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http://oligogenome.stanford.edu/

The Stanford Human OligoGenome Project hosts a database of capture oligonucleotides for conducting high-throughput targeted resequencing of the human genome. This set of capture oligonucleotides covers over 92% of the human genome for build 37 / hg19 and over 99% of the coding regions defined by the Consensus Coding Sequence (CCDS). The capture reaction uses a highly multiplexed approach for selectively circularizing and capturing multiple genomic regions using the in-solution method developed in Natsoulis et al, PLoS One 2011. Combined pools of capture oligonucleotides selectively circularize the genomic DNA target, followed by specific PCR amplification of regions of interest using a universal primer pair common to all of the capture oligonucleotides. Unlike multiplexed PCR methods, selective genomic circularization is capable of efficiently amplifying hundreds of genomic regions simultaneously in multiplex without requiring extensive PCR optimization or producing unwanted side reaction products. Benefits of the selective genomic circularization method are the relative robustness of the technique and low costs of synthesizing standard capture oligonucleotide for selecting genomic targets.

Proper citation: OligoGenome (RRID:SCR_006025) Copy   

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https://crispresso.pinellolab.partners.org/submission

Software suite of tools to qualitatively and quantitatively evaluate outcomes of genome editing experiments in which target loci are subject to deep sequencing and provides integrated, user friendly interface. Used for analysis of CRISPR-Cas9 genome editing outcomes from sequencing data. CRISPResso2 provides accurate and rapid genome editing sequence analysis.Used for analysis of deep sequencing data for rapid and intuitive interpretation of genome editing experiments.

Proper citation: CRISPResso (RRID:SCR_021538) Copy   

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https://bitbucket.org/biobakery/biobakery/wiki/Home

Analysis environment and collection of individual software tools to process raw shotgun metagenome or metatranscriptome sequencing data for quantitative microbial community profiling. Used for a metaomics data analysis., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: biobakery (RRID:SCR_016596) Copy   

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https://github.com/hakyimlab/PrediXcan

Software tool to detect known and novel genes associated with disease traits and provide insights into the mechanism of these associations. Used to test the molecular mechanisms through which genetic variation affects phenotype.

Proper citation: PrediXcan (RRID:SCR_016739) Copy   

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https://pachterlab.github.io/sleuth/about

Software tool for analysis of RNA-Seq experiments for which transcript abundances have been quantified with kallisto. Used for the differential analysis of gene expression data that utilizes bootstrapping in conjunction with response error linear modeling to decouple biological variance from inferential variance.

Proper citation: sleuth (RRID:SCR_016883) Copy   

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http://www.bx.psu.edu/~giardine/vision/

International project to analyze mouse and human hematopoiesis, and provide a tractable system with clear clinical significance and importance to NIDDK. Collection of information from the flood of epigenomic data on hematopoietic cells as catalogs of validated regulatory modules, quantitative models for gene regulation, and a guide for translation of research insights from mouse to human.

Proper citation: ValIdated Systematic IntegratiON of epigenomic data (RRID:SCR_016921) Copy   

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https://www.sciencescott.com/pyminer

Software tool to automate cell type identification, cell type-specific pathway analyses, graph theory-based analysis of gene regulation, and detection of autocrine-paracrine signaling networks. Finds Gene and Autocrine-Paracrine Networks from Human Islet scRNA-Seq.

Proper citation: PyMINEr (RRID:SCR_016990) Copy   

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https://www.nature.com/articles/s41467-018-03367-w

Nanodroplet processing platform for deep and quantitative proteome profiling of 10 to 100 mammalian cells. It enhances efficiency and recovery of sample processing by downscaling processing volumes.

Proper citation: nanoPOTS (RRID:SCR_017129) Copy   

•   Source: SciCrunch Registry

Ratings or validation data are available for this resource

https://www.zurich.ibm.com/cellcycletracer/

Software tool as supervised machine learning algorithm that classifies and sorts single cell mass cytometry data according to their cell cycle, which allows to correct for cell cycle state and cell volume heterogeneity. Reveals signaling relationships and cell heterogeneity that were otherwise masked. Computational method to quantify cell cycle and cell volume variability.

Proper citation: CellCycleTRACER (RRID:SCR_017128) Copy   

•   Source: SciCrunch Registry

https://github.com/epistasislab/hibachi

Software tool that creates data sets with particular characteristics. Method and open source software for simulating complex biological and biomedical data to aid in comparing and evaluating machine learning methods.

