dkNET Webinar: The 4DN Data Portal - Data, Resources and Tools to Help Elucidate Nuclear Organization in Space and Time 05/10/2024
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Presenter: Andrew Schroeder, PhD. Project Manager & Senior Data Curator, 4D Nucleome Data Coordination and Integration Center (4DN-DCIC), Park Lab, Department of Biomedical Informatics, Harvard Medical School Abstract The Common Fund 4D Nucleome program, currently in its 9th year, is a consortium of researchers that aims to understand the principles behind the three-dimensional organization of the nucleus and how this organization can change over time to affect a variety of cellular processes. The 4DN Data Portal (data.4dnucleome.org) is an expanding resource hosting data generated by the 4DN Network and other reference nucleomics data sets. The portal provides tools for search, exploration, visualization, and download. An overview of the data portal, highlighting available data, how it can be found, visualized and used for analyses will be presented. The top 3 key questions that the 4DN data portal can answer: 1. Are there significant sites of long-range chromatin contacts near my gene or region of interest? 2. What omics datasets are available for my tissue of interest? 3. Are there imaging datasets available that are relevant to my tissue of interest? Upcoming webinars schedule: https://dknet.org/about/webinar
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Presenter: Pieter Dorrestein, PhD, Professor, Skaggs School of Pharmacy and Pharmaceutical Sciences, Department of Pharmacology and Pediatrics, University of California San Diego
Abstract
In the analysis of organs, volatilome, or biofluids, the microbiome influences 15-70% of detectable mass spectrometry molecules. Typically, only 10% of human untargeted metabolomics data can be assigned a molecular structure, with merely 1-2% traceable to microbial origins. Human microbiomes contribute metabolites through the microbial metabolism of host-derived substances, digestion of food and beverage molecules, and de novo assembly using proteins encoded by genetic elements. Despite the significance of microbiome-derived metabolites to human health, there is no centralized knowledge base for community access. To address this, the "Collaborative Microbial Metabolite Center" (CMMC) leverages expertise in mass spectrometry, microbiome innovation, and the GNPS ecosystem to built a knowledgebase. It aims to create a user-accessible microbiome resource, enrich bioactivity knowledge, and facilitate data deposition. The CMMC includes the construction of a knowledge base, MicrobeMASST tool, and health phenotype enrichment workflows, the construction and use will be discussed in this presentation. The use of this ecosystem will be exemplified by the discovery of 20,000 bile acids, many of which were shown to be of microbial origin and linked to diet and IBD.
The top 3 key questions that this resource can answer:
1. How can we leverage the 1000’s of public metabolomics studies to discover microbial metabolites and their organ distributions as well as their phenotypic, including health, associations?
2. If one has an unknown molecule, how can one assess what microbes make a molecule without known structure?
3. How can one contribute to the expansion of the knowledgebase on microbial metabolites?
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: An Encyclopedia of the Adipose Tissue Secretome to Identify Me...
Presenter: Paul Cohen, MD, PhD, Albert Resnick, M.D. Associate Professor, Rockefeller University
Abstract
White and brown adipocytes not only play a central role in energy storage and combustion but are also dynamic secretory cells that secrete signaling molecules linking levels of energy stores to vital physiological systems. Disruption of the signaling properties of adipocytes, as occurs in obesity, contributes to insulin resistance, type 2 diabetes, and other metabolic disorders. Fat cells have been estimated to secrete over 1,000 polypeptides and microproteins and an even larger number of small molecule metabolites. The great majority of the adipocyte secretome has not been defined or characterized. A major obstacle has been the lack of suitable technologies to quantitatively identify circulating proteins and metabolites, determine their cellular origin, and elucidate their function. Building on key innovations in chemical biology and mass spectrometry, our team is generating an encyclopedia of the white and brown adipocyte secretome in mouse models and humans. Our work has the potential to identify new secreted mediators with roles in obesity, type 2 diabetes, and metabolic diseases, provide a crucial resource for researchers and clinicians, and lead to new biomarkers and therapies.
