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The vervet monkey (Chlorocebus aethiops) or African green monkey is among the most important non-human primate (NHP) models for biomedical research. An international collaborative effort (funded through several sources) has generated resources to enable large-scale genetic and genomic investigations of the vervet.
This site aims to provide integrated access to these resources, some of which are already available for use by the biomedical research community, others of which are still in early stages of development. This site will be updated frequently, and will provide signposts to such updates.
Current and planned resources (indicating the source of funding where applicable) include the following:
Vervet Genome Sequencing Project (NIH)
The Vervet Genome Sequencing Project has been assigned by the NHGRI to the Washington University Genome Center. The project is being led by Dr Wes Warren and Dr Elaine Mardis.
The VGSP will:
- generate a high quality genome sequence for a reference animal from the VRC
- identify genome wide SNPs for vervet subspecies
- detect genome wide genome rearrangements
- initiate sequence-based transcriptome analysis resources.
The VGSP plan has been developed in coordination with the NCRR integrated vervet genomics application.
As the VGSP progresses, we will provide links to those resources and coordinate with them to provide data access and data visualization.
Physical Map Project (Genome Canada and Genome Quebec)  [Go]
Genetic Map (NIH)
A variety of resources for analysis of the vervet transcriptome are either already available (and searchable from this site), under development, or in planning.
Currently available microarray resources developed from the Vervet Research Colony are based on widely utilized platforms (Affymetrix and Illumina human arrays) and permit analysis of expression variability across tissues (including several brain regions) and between individuals.
- Illumina expression resource
The Illumina gene expression resource using the HumanRef-8 v2 chip, including 22,184 probes representing 18,189 unique human genes (or 20,424 unique transcripts from Reference Sequence (RefSeq) database1, Release 17) consists of two sample sets:
1. Brain-Blood dataset comprising blood and 8 brain regions (cerebellar vermis, pulvinar, head of caudate, hippocampus, frontal pole, dorsolateral prefrontal cortex, orbital frontal cortex, and occipital pole) from 12 males. This dataset has been deposited in GEO (GSE15301) and is available here:
Jasinska AJ, Service S, Choi OW, Deyoung J, Grujic O, Kong SY, Jorgensen MJ, Bailey J, Breidenthal S, Fairbanks LA, Woods RP, Jentsch JD, Freimer NB. Identification of Brain Transcriptional Variation Reproduced in Peripheral Blood: an Approach for Mapping Brain Expression Traits.
Hum Mol Genet. 2009 Aug 19. [Epub ahead of print] PubMed PMID: 19692348
2. Biological Replicate dataset consisting of duplicate samples from 18 individuals in the VRC.
The Illumina gene expression resource currently allows searching, by gene or probe for expression in different tissues in the Brain-Blood dataset and expression reproducibility in blood from the Biological Replicate dataset.
This resource was created to compare brain and peripheral blood expression and to account for environmental effects and random expression signal changes due to technical factors.
Among 22,184 Illumina probes, 6,550 showed a Spearman correlation of > 55% between expression levels in peripheral blood and one or more brain regions. A total of 8,025 probes showed a percent of variability attributable to the inter-individual component (PV) > 55%. Such probes where most of the variability is attributable to the between monkey component show more inter-monkey variability than intra-monkey variability and therefore have less variability between brain and blood tissue than between monkeys.
In interpreting these results, it should be noted that, in the absence of sequence data, some of the apparent vervet-vervet variation in both the periphery and in the brain may be due to sequence-related variations in probe affinity across animals rather than true gene expression differences. The replicate data serve to identify genes with peripheral expression levels that are sensitive to temporal, environmental or stress-related variation. Characterizing within-vervet variations in gene expression is important in generating a more complete framework within which to interpret between-vervet variation.
To the Illumina gene expression resource
Download Illumina summary statistics (excel spreadsheet)