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https://med.stanford.edu/cancer/research/shared-resources/tissue-procurement.html

Procures and provides needed tissue specimens to Stanford Cancer Institute investigators to support cancer related research. Services include collecting and banking freshly frozen tumor and normal tissue from surgical material and autopsy specimens;Providing fresh tumor tissue for viable cell studies;Processing and banking blood from cancer patients;Maintaining tissue database with links to clinicopathological data;Providing histological staining and pathological review;Coordinating patient consent and assuring regulatory compliance.Provides portal to virtual bank which links inventories of specialized satellite repositories, including collections of hematological and neurosurgical specimens.

Proper citation: Stanford University School of Medicine Cancer Institute Tissue Procurement Shared Resource Core Facility (RRID:SCR_023387) Copy   

•   Source: SciCrunch Registry

https://med.stanford.edu/ctru.html

Collecting and banking freshly frozen tumor and normal tissue from surgical material and autopsy specimens.Provides fresh tumor tissue for viable cell studies,processes and banks blood from cancer patients,maintains tissue database with links to clinicopathological data,providing histological staining and pathological review,coordinating patient consent and assuring regulatory compliance.

Proper citation: Stanford University School of Medicine Clinical and Translational Research Unit Core Facility (RRID:SCR_023386) Copy   

•   Source: SciCrunch Registry

https://cs.stanford.edu/csdcf/about-us

Academic service center specializing in planning, installation and maintenance of research computer systems.

Proper citation: Stanford University Computer Science Department Computer Core Facility (RRID:SCR_023383) Copy   

•   Source: SciCrunch Registry

https://www.stanfordmicrofluidics.com/

Core in part of Bioengineering Department at Stanford University and is the main service provider of integrated microfluidic devices on Stanford campus servicing universities and academic institutions.

Proper citation: Stanford University Microfluidics Foundry Core Facility (RRID:SCR_024527) Copy   

•   Source: SciCrunch Registry

https://ampf.stanford.edu/

Advanced 3D printing facility enables digital design, prototyping, and small scale production as integrated innovation hub and shared use facility for innovation and commercialization for investigators spanning the Stanford campus and Silicon Valley.

Proper citation: Stanford University Additive Manufacturing and Printing Core Facility (RRID:SCR_024529) Copy   

•   Source: SciCrunch Registry

https://geomicrobiologylabs.stanford.edu/

Geomicrobiology analytical facilities consist of four different labs in the Green Earth Sciences Building. Main areas of study are: molecular, bio(geo)chemical, and ecological aspects of microbial cycling of nitrogen, carbon, and metals in environment diversity, distribution, and activity of microorganisms driving greenhouse gas cycling in ocean and beyond molecular biomarkers in modern organisms to refine our interpretations of microbial fossil record.

Proper citation: Stanford Geomicrobiology Shared Laboratories Core Facility (RRID:SCR_025000) Copy   

•   Source: SciCrunch Registry

https://ecosystemslab.sites.stanford.edu/

Ecosystems Laboratory provides bench space, laboratory supplies, chemicals, sample preparation equipment, and sample storage space to enhance and facilitate research within Stanford University community.

Proper citation: Stanford Ecosystems Shared Laboratory Core Facility (RRID:SCR_026190) Copy   

•   Source: SciCrunch Registry

https://maria.stanford.edu/index.php

Web multimodal recurrent neural network tool designed to predict HLA-II (Human Leukocyte Antigen class II) peptide ligand presentation. It uses cell HLA alleles, peptide sequences, and source genes to evaluate antigen presentation. Used for predicting the likelihood of antigen presentation from a gene of interest in the context of specific HLA class II alleles.

Proper citation: MARIA (RRID:SCR_028673) Copy   

•   Source: SciCrunch Registry

http://www.pharmgkb.org/

Database and central repository for genetic, genomic, molecular and cellular phenotype data and clinical information about people who have participated in pharmacogenomics research studies. The data includes, but is not limited to, clinical and basic pharmacokinetic and pharmacogenomic research in the cardiovascular, pulmonary, cancer, pathways, metabolic and transporter domains. PharmGKB welcomes submissions of primary data from all research into genes and genetic variation and their effects on drug and disease phenotypes. PharmGKB collects, encodes, and disseminates knowledge about the impact of human genetic variations on drug response. They curate primary genotype and phenotype data, annotate gene variants and gene-drug-disease relationships via literature review, and summarize important PGx genes and drug pathways. PharmGKB is part of the NIH Pharmacogenomics Research Network (PGRN), a nationwide collaborative research consortium. Its aim is to aid researchers in understanding how genetic variation among individuals contributes to differences in reactions to drugs. A selected subset of data from PharmGKB is accessible via a SOAP interface. Downloaded data is available for individual research purposes only. Drugs with pharmacogenomic information in the context of FDA-approved drug labels are cataloged and drugs with mounting pharmacogenomic evidence are listed.