The top 3 key questions that this resource can answer:
1. What techniques can be used to characterize the secretome of a cell type in vitro and in vivo?
2. What is the full complement of proteins and metabolites secreted by different kinds of adipocytes?
3. How should one prioritize uncharacterized secreted mediators for functional study?
Resource link: https://secrepedia.org/
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: A Single Cell Atlas of Human and Mouse White Adipose Tissue 11...
Presenter: Margo Emont, PhD. Instructor, Beth Israel Deaconess Medical Center/Harvard Medical School
Abstract
White adipose tissue, once regarded as morphologically and functionally bland, is now recognized to be dynamic, plastic and heterogenous, and is involved in a wide array of biological processes including energy homeostasis, glucose and lipid handling, blood pressure control and host defense. High-fat feeding and other metabolic stressors cause marked changes in adipose morphology, physiology and cellular composition, and alterations in adiposity are associated with insulin resistance, dyslipidemia and type 2 diabetes. Here we provide detailed cellular atlases of human and mouse subcutaneous and visceral white fat at single-cell resolution across a range of body weight. We identify subpopulations of adipocytes, adipose stem and progenitor cells, vascular and immune cells and demonstrate commonalities and differences across species and dietary conditions. We link specific cell types to increased risk of metabolic disease and provide an initial blueprint for a comprehensive set of interactions between individual cell types in the adipose niche in leanness and obesity. These data comprise an extensive resource for the exploration of genes, traits and cell types in the function of white adipose tissue across species, depots and nutritional conditions.
The top 3 key questions that this resource can answer:
1. How specific is my gene of interest to a particular cell type in adipose tissue?
2. Is the gene/pathway that I am studying in mouse adipose tissue also present in human adipose tissue (and is it regulated similarly in low vs high body weight)?
3. What are the changes in gene expression in a specific cell type at low vs high body weight?
Resource link:
https://singlecell.broadinstitute.org/single_cell/study/SCP1376/a-single-cell-atlas-of-human-and-mouse-white-adipose-tissue#study-summary
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar "The National Sleep Research Resource (NSRR) - Opportunities fo...
Presenter: Susan Redline, MD, MPH, Peter C. Farrell Professor of Sleep Medicine, Professor of Epidemiology, Harvard T.H. Chan School of Public Health
Abstract
Experimental, clinical and epidemiological studies have identified multiple inter-relationships of sleep with glucose regulation and metabolic disease. In one meta-analysis, after overweight and family history of diabetes, the next 7 top risk factors for incident diabetes were measures of sleep health. These included poor sleep quality, insomnia, short or extremely long sleep duration, and sleep apnea; each sleep problem was associated with incident diabetes with relative risks ranging from 1.38 to 1.74. A mechanism linking sleep apnea with diabetes is through the effects of intermittent hypoxemia on insulin sensitivity. However, studies using neurophysiological markers of sleep in healthy adults showed that selective reduction of slow wave sleep reduced glucose tolerance by 23%, thus additionally suggesting the importance neurophysiological mechanisms during sleep in glucose regulation. In support of this, longitudinal epidemiological studies demonstrated that higher proportions of slow wave sleep (N3) were protective for the development of type 2 diabetes. Recent animal and human studies also point to the effects of sleep micro-architecture—specifically the coupling of slow waves and spindles- on short-term and long-term glucose regulation, possibly through the effects on signaling between the hippocampus and hypothalamus, and changes in autonomic nervous system output. Experimental data also demonstrate a prominent role of the circadian system in regulating glucose and lipid levels. In support of those studies, epidemiological associations have identified significant associations between actigraphy-based measures of sleep irregularity (a marker of circadian disruption) with incident metabolic dysfunction and hypertension. This rich data implicating sleep disturbances as drivers of metabolic disease, coupled with data indicating a high prevalence of sleep and circadian disorders in the population, suggest novel opportunities to target sleep and circadian pathways for preventing or treating metabolic dysfunction, as well as key knowledge gaps.
The National Sleep Research Resource (NSRR; sleepdata.org) provides a large and growing repository of well-annotated polysomnograms (PSGs), actigraphy studies, and questionnaires, some associated with clinical and biochemical data relevant to understanding the links between sleep and circadian disorders with metabolic disease. Notably, the NSRR includes over 50,000 PSGs, which concurrently include multiple physiological signals with high temporal resolution, allowing generation of thousands of variables summarizing dynamic physiological changes and “cross-talk” between physiological systems...(Please see https://dknet.org/about/blog/2674 for full abstract)
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Office Hours - "Are You Ready for 2023: New NIH Data Management and Sha...
For all proposals submitted on/after January 25 2023, NIH will require the sharing of data from all NIH funded studies. Do you have appropriate data management practices and sharing plans in place to meet these requirements? Have questions or need some help? Join the dkNET office hours to learn about NIH’s policy (NOT-OD-21-013) and resources that could help.
*Previous Office Hours Slides and Recording: https://dknet.org/rin/research-data-management
Upcoming Webinars Schedule: https://dknet.org/about/webinar
dkNET Webinar: Discover the Latest from dkNET - Biomed Resource Watch 06/02/2023
dkNET Webinar: Discover the Latest from dkNET - Biomed Resource Watch
Presenter: Jeffrey Grethe, PhD, dkNET Principal Investigator, University of California San Diego
Abstract
The dkNET (NIDDK Information Network) team is announcing an exciting new service - Biomed Resource Watch (BRW, https://scicrunch.org/ResourceWatch), a knowledge base for aggregating and disseminating known problems and performance information about research resources such as antibodies, cell lines, and tools. We aggregate trustworthy information from authorized sources such as Cellosaurus, Antibody Registry, Human Protein Atlas, ENCODE, and many more. In addition, BRW includes antibody specificity text mining information extracted from the literature via natural language processing. BRW provides researchers and curators an easy-to-use interface to report their claims about a specific resource. Researchers can check information about a resource before planning their experiments via BRW-enhanced Resource Reports. This new service aims to help improve efficiency in selecting appropriate resources, enhancing scientific rigor and reproducibility, and promoting a FAIR (Findable, Accessible, Interoperable, Reusable) research resource ecosystem in the biomedical research community.
Join us for a webinar to introduce the following resources & topics:
1. An overview of dkNET
2. How Resource Reports benefit you
3. Biomed Resource Watch
3.1 Navigating Biomed Resource Watch
3.2 How to Submit a Claim
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: Leveraging Computational Strategies to Identify Type 1 Diabete...
dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series
Presenter: Wenting Wu, PhD. Research Assistant Professor, Center for Diabetes and Metabolic Diseases, Department of Medical and Molecular Genetics, Associate Director of Data and Analytics Core for Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine
Abstract
Type 1 diabetes (T1D) is an immune-mediated disease that results in insulin insufficiency and affects 0.3% of the population, including both children and adults. To support clinical trial efforts, there is an urgent need to develop reliable biomarkers capable of predicting T1D risk and guiding therapeutic interventions. Recently, whole blood bulk RNA sequencing has been used to guide T1D clinical trial design and assess response to disease modifying interventions. While the use of bulk RNA sequencing is cost-effective, these datasets provide limited information about cell specific gene expression changes. Here, we aimed to apply computational strategies to deconvolute cell type composition using cell specific gene expression references. Single-cell RNA sequencing (scRNA-seq) was conducted to profile peripheral blood mononuclear cells obtained from youth within recent T1D onset and age- and sex-matched controls and identified 31 distinct cell clusters. Using this pre-defined reference dataset, we ran computational algorithms CIBERSORTx and other deconvolution methods simultaneously to deconvolute cell proportions using public clinical trial data. We focused our initial analysis on data from the TN-20 Rituximab trial, which tested the anti-CD20 monoclonal antibody rituximab vs placebo in recent onset T1D. This talk will introduce recent advances of scRNA-seq techniques and computational deconvolution methods and demonstrate that how we apply different deconvolution approaches for secondary analysis of existing clinical trial data, in the purpose of linking cell specific immune signatures associated with drug responder status.
