer to manufacturer	Consistent with Platform Type
SurfaceType:

glass, membrane, coating type

Array dimensions	Dimensions of the array support slide.	Array is novel
and cannot refer to manufacturer

width, length

Number of features on the array	The number of features on the array.
Array is novel and cannot refer to manufacturer

number of features

Production protocol

	A description of how the array was manufactured.

	Array is novel and cannot refer to manufacturer

Protocol

description,

printing hardware,

printing software

Provider	The primary contact (manufacturer) for the information on the
array design.	Always

Contact details of manufacturer

B.3.2 Reporter related information	Information on the nucleotide
sequence present in a particular location on the array.

	B.3.2.1 For each reporter type

Reporter type	Physical nature of the reporter (e.g. PCR product,
synthesized oligonucleotide).	Array is novel and cannot refer to
manufacturer	Consistent with Platform Type	Types: empty, PCR,
synthesized  oligonucleotide,

plasmid, colony,

etc.

Single or double stranded	Reporter sequences are single or double
stranded.	Array is novel and cannot refer to manufacturer	Consistent
with Platform Type	Single, double

B.3.2.2 For each reporter

Reporter sequence information	Nucleotide sequence for each reporter:
accession number (from DDBJ/EMBL/GenBank), the sequence itself or
reference sequences and primers pair information 	Array is novel and
cannot refer to manufacturer	Consistent with Platform Type and clone
Sequence annotation,

accession number,

PCR primer pair

Reporter approximate length	The approximate length of the reporter
sequence.	When the exact reporter sequence is NOT known

Number of bases

Clone information	For each reporter, identity of the clone, clone
provider, date obtained, and availability.	When elements are from clones
When an array design is novel and cannot refer to manufacturer
Consistent with Platform Type	Clone ID,

provider,

date obtained,

availability

Reporter generation protocol	A description of how the reporters were
generated.	Array  is novel and cannot refer to manufacturer

Protocol

B.3.3 Features related information	Information on the location of the
reporters on the array

	B.3.3.1 For each feature type

Feature dimensions	Dimensions of each feature.	Array is novel and cannot
refer to manufacturer	Consistent with array dimensions and number of
features	Width, length, height, diameter

Attachment	How the elements (reporters) are physically attached to the
array.	Array is novel and cannot refer to manufacturer	Consistent with
element generation protocol	Covalent,

ionic, hydrophobic,

etc.

B.3.3.2 For each feature

Reporter and location	Arrangement and system used to specify location of
each feature 	Array is novel and cannot refer to manufacturer	Consistent
with array dimensions and number of features	Row, column,

x microns,

y microns,

zone

B.3.4 Composite sequence related information	Information on the set of
reporters used collectively to measure an expression of a particular
gene.

	B.3.4.1 For each composite sequence

Composite sequence information	The set of reporters contained in the
composite sequence.

	When elements are composite array  is novel cannot refer to
manufacturer	Consistent with element type	Oligonucleotide sequences,

number of oligonucleotides,

reference sequence

Gene name	The gene represented at each composite sequence	Array is novel
and cannot refer to manufacturer	Consistent with clone and composite
sequence information	Gene name,

accession number,

annotation

Qualifier, value, source (may use more than once)	Describe any further
information about the array in a structured manner.	When additional
information is available that would be useful to base queries on

Qualifier= name

Value= value

Source= database entry or ontology entry

B.3.5 Control elements related information	Array elements that have an
expected value and/or are used for normalization.

	Control element position	The position of the control features on the
array.	When any elements on the array were used as controls	Consistent
with

Quality Control Description	Row, column,

x microns,

y microns,

zone

Control type	The type of control used for the normalization and their
qualifier.	When any elements on the array were used as controls
Consistent with

Quality Control Description	Control type (spiking, negative, positive),

control qualifier (endogenous, exogenous)

Table B.4  Biomaterials

MIAME	Description	When applicable	Notes	Values

B.4 Biomaterials

	The biological material from which the nucleic acids have been
extracted for subsequent labelling and hybridization.	Always

	B.4.1 Biosource properties	Information on the source of the sample.

	Organism	The genus and species (and subspecies) of the organism from
which the biomaterial is derived.	Always

Genus, species,

subspecies from NCBI taxonomy

Sample Contact details	The resource used to obtain the biomaterial 	When
biomaterial was prepared or grown outside of the laboratory listed for
the author

Biosource provider

Type of specimen (tumor biopsy,

paraffin section,

stool sample}

Cell type	Cell type(s) or organs used in the experiment. 	Always
Consistent with organism and targeted cell type	Name of organ tissue
cell type (ATCC # ) and source

Sex	Term applied to any organism able to undergo sexual reproduction in
order to differentiate the individuals or type involved. 	When
applicable	Consistent with organism	Mating type alpha, F+, F-, Hfr,

Mating type a,

Mixed sex,

Unknown sex

Age	The time period elapsed since an identifiable point in the life
cycle of an organism. 	When applicable	Consistent with organism

	Age =

combination of real number (measurement) and initial time point e.g.:

coitus, birth, planting,  beginning of stage

Developmental stage	The developmental stage of the organism's life cycle
during which the biomaterial was extracted.	For multicellular species
Consistent with organism	Developmental stage (i.e., embryo, fetus,
adult)

Organism part	The part or tissue of the organism's anatomy from which
the biomaterial was derived.	For multicellular species	Consistent with
organism	Organism part

term)

Strain or line

	Animals or plants that have an ancestral breeding.

	When known

	Consistent with organism

	Strain or line ( e.g.:

Jax mouse strains, Cultivar,NCBI taxonomy)

Genetic variation	The genetic modification introduced into the organism
from which the biomaterial was derived. 	When the source organism is
genetically modified	Consistent with organism	Examples of genetic
variation include specification of a transgene or the gene knocked-out.

Individual number	Identifier or number of the individual organism from
which the biomaterial was derived. 	When the organism can be
distinguished on an individual basis with a unique ID	Consistent with
organism	Individual ID. For patients, the identifier should be approved
by

Institutional Review Boards (IRB, review and monitor biomedical research
involving human subjects) or appropriate body.

Individual genetic characteristics	The genotype of the individual
organism from which the biomaterial was derived.	When applicable
Consistent with organism	Allele,

genotype,

haplotype,

polymorphisms.

Disease state	The name of the pathology diagnosed in the organism from
which the biomaterial was derived. 	When applicable	Consistent with
organism	 “Normal” or .

disease state

 description

Targeted cell type	Cell of primary interest.

	Biomaterial is a mixed population of cells	Consistent with organism and
cell type

Biomaterial may be derived from a mixed population of cells although
only one cell type is of interest.	Targeted cell type=

term,

(Mouse Anatomical Dictionary,

FlyBase,

CBIL vocabulary)

Cell line

	Identifier for the cell line

	Biomaterial is derived from an immortalized cell line	Consistent with
organism and cell type	Cell line term, source of term ( ATCC #
),e.g.,Hela,

Caco-2

B.4.2 Biomaterial manipulation	Information on the treatment applied to
the biomaterial

	Growth conditions

	Description of environment used to grow organisms

	Culture condition details

In vivo treatment	Manipulation to generate variable(s) under study .
When sample has been treated or manipulated for the study

	Consistent with Experiment Type and Experimental Factors

	Documentation of the set of steps taken in the treatment

In vitro

treatment	Manipulation of cell culture condition for generating
variables under study.	When the sample has been treated or manipulated
in vitro for the study purpose

	Should be consistent (where appropriate) with Experiment Type,
Experimental

Factors	Documentation of the set of steps taken in the treatment

Treatment type

	Manipulation for generating variables under study.

	When sample has been treated or manipulated for the study	Consistent
with experiment type, experimental factors and treatment

	Description of treatment

(behavioral stimulus,

compound based treatment,

infection,

modification (genetic, somatic)),

Compound	Drug, solvent, chemical, etc., that can be measured.	When
sample has been treated or manipulated with a compound 	Consistent with
treatment type

	Description of compound’s physical and chemical characteristics

Separation technique	Technique to separate tissues or cells.	When the
cells or tissue are separated from a heterogenous sample

Protocol

B.4.3 Hybridization extract preparation	Information on the extract
preparation for each extract prepared from the sample

Extraction method

	The protocol used to extract nucleic acids from the sample.

	Always

Protocol

Nucleic acid type	The type of nucleic acid extracted (e.g. total RNA,
mRNA).	Always

Polymer type

(total RNA,

mRNA,

DNA)

Amplification method	The method used to amplify the nucleic acid
extracted.	When applicable

Protocol

B.4.4 Sample labelling	Information on the labelling preparation for each
labelled extract.

	Amount of nucleic acid labelled	Amount of nucleic acid labelled.

	Protocol

Label used	Label used.	Always

Label

(Cy3,

Cy5,

etc.)

Label incorporation method

	Label incorporation method 	Always

Protocol

B.4.5 Spiking control	External controls added to the hybridization
extract(s).

	Spiking control feature	Position of the feature(s) on the array
expected to hybridize to the spiking control.	When applicable	Consistent
with

quality control description	row, column,

x microns,

y microns,

zone

Spike type and qualifier	Type of spike used  and its qualifier 	When
applicable	Consistent with

quality control description	Oligonucleotide, plasmid DNA, transcript,.
concentration, expected ratio, labelling methods

Qualifier, value, source (may use more than once)	Describe any further
information about the sample in a structured manner.	When additional
information is available that would be useful to base queries on

Table B.5  Hybridization

MIAME	Description	When applicable	Notes	Values	Included in DER?

B.5 Hybridization	Procedures and parameters for each hybridization.
Always

	Relationship between samples and arrays 	Relationship between the
labelled extract 	Always	Consistent with technology quality control
Which sample, which extract “array design, batch and serial number,
during which hybridization	Yes

Hybridization protocol	Set of steps taken in the hybridization:

(solution blocking agent, concentration, wash procedure);

quantity of labelled target used;

time;

concentration;

volume,

temperature.

	Always

Description of the hybridization instruments	Yes