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http://alizadehlab.stanford.edu/

This is an open-source Mouse Exonic Evidence-Based Oligonucleotide Chip (MEEBOChip), and are in the process of building the human counterpart, HEEBOChip. The set of 70mers for MEEBOChip is already available from Illumina, Inc., with synthesis of HEEBOChip 70mers in progress. Both arrays are based on a novel selection of exonic long-oligonucleotides (70-mers) from a genomic annotation of the corresponding complete genome sequences, using a transcriptome-based annotation of exon structure for each genomic locus. Using a combination of existing and custom-tailored tools and datasets (including millions of mRNA and EST sequences), we built and performed a systematic examination of transcript-supported exon structure for each genomic locus at the base-pair level (i.e., exonic evidence). This strategy allowed them to select both constitutive and in many cases alternative exons for nearly every gene in the corresponding genome (e.g., protocadherin locus), allowing an unprecedented exploration of human and mouse biology. Furthermore, they used experimentally derived data to hone the selection of these 70mers, helping maximize their performance under typical fluorescent labeling and hybridization conditions. Specifically, they applied and refined the ArrayOligoSelector algorithm from Joe DeRisis laboratory to select 70mers, considering not only their uniqueness (i.e., hybridization specificity) within the content of the entire genome, but also to overcome the known biases of labeling and hybridization methods (e.g., 3-biased reverse transcription and in vitro transcription reactions).

Proper citation: Alizadehlab: MeeboChip and HeeboChip Open Source Project (RRID:SCR_008384) Copy   

•   Source: SciCrunch Registry

http://www.simbiome.org

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 18, 2015. Simbiome is a curated, online electronic resource that organizes and presents relevant resources for physics-based simulation of biomedical structures and related entities in biology and life sciences.

Proper citation: Simbiome (RRID:SCR_013640) Copy   

•   Source: SciCrunch Registry

http://med.stanford.edu/compmed/education/phd_training.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented on January 27,2022.This program helps address the shortage of veterinarians who are trained for independent research. It supports veterinarians during the first 3 years of their PhD training. Trainees can participate in any graduate program involved in biomedical research at Stanford University: Biochemistry, Biological Sciences, Biophysics, Cancer Biology, Developmental Biology, Genetics, Immunology, Microbiology & Immunology, Molecular & Cellular Physiology, Molecular Pharmacology, Neurosciences, Structural Biology, Bioengineering, and Biomedical Informatics (graduate programs in biomedical research). All share common features: :1. Year 1 focuses on a limited group of courses and laboratory rotations. :2. Subsequent years focus on developing critical thinking via seminars and journal clubs and on independent research. :3. Annual retreats to present and learn about ongoing research in the graduate program. :4. Emphasis on mastering communications skills essential for success in science: oral presentation, writing manuscripts and grant proposals. :5. Developing an appreciation of the role of scientists in the context of society with emphasis on ethical and professional responsibility. Sponsors: It is funded by a T32 training grant from the NIH.

Proper citation: Postdoctoral Program for Veterinarians (RRID:SCR_008296) Copy   

•   Source: SciCrunch Registry

http://www.jcsg.org/

The JCSG is a multi-institutional consortium that aims to explore the expanding protein universe to find new challenges and opportunities to significantly contribute to new biology, chemistry and medicine through development of HT approaches to structural genomics. The mission of JCSG is to to operate a robust HT protein structure determination pipeline as a large-scale production center for PSI-2. A major goal is to ensure that innovative high-throughput approaches are developed that advance not only structural genomics, but also structural biology in general, via investigation of large numbers of high-value structures that populate protein fold and family space and by increasing the efficiency of structure determination at substantially reduced cost. The JCSG centralizes each core activity into single dedicated sites, each handling distinct, but interconnected objectives. This unique approach allows each specialized group to focus on its own area of expertise and provides well-defined interfaces among the groups. In addition, this approach addresses the requirements for the scalability needed to process large numbers of targets at a greatly reduced cost per target. JCSG production groups are: - Administrative Core - Bioinformatics Core - Crystallomics Core - Structure Determination Core - NMR Core JCSG is deeply committed to the development of new technologies that facilitate high throughput structural genomics. The areas of development include hardware, software, new experimental methods, and adaptation of existing technologies to advance genome research. In the hardware arena, their commitment is to the development of technologies that accelerate structure solution by increasing throughput rates at every stage of the production pipeline. Therefore, one major area of hardware development has been the implementation of robotics. In the software arena, they have developed enterprise resource software that track success, failures, and sample histories from target selection to PDB deposition, annotation and target management tools, and helper applications aimed at facilitating and automating multiple steps in the pipeline. Sponsors: The Joint Center for Structural Genomics is funded by the National Institute of General Medical Sciences (NIGMS), as part of the second phase of the Protein Structure Initiative (PSI) of the National Institutes of Health (U54 GM074898).

Proper citation: Joint Center for Structural Genomics (RRID:SCR_008251) Copy   

•   Source: SciCrunch Registry