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: Estimating Relative Beta-Cell Function During Continuous Gluco...
dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series
Presenter: Joon Ha, PhD. Associate Professor, Department of Mathematics, Howard University, Washington DC.
Abstract
The most common form of diabetes, type 2 diabetes (T2D) is a failure of insulin-secreting pancreatic beta-cells to increase insulin to the level required to maintain normal blood glucose. Thus, identifying beta-cell function and insulin sensitivity in those who are at high risk is crucial to preventing and delaying the disease. Hyper-glycemic clamp and euglycemic hyper- insulinemic clamp are considered to be gold standard measures for these quantities. However, these two methods demand highly skilled labor and thus are cost-prohibitive. Glucose challenge tests have been used to estimate beta-cell function and insulin sensitivity. The product of beta-cell function and insulin sensitivity, termed the disposition index (DI), is of great value because it measures beta-cell function relative to insulin requirements. However, glucose challenge tests are expensive and time-consuming and therefore impractical to implement in large-scale clinical studies. To address this challenge, we developed a model disposition index (mDI estimated without insulin) that does not require insulin measurements during an oral glucose tolerance test (OGTT) (Ha et al., Diabetes 2021 (70) suppl. 1). mDI outperforms the conventional oral disposition index (oDI) at predicting progression to diabetes.
To further increase access and refine the assessments of beta-cell function, we are adapting our model to calculate a model disposition index using continuous glucose monitoring (CGM). CGM has been in the spotlight of diabetes management and has revolutionized the field of medicine as they are approved for glucose monitoring and clinical decision-making in patients with diabetes. CGM devices are relatively inexpensive compared to oral glucose challenge tests, accessible, and simple to use, especially in remote or free-living environments. The CGM device continuously measures interstitial glucose every 5 minutes and provides glucose profiles for 7-14 days. Thus, there are numerous data points compared to glucose challenge tests, but the abundant data points have not previously been used for estimating metabolic parameters. We compared mDI to two widely used CGM-derived metabolic parameters for assessing metabolic status and risk, mean glucose and glycemic excursion. Both mean glucose and glycemic excursion correlated strongly with mDI. The new approach promises to be cost- effective and easy to perform and therefore implementable in large-scale clinical studies. As for specific clinical applications, estimated model parameters during OGTTs identified ethnic differences in common pathways to T2D between Pima Indians and Koreans.
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Office Hours - "Are You Ready for 2023: New NIH Data Management and Sha...
For all proposals submitted on/after January 25 2023, NIH requires data sharing from all NIH-funded studies. Do you have appropriate data management practices and sharing plans in place to meet these requirements? Have questions or need some help? Join the dkNET office hours to learn about NIH’s policy (NOT-OD-21-013) and available resources that could help.
In our upcoming session on March 3, 2023, we are pleased to invite Dr. Jeffrey Grethe, dkNET co-PI and expert on Data Management and Sharing, Dr. Rebecca Rodriguez, Repository Program Director at NIDDK, Ms. Reaya Reuss, Chief of Staff to the Deputy Director at NIDDK, and the support team members from the NIDDK Central Repository. They will be available to answer any questions you may have.
*Previous Office Hours Slides and Recording: https://dknet.org/about/blog/2535
Upcoming Webinars Schedule: https://dknet.org/about/webinar
dkNET Webinar: Postpartum Glucose Screening Among Homeless Women with Gestati...
dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series
Presenter: Rie Sakai-Bizmark, PhD. Assistant Professor, The Lundquist Institute at Harbor-UCLA Medical Center, David Geffen School of Medicine at UCLA
Abstract
Women with gestational diabetes mellitus (GDM) are at high risk of developing glucose intolerance after delivery. In the long term, women with GDM have a nearly 10-fold higher risk of developing type 2 diabetes mellitus (T2D) than women without GDM. The American Diabetes Association (ADA) and the American College of Obstetrics and Gynecology (ACOG) recommend that women with GDM undergo a 75-g oral glucose tolerance test (OGTT) between four and 12 weeks postpartum, and periodically thereafter. However, postpartum glucose screening (PGS) rate is historically low despite of various interventions to improve such rate. We hypothesized that PGS rate is lower among postpartum homeless women than their housed counterparts, and that interventions to improve PGS rate among postpartum homeless women with GDM should be tailored to their unique circumstances. The Japanese Society of Diabetes and Pregnancy (JSDP) modified the method to perform PGS with random plasma glucose (RPG) and glycated hemoglobin (HbA1c), which are simple and less invasive, to reduce the risk of COVID-19 infection by shortening the time spent in the hospital. RPG or HbA1c test do not require fasting. Therefore, homeless women who utilized care for other reasons could have the test as PGS. Given the barriers faced by homeless individuals, we hypothesize that RPG and HbA1c at healthcare utilizations during the postpartum period could be one of the strategies to identify high-risk individuals early because 1] healthcare utilizations are an opportunity for healthcare providers and social workers to educate homeless patients on GDM and their insurance eligibility and coverage for the screening, and 2] the physical barriers to health care access, which are often cited as a reason for the low PGS rate, are removed.
This proposed study will use administrative data from five states (AZ, CO, NC, NJ, and OR), which collectively include 9.3% of the US female homeless population. Each state will provide detailed, linked, multi-level, anonymized data for postpartum homeless women from four sources: 1] Medicaid claims; 2] Homeless Management Information System (HMIS); 3] birth records; and 4] the American Hospital Association (AHA) database to obtain hospital characteristics. With data from 2013 to 2020, an estimated sample size of 24,000 homeless women who delivered babies and 3,290 postpartum homeless women with GDM will be included.
First, we will estimate rates of GDM and PGS among homeless women. Second, we will estimate the cost-effectiveness of performing RPG and HbA1c tests...[Full abstract: https://dknet.org/about/blog/2581]
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: Choosing Sample Sizes for Multilevel and Longitudinal Studies ...
dkNET Webinar: Choosing Sample Sizes for Multilevel and Longitudinal Studies Analyzed with Linear Mixed Models
Presenter:
Kylie K. Harrall, MS, Research Instructor, Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus
Abstract
Planning a reproducible study requires selecting a sample size which will ensure appropriate statistical power. Free point-and-click software (Kreidler et al., Journal of Statistical Software, 2013, 10.18637/jss.v054.i10) makes it easy to select a sample size for clustered and longitudinal designs with linear mixed models. The software, a suite of training modules, and reference materials are freely available online (www.SampleSizeShop.org ). The software interface and training materials are aimed at biomedical scientists, included those funded by NIDDK. We give examples of study designs for which the software will compute power and sample size, including a study with clustering, a study with longitudinal repeated measures, and a study with multiple outcomes, where heterogeneity of response among subgroups is of interest.
The top 3 key questions that the Sample Size Shop can answer:
1. What free, online, point-and-click, wizard-style, NIH-funded, validated, published power and sample size software provides calculations for studies with clusters, longitudinal studies, and longitudinal studies with clusters?
2. Can GLIMMPSE (www.SampleSizeShop.org) compute power and sample size for randomized controlled clinical trials and observational studies funded by NIDDK?
3. Why use validated power and sample size software instead of writing simulations?
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: : FAIR Data Curation of Antibody/B-cell and T-cell Receptor Se...
Abstract
AIRR-seq data (antibody/B-cell and T-cell receptor sequences from Adaptive Immune Receptor Repertoires) can describe the adaptive immune response in exquisite detail, and comparison and analysis of these data across studies and institutions can greatly contribute to the development of diagnostics and therapeutics, including the discovery of monoclonal antibodies for treatment of autoimmune diseases.
The AIRR community has developed protocols and standards for curating, analyzing and sharing AIRR-seq data (www.airr-community.org), and supports the AIRR Data Commons, a set of geographically distributed repositories that follows the AIRR Community’s metadata standards and the FAIR principles. The ADC currently comprises > 5 Bn receptor sequences from over 86 studies and ~9000 repertoires. The data model of the ADC has recently been expanded to include gene expression and cell phenotype data from single immune receptor cells, as well as MHC/HLA genotyping.
The iReceptor Gateway (ireceptor.org) queries this AIRR Data Commons for specific “metadata”, e.g. “find all repertoires from T1D studies” or for specific CDR3 sequences (e.g., find all repertoires from healthy individuals expressing this CDR3 sequence). Data from these federated repositories can then be analyzed through the Gateway by several sophisticated analysis tools, or downloaded for further analysis offline. The iReceptor Team at Simon Fraser University has recently initiated a collaboration to greatly expand the amount of bulk and single-cell immune profiling data from T1D studies in the AIRR Data Commons. For more information on obtaining or sharing AIRR-seq data contact support@ireceptor.org.
The top 3 key questions that the Adaptive Immune Receptor Repertoire (AIRR) can answer:
1. A researcher observes that many individuals with Type 1 Diabetes express a specific B-cell or T-cell receptor compared to controls (i.e., a “public” clonotype). To what degree is this receptor observed to be public across other T1D studies or other autoimmune disease populations?
2. Can Machine Learning be used to identify individuals who will respond well to a new cancer immunotherapy based on differences in their antibody/B-cell or T-cell receptor repertoires as curated in the AIRR Data Commons?
3. Is there an association between particular HLA, immunoglobulin (IG), or T-cell receptor (TR) germline gene polymorphisms and propensity toward specific infectious or autoimmune diseases?
Presenters:
Dr. Felix Breden, Scientific Director, iReceptor
Dr. Brian Corrie, Technical Director, iReceptor
Dr. Kira Neller, Bioinformatics Director, iReceptor
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Office Hours - "Are You Ready for 2023? New NIH Data Management and Sha...
For all proposals submitted on/after January 25 2023, NIH will require the sharing of data from all NIH funded studies. Do you have appropriate data management practices and sharing plans in place to meet these requirements? Have questions or need some help? Join the dkNET office hours to learn about NIH’s policy (NOT-OD-21-013) and resources (https://dknet.org/rin/research-data-management) that could help.
Upcoming Webinars Schedule: https://dknet.org/about/webinar
dkNET Webinar "The Mission and Progress of the(sugar)science: Helping Scienti...
Abstract
The(sugar)science was launched two years ago with the aim of helping scientists who study type 1 diabetes (T1D) and related interdisciplinary fields connect globally. We also wanted to create a digital space where trainees in the field can be supported, celebrated and connected to future positions. As part of our mission, our all volunteer team created the State of the Science series (2021. 2022), connecting global thought leaders around T1D research topics for discussion with a larger scientific audience. The second State of Science series was led by women scientists following the ADA publication which highlighted the paucity of women scientists in the leadership positions in the field.
To encourage the scientific community at large to dive into pre-existing data and pull out novel hypotheses that pertain to T1D, we created and together with dkNET, hosted D-Challenge 2021 and 2022. These competitions awarded $40K and $50K respectively to those who mined data and developed the most creative and testable hypothesis as judged by scientific experts in the field. These teams were also able to have an audience with the JDRFT1D Fund as part of a "pitch polish" which facilitated their interaction with venture capital.
To date, we have hosted over 200 interviews with T1D focused scientists in academia and industry and have an audience of 35K. Our reach on social media continues to grow and our metrics indicate a robust following. We share opportunities for positions in the field, engage and support trainees and together, our young scientific team published a paper, Similarities between bacterial GAD and human GAD65: Implications in gut mediated autoimmune type 1 diabetes, PLOS, February 2022.
We are currently engaged in the build of a T1D TCR Repository. We connected the AIRR data commons community with top TCR scientists in the field to begin this community based venture. It has the possibility to be incredibly instructive in defining the prodrome , which will further inform the field as it pertains to understanding the etiology of T1D.
Current team members that will join the discussion today will be Neha Mejety, Johns Hopkins University undergraduate and Tiffany Richardson, doctoral degree candidate at VUMC Diabetes.
The top 3 key questions that the(sugar)science can answer:
1. How can I find scientists to collaborate with in Type 1 diabetes research?
2. Where can I learn about Type 1 diabetes trending topics?
3. Where can I find forums to discuss novel ideas with scientists or key opinion leaders and find opportunities for Type 1 diabetes research ?
Presenters:
Monica Westley, PhD, Founder, the(sugar)science
Tiffany Richardson
Neha Majety
Upcoming webinars schedule: https://dknet.org/about/webinar
dkNET Webinar: Discovering and Evaluating Antibodies, Cell Lines, Software To...
Abstract
dkNET’s Resource Reports (https://dknet.org/rin/rrids) enable researchers to discover research resources that would be useful for their research. The resource report integrated data set and analytics platform combines Research Resource Identifiers (RRIDs), text mining and data aggregation to help you identify key biomedical resources, track these resources, and compare their performance. Resource Reports offer a detailed overview of each resource along with citation metrics from the biomedical literature and even information about what resources have been used together. You'll gain insights about who is using particular resources and how the community views those resources, including usage in published protocols.
The dkNET Co-PI, Dr Jeffrey Grethe, will give you live demos during this webinar, including:
- How to find and select a research resource such as an antibody or a cell line
- How to find Research Resource Identifiers (RRIDs) and proper citation of your resources
- How to register resources to obtain RRIDs if the resources do not exist in the system
We hope this short webinar will provide an opportunity to use this tool to shape your research activities.
Presenter: Jeffrey Grethe, PhD, dkNET Co-Principal Investigator, University of California San Diego
Upcoming webinars schedule: https://dknet.org/about/webinar
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dkNET Office Hours: NIH Data Management and Sharing Mandate 05/03/2024
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dkNET Webinar "Texera: A Scalable Cloud Computing Platform for Sharing Data a...
dkNET Webinar "Texera: A Scalable Cloud Computing Platform for Sharing Data a...
dkNET Webinar: Unlocking the Power of FAIR Data Sharing with ImmPort 04/12/2024
dkNET Webinar: Unlocking the Power of FAIR Data Sharing with ImmPort 04/12/2024
dkNET Webinar "The Multi-Omic Response to Exercise Training Across Rat Tissue...
dkNET Webinar "The Multi-Omic Response to Exercise Training Across Rat Tissue...
dkNET Webinar: The Collaborative Microbial Metabolite Center – Democratizing ...
dkNET Webinar: The Collaborative Microbial Metabolite Center – Democratizing ...
dkNET Webinar: An Encyclopedia of the Adipose Tissue Secretome to Identify Me...
dkNET Webinar: An Encyclopedia of the Adipose Tissue Secretome to Identify Me...
dkNET Webinar: A Single Cell Atlas of Human and Mouse White Adipose Tissue 11...
dkNET Webinar: A Single Cell Atlas of Human and Mouse White Adipose Tissue 11...
dkNET Webinar "The National Sleep Research Resource (NSRR) - Opportunities fo...
dkNET Webinar "The National Sleep Research Resource (NSRR) - Opportunities fo...
dkNET Office Hours - "Are You Ready for 2023: New NIH Data Management and Sha...
dkNET Office Hours - "Are You Ready for 2023: New NIH Data Management and Sha...
dkNET Webinar: Discover the Latest from dkNET - Biomed Resource Watch 06/02/2023
dkNET Webinar: Discover the Latest from dkNET - Biomed Resource Watch 06/02/2023
dkNET Webinar: Leveraging Computational Strategies to Identify Type 1 Diabete...
