LightSwitch 3’UTR Reporter GoClone Collection
The LightSwitch 3’UTR reporter GoClone collection includes transfection-ready luciferase reporter constructs utilizing the novel RenSP luciferase technology for over 12,000 human 3’UTRs and highly opt...
SwitchGear Genomics
At SwitchGear Genomics we are interested in the set of switching and interrupting devices that regulate expression of genes across the human genome.
SwitchGear Genomics was founded in March of 2005 by Dr. Richard Myers, Dr. Nathan Trinklein, and Dr. Shelley Force Aldred of Stanford University. The goal of SwitchGear Genomics is to provide custom research services and experimental tools to aid researchers in large-scale studies of transcriptional regulation. The inspiration for forming this company grew out of our previous work in the field of functional genomics and most recently as members of the ENCODE (Encyclopedia of DNA Elements) Project Consortium funded by the National Human Genome Research Institute. The goal of the ENCODE Project is to apply new technologies and genomic scaling of existing methodologies to discover and characterize functional elements in 1% of the human genome. Our first-hand experience in the field of functional genomics and specifically our involvement in the ENCODE project has highlighted the unmet needs of researchers studying functional elements in the genome.
Our positions as leaders in the fields of computational regulatory element prediction, experimental validation, functional characterization, and data analysis put us in a unique position to fulfill these unmet needs by providing the research community with high-throughput tools to assay regulatory element function across the genome. The new tools and services offered by SwitchGear Genomics will enable researches to gather novel data, greatly enrich existing genomic datasets, and focus on experiments rather than resource development. The experimental results from the ENCODE project and other functional genomics studies demonstrate the tremendous value of combining multiple independent datasets to draw conclusions.
LightSwitch Single Gene GoClone Reporters
The LightSwitch promoter reporter GoClone collection includes transfection-ready luciferase reporter constructs utilizing the novel RenSP luciferase technology for over 17,000 human promoters and high...
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At SwitchGear Genomics we are interested in the set of switching and interrupting devices that regulate expression of genes across the human genome.
SwitchGear Genomics was founded in March of 2005 by Dr. Richard Myers, Dr. Nathan Trinklein, and Dr. Shelley Force Aldred of Stanford University. The goal of SwitchGear Genomics is to provide custom research services and experimental tools to aid researchers in large-scale studies of transcriptional regulation. The inspiration for forming this company grew out of our previous work in the field of functional genomics and most recently as members of the ENCODE (Encyclopedia of DNA Elements) Project Consortium funded by the National Human Genome Research Institute. The goal of the ENCODE Project is to apply new technologies and genomic scaling of existing methodologies to discover and characterize functional elements in 1% of the human genome. Our first-hand experience in the field of functional genomics and specifically our involvement in the ENCODE project has highlighted the unmet needs of researchers studying functional elements in the genome.
Our positions as leaders in the fields of computational regulatory element prediction, experimental validation, functional characterization, and data analysis put us in a unique position to fulfill these unmet needs by providing the research community with high-throughput tools to assay regulatory element function across the genome. The new tools and services offered by SwitchGear Genomics will enable researches to gather novel data, greatly enrich existing genomic datasets, and focus on experiments rather than resource development. The experimental results from the ENCODE project and other functional genomics studies demonstrate the tremendous value of combining multiple independent datasets to draw conclusions.