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Contributions to TBC* from:
MGED (now FGED)
ILSI-HESI
US NTP/NIEHS/NIH
Jennifer Fostel
MIBBI Workshop
December 1-2, 2010
*Tox Biology Checklist in MIBBI
Scope of guidelines
• TBC – Tox-Biology Checklist
– Published/shared toxicology/biological studies
• FGED – Functional Genomics Data Society
– Phenotype data for reuse in meta-analysis
• ILSI HESI Technical Committee on Genomics
– Toxicogenomics data exchange
• US National Toxicology Program/NIEHS/NIH
– NTP toxicology studies
Implementation/Activity:
• FGED:
– Discussing letter to journal editors to add to MIAME
– FGED is supported by a grant
• HESI:
– Extending work to new tissues, species
– HESI Technical Committee is supported by committee
subscription: 11 academic/governmental agencies, 21
companies in 2010.
• NTP:
– Database implementation (http://cebs.niehs.nih.gov)
– SIFT format – tab format or XML builder.
– NTP is a funding agency
NTP minimal information
For any study, provide the name / ID of the appropriate comparator group.
MI depends on
Study subject type
For an animal study…
For a cell culture study…
Study design
If a chemical is administered…
If this is a reproductive toxicity study…
Assay type
For a microarray study, MIAME plus…
(formerly MGED)
Functional Genomics Data Society
Standards for exchange of phenotype data for reuse in meta analysis.
The following information is strongly suggested / required
with a publication / data deposition (in addition to MIAME requirements):
(1) statement of informed consent; the data may (not) be used in additional analysis.
(2) enrollment criteria / exclusion criteria
(3) population characteristics: race/ethnicity/strain, age, gender
(4) tissue or cellular makeup of specimen
(5) non-omics phenotype for each participant (subject or group)
Different study or trial designs would have additional reporting requirements:
(A) study involving a disease
(B) study involving chemical exposure
(C) study involving surgical intervention
(D) Observational or epidemiological study
HESI Baseline Animal Database
Led by Karol Thompson, FDA, CDER
Collected over 500 microarrays from liver or kidney of control animals from 16
different institutions, with metadata on protocol factors such as diet, housing,
administration of vehicle, amount of material on the array, array type, animal weight
and age at sacrifice.
Multivariate analysis of the 17 mostly complete factors identified the factors
contributing the most to variability in this data set.
The study factors that emerged as key sources of variability included gender, organ
section, strain, and fasting state.
HESI Baseline Animal Database
Led by Karol Thompson, FDA, CDER
Collected over 500 microarrays from liver or kidney of control animals from 16
different institutions, with metadata on protocol factors such as diet, housing,
administration of vehicle, amount of material on the array, array type, animal weight
and age at sacrifice.
Multivariate analysis of the 17 mostly complete factors identified the factors
contributing the most to variability in this data set.
The study factors that emerged as key sources of variability included gender, organ
section, strain, and fasting state.

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TBC contributions from MGED, ILSI-HESI, NTP

  • 1. Contributions to TBC* from: MGED (now FGED) ILSI-HESI US NTP/NIEHS/NIH Jennifer Fostel MIBBI Workshop December 1-2, 2010 *Tox Biology Checklist in MIBBI
  • 2. Scope of guidelines • TBC – Tox-Biology Checklist – Published/shared toxicology/biological studies • FGED – Functional Genomics Data Society – Phenotype data for reuse in meta-analysis • ILSI HESI Technical Committee on Genomics – Toxicogenomics data exchange • US National Toxicology Program/NIEHS/NIH – NTP toxicology studies
  • 3. Implementation/Activity: • FGED: – Discussing letter to journal editors to add to MIAME – FGED is supported by a grant • HESI: – Extending work to new tissues, species – HESI Technical Committee is supported by committee subscription: 11 academic/governmental agencies, 21 companies in 2010. • NTP: – Database implementation (http://cebs.niehs.nih.gov) – SIFT format – tab format or XML builder. – NTP is a funding agency
  • 4. NTP minimal information For any study, provide the name / ID of the appropriate comparator group. MI depends on Study subject type For an animal study… For a cell culture study… Study design If a chemical is administered… If this is a reproductive toxicity study… Assay type For a microarray study, MIAME plus…
  • 5. (formerly MGED) Functional Genomics Data Society Standards for exchange of phenotype data for reuse in meta analysis. The following information is strongly suggested / required with a publication / data deposition (in addition to MIAME requirements): (1) statement of informed consent; the data may (not) be used in additional analysis. (2) enrollment criteria / exclusion criteria (3) population characteristics: race/ethnicity/strain, age, gender (4) tissue or cellular makeup of specimen (5) non-omics phenotype for each participant (subject or group) Different study or trial designs would have additional reporting requirements: (A) study involving a disease (B) study involving chemical exposure (C) study involving surgical intervention (D) Observational or epidemiological study
  • 6. HESI Baseline Animal Database Led by Karol Thompson, FDA, CDER Collected over 500 microarrays from liver or kidney of control animals from 16 different institutions, with metadata on protocol factors such as diet, housing, administration of vehicle, amount of material on the array, array type, animal weight and age at sacrifice. Multivariate analysis of the 17 mostly complete factors identified the factors contributing the most to variability in this data set. The study factors that emerged as key sources of variability included gender, organ section, strain, and fasting state.
  • 7. HESI Baseline Animal Database Led by Karol Thompson, FDA, CDER Collected over 500 microarrays from liver or kidney of control animals from 16 different institutions, with metadata on protocol factors such as diet, housing, administration of vehicle, amount of material on the array, array type, animal weight and age at sacrifice. Multivariate analysis of the 17 mostly complete factors identified the factors contributing the most to variability in this data set. The study factors that emerged as key sources of variability included gender, organ section, strain, and fasting state.