Proper citation: PharmGKB (RRID:SCR_002689) Copy   

•   Source: SciCrunch Registry

http://igenomed.stanford.edu/~junhee/JETTA/rnaseq.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented July 6, 2017. Software to detect alternatively spliced exons between two conditions, for example, between two groups of treated and untreated patients in a typical clinical study.

Proper citation: JETTA (RRID:SCR_003091) Copy   

•   Source: SciCrunch Registry

http://www.stanford.edu/group/nusselab/cgi-bin/wnt/

A resource for members of the Wnt community, providing information on progress in the field, maps on signaling pathways, and methods. The page on reagents lists many resources generously made available to and by the Wnt community. Wnt signaling is discussed in many reviews and in a recent book. There are usually several Wnt meetings per year.

Proper citation: Wnt homepage (RRID:SCR_000662) Copy   

•   Source: SciCrunch Registry

https://github.com/pmelsted/BFCounter

Software program for counting k-mers in DNA sequence data. It identifies all the k-mers that occur more than once in a DNA sequence data set using a Bloom filter, a probabilistic data structure that stores all the observed k-mers implicitly in memory with greatly reduced memory requirements.

Proper citation: BFCounter (RRID:SCR_001248) Copy   

•   Source: SciCrunch Registry

https://github.com/nolanlab/cytospade

Cytoscape plugin that provides a high-performance implementation of an interface for the Spanning-tree Progression Analysis of Density-normalized Events (SPADE) algorithm for tree-based analysis and visualization of high-dimensional cytometry data.

Proper citation: CytoSPADE (RRID:SCR_001457) Copy   

•   Source: SciCrunch Registry

https://cran.r-project.org/src/contrib/Archive/PoissonSeq/

Software package that implements a method for normalization, testing, and false discovery rate estimation for RNA-sequencing data.

Proper citation: PoissonSeq (RRID:SCR_001784) Copy   

•   Source: SciCrunch Registry

https://med.stanford.edu/sfgf.html

Stanford Genomics formerly Stanford Functional Genomics Facility provides services for high throughput sequencing, single cell assays, gene expression and genotyping studies utilizing microarray and real time PCR, and related services. High throughput sequencing (Illumina HiSeq 4000, NextSeq 500, MiSeq and MiniSeq), microarray gene expression and genotyping services (Affymetrix, Agilent and Illumina). Provides 24/7 access to instruments, equipment and software utilized within genomics field.

Proper citation: Stanford Genomics Service Center Core Facility (RRID:SCR_002050) Copy   

•   Source: SciCrunch Registry

http://lilab.stanford.edu/SNPiR/

Software for reliable Identification of Genomic Variants Using RNA-seq Data.

Proper citation: SNPiR (RRID:SCR_000557) Copy   

•   Source: SciCrunch Registry

http://www.stanford.edu/~cpatton/maxc.html

A series of programs for determining the free metal concentration in the presence of chelators or total metal given a desired free concentration.

Proper citation: MAXCHELATOR (RRID:SCR_000459) Copy   

•   Source: SciCrunch Registry

http://highwire.stanford.edu/

A division of the Stanford University Libraries, which produces the online versions of journals and other scholarly content.

Proper citation: HighWire Press (RRID:SCR_000761) Copy   

•   Source: SciCrunch Registry

http://www.hybrow.org/

A prototype bioinformatics tool for designing hypotheses and evaluating them for consistency with existing knowledge. It consists of a modeling framework with the ability to accommodate diverse biological information sources, an event-based ontology for representing biological processes at different levels of detail, a database to query information in the ontology, and programs to perform hypothesis design and evaluation. There are five key components involved in making HyBrow work. # The Event-based ontology for representing biological knowledge # The Discreet Event Systems based conceptual framework which provides the theory that allows us to make statements in a context free formal language (made up of the ontology) and evaluate the statements for validity using constraints declared on existing data # The rule library that provides the steps to apply those constraints and decide support, contradiction or no comment. # The relational database that stores existing information structured into the ontology. # The user interface.

Proper citation: HyBrow (Hypothesis Browser) (RRID:SCR_006272) Copy   

•   Source: SciCrunch Registry

http://vis.stanford.edu/wrangler/

Wrangler is an interactive tool for data cleaning and transformation. Spend less time formatting and more time analyzing your data. Why wrangle? * Too much time is spent manipulating data just to get analysis and visualization tools to read it. Wrangler is designed to accelerate this process: spend less time fighting with your data and more time learning from it. * Wrangler allows interactive transformation of messy, real-world data into the data tables analysis tools expect. Export data for use in Excel, R, Tableau, Protovis, ... * Want to learn more about Wrangler''s design? Take a look at our research paper. * Wrangler is still a work-in-progress. Please share your feedback and feature requests!

Proper citation: DataWrangler (RRID:SCR_006335) Copy   

•   Source: SciCrunch Registry