Proper citation: Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (RRID:SCR_017140) Copy   

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http://drc.ucsf.edu/

One of sixteen research centers established by the National Institute of Diabetes and Digestive and Kidney Diseases that fosters research and training in the areas of diabetes and related endocrine and metabolic disorders.

Proper citation: University of California San Francisco Diabetes Research Center (RRID:SCR_015102) Copy   

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http://drtc.bsd.uchicago.edu

Center which promotes multidisciplinary research in diabetes through raising awareness and interest in fundamental and clinical research, enhancing diabetes research, education and training opportunities, and providing core services that leverage funding and unique expertise.

Proper citation: University of Chicago Diabetes Research and Training Center (RRID:SCR_015114) Copy   

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http://www.ucdenver.edu/academics/colleges/PublicHealth/research/centers/CAIANH/cdtr/Pages/CAIANDTR.aspx

Center dedicated to extending prevention and management research of proven efficacy to both clinical and community settings, with the goal of improving the diabetes-related health of Native Americans. The CAIANDTR provides core services and consultation locally, regionally, and nationally in areas relevant to NIDDK's translational research agenda.

Proper citation: Center for American Indian and Alaska Native Diabetes Translational Research (RRID:SCR_015146) Copy   

•   Source: SciCrunch Registry

http://www.med.upenn.edu/idom/

Diabetes research center created to address the prevalence of diabetes and obesity. The goal of the center is to support and develop successful approaches to the prevention, treatment, and cure of diabetes mellitus and obesity.

Proper citation: Penn Diabetes Research Center (RRID:SCR_015123) Copy   

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http://diabetesresearchcenter.dom.wustl.edu

University-affiliated center established to support and enhance research in diabetes and related metabolic diseases. Its long-term goal is the development of new preventive strategies and therapies aimed at improving the lives of Americans with or at risk for diabetes.

Proper citation: Washington University School of Medicine Diabetes Research Center (RRID:SCR_015138) Copy   

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https://diabetes.med.umich.edu/partners/michigan-center-diabetes-translational-research-mcdtr

Multidisciplinary unit of the University of Michigan funded by National Institute of Diabetes and Digestive and Kidney Diseases/National Institutes of Health. MCDTR is one of seven NIH Centers funded to focus on type 2 translational research in diabetes with mission to establish, promote, and enhance multidisciplinary collaboration among researchers directed at prevention and control of diabetes, its complications, and comorbidities, by providing access to specialized expertise and resources.

Proper citation: Michigan Center for Diabetes Translational Research (RRID:SCR_015187) Copy   

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http://cihd.cores.utah.edu

Research center for iron and hematology research. It hosts cores that provide services for mutation generation and detection, metabolomics, and iron and heme experiments and research. In addition to these cores, it has an Enrichment Program, and Internal and External Advisory Committees, and a Pilot and Feasibility program.

Proper citation: Center for Iron and Heme Disorders at the University of Utah (RRID:SCR_015341) Copy   

•   Source: SciCrunch Registry

http://sph.unc.edu/norc/norc-home/

Center whose goals include providing resources and support to investigators conducting multidisciplinary and interdisciplinary research in nutritional sciences and obesity, strengthening clinical nutrition training programs for medical students, practicing physicians, and allied health personnel, and translating findings from obesity and nutrition research to the general public.

Proper citation: University of North Carolina at Chapel Hill Nutrition and Obesity Research Center (RRID:SCR_015462) Copy   

•   Source: SciCrunch Registry

https://ww2.mc.vanderbilt.edu/ddrc/

Center whose objectives include promoting digestive diseases-related research in an integrative, collaborative and multidisciplinary manner, developing and implementing programs for attracting, training, and retaining young investigators in digestive disease-related research, and facilitating the transfer of basic research discoveries to improvements in prevention and/or clinical care.

Proper citation: Vanderbilt Digestive Disease Research Center (RRID:SCR_015225) Copy   

•   Source: SciCrunch Registry

http://depts.washington.edu/uwnorc/

Research center that facilitates the integration and coordination of the ongoing activities of the University of Washington system. Its overarching goals are to foster interdisciplinary research collaborations, stimulate new research activities, improve nutrition and obesity education, and facilitate optimal nutritional management of patients.

Proper citation: University of Washington Nutrition and Obesity Research Center (RRID:SCR_015475) Copy   

•   Source: SciCrunch Registry