dkNET Webinar: Leveraging Computational Strategies to Identify Type 1 Diabete...
dkNET Webinar: Estimating Relative Beta-Cell Function During Continuous Gluco...
dkNET Webinar: Estimating Relative Beta-Cell Function During Continuous Gluco...
dkNET Office Hours - "Are You Ready for 2023: New NIH Data Management and Sha...
dkNET Office Hours - "Are You Ready for 2023: New NIH Data Management and Sha...
dkNET Webinar: Postpartum Glucose Screening Among Homeless Women with Gestati...
dkNET Webinar: Postpartum Glucose Screening Among Homeless Women with Gestati...
dkNET Webinar: Choosing Sample Sizes for Multilevel and Longitudinal Studies ...
dkNET Webinar: Choosing Sample Sizes for Multilevel and Longitudinal Studies ...
dkNET Webinar: : FAIR Data Curation of Antibody/B-cell and T-cell Receptor Se...
dkNET Webinar: : FAIR Data Curation of Antibody/B-cell and T-cell Receptor Se...
dkNET Office Hours - "Are You Ready for 2023? New NIH Data Management and Sha...
dkNET Office Hours - "Are You Ready for 2023? New NIH Data Management and Sha...
dkNET Webinar "The Mission and Progress of the(sugar)science: Helping Scienti...
dkNET Webinar "The Mission and Progress of the(sugar)science: Helping Scienti...
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Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
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of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
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dkNET Webinar: The 4DN Data Portal - Data, Resources and Tools to Help Elucidate Nuclear Organization in Space and Time 05/10/2024
- 1. May 10, 2024
- 2. Outline ● Brief introduction to the 4D Nucleome ● Finding data of interest in the portal ● Metadata and visualizing data on the portal ● Downloading Data Files ● Standardized bioinformatics pipelines ● Exploring Data
- 4. Pongubala and Murre, 2021
- 5. Imaging B. Bintu lab, UCSD Sequencing
- 6. Sequencing Chromatin Conformation Capture in situ Hi-C Dilution Hi-C DNase Hi-C Micro-C, TCC HiChip PLAC-seq ChIA-PET/Drop Capture Hi-C GAM SPRITE MARGI 4C-seq DAMID-seq, TSA-seq, repli-seq, CUT&RUN, ChIP-seq, ATAC-seq, RNA-seq … Additional Genomics Assays
- 7. Imaging B. Bintu lab, UCSD Multiplexed FISH MERFISH, MINA, OligoFISSEQ, ORCA, seqFISH+, OligoSTORM DNA/RNA FISH, Imunofluorescence, SPT, OptoDroplet, Electron Tomography Additional Imaging Assays
- 9. Useful Links 4DN Data portal - https://data.4dnucleome.org/ 4DN Data Collections - https://data.4dnucleome.org/resources/data-collections FISH Omics Format RTD - https://fish-omics-format.readthedocs.io/en/latest/ HiGlass tutorial video - https://www.youtube.com/watch?v=LEDaOa3NZtM 4DN-DCIC github - https://github.com/4dn-dcic/ Hi-C pipeline repo - https://github.com/4dn-dcic/docker-4dn-hic 4DN JupyterHub - https://data.4dnucleome.org/tools/jupyterhub Data Portal Publication - https://pubmed.ncbi.nlm.nih.gov/35501320/ 4DN Organizational Hub - https://www.4dnucleome.org/
- 10. Data portal paper in Nature Communications Data portal https://data.4dnucleome .org/publications/ad9b5 72b-fe5e-4d9f-9ba3-7c6 2ff51ce9d/ Pubmed https://pubmed.ncbi.nlm .nih.gov/35501320/ Nature Communications https://www.nature.com/ articles/s41467-022-296 97-4
- 12. Please reach out to us with questions, comments or suggestions support@4dnucleome.org