dkNET Webinar "Texera: A Scalable Cloud Computing Platform for Sharing Data and Workflow-Based Analyses" 04/26/2024

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Presenter: Chen Li, PhD. Professor, Department of Computer Science, University of California Irvine Abstract Many data analytics projects have collaborators with complementary backgrounds, including biologists, bioinformaticians, computer scientists, and AI/ML experts. Many of them have limited experience to code, set up a computing infrastructure, and use MLmodels. Existing tools and services, such as email attachments, GitHub, and Google Drive are inefficient for sharing data and analyses. In this talk, we present an open source system called Texera that provides a cloud computing platform for collaborators to share data and analyses as workflows. After seven years of development, the system has a rich set of powerful features, such as shared editing, shared execution, version control, commenting, debugging, user-defined functions in multiple languages (e.g., Python, R, Java), and support of state-of-the-art AI/ML techniques. Its backend parallel engine enables scalable computation on large data sets using computing clusters. We will show a demo of the system, and present our vision supported by a recent NIH award, dkNET(NIDDK Information Network, https://dknet.org), to serve the diabetes, endocrinology, and metabolic diseases research communities through the FAIR sharing of data and knowledge. Resource link: https://github.com/Texera/texera Upcoming webinars schedule: https://dknet.org/about/webinar

Sciences

Prof. Chen Li
Computer Science, UC Irvine
April 26, 2024
Texera: A Scalable Cloud Computing
Platform for Sharing Data and
Workflow-Based Analyses
1

2
Alice: Biologist (PI)
Sally: Bioinformatician Bob: Bioinformatician
Chen Li, UCI
Example: sequence analysis in biology

Differential
"gene" expression analysis
AI/ML analysis
Count Matrix
Cellranger
Quality Control
Data Integration
Clustering
Cluster Annotation
Trajectory analysis
Sequence
analysis
pipeline
FASTQ Sequences
Dimensionality
Reduction
Step 1
Step 2
Step 4
Step 5
Step 6
Step 7
Normalization Step 3

- Coding is hard!
- Version control of libraries
- Needs servers
- Slow on large data
- Not every lab can afford a
bioinformatician
4
Data preparation
Coding challenges
Data analytics
Visualization
Sally: Bioinformatician
Chen Li, UCI

Collaboration challenges
● Collaborators of different backgrounds:
○ Biologists
○ Bioinformaticians
○ Computer scientists
● Collaborators from different organizations
○ Same lab: senior students vs new students
○ Other labs
5
Chen Li, UCI

Limitations
● Only file management, no run-time environment
● Inefficient!
Collaboration: existing tools
6
Chen Li, UCI

● How to utilize state-of-the-art AI/ML technologies?
● Require advanced coding skills
● Not easily available
AI/ML opportunities
7
Chen Li, UCI

Cloud-computing services for sharing data and workflow-based analyses
Benefits:
- Cloud services (no installation, software patches)
- Version control
- Shared editing/execution
- Sharing data and workflows
- Parallel engine, scalable
- …
Our solution
8
Chen Li, UCI

Demo!
11
Alice: Biologist (PI)
Sally: Bioinformatician Bob: Bioinformatician
Chen Li, UCI

Figures on the entire dataset
12
Quality Control
Elbow plot
Clustered UMAP
Annotated UMAP
Chen Li, UCI

Texera Statistics
13
# of user accounts 332 # of projects 86
# of workflows 2,257 # of executions 31,000
# of workflow versions 357,000 # of publications 23
# of deployed servers 7 # of CPU cores in the largest deployment 400
# of files on GitHub 1,291 # of lines of code on GitHub 101,690
# of pull requests on GitHub 2,096 # of current PhD students 7
# of collaborating professors 17 # of involved undergraduates 80+
# of completed PhD theses 3 # of development years 7
Chen Li, UCI

Example: analyzing brain images, 256GB
14

Prof. Chen Li
Teaching non-STEM students AI/ML using Texera
15

Prof. Chen Li
High-school students using Texera
16

Mission: to serve the diabetes, endocrinology, and metabolic diseases
research communities through the FAIR sharing of data and knowledge.
New NIH award (dkNet)
17
Chen Li, UCI

Pilot project: inviting users
18
Chen Li, UCI

- Support a ChatGPT-like interface
- Provide more operators and workflows related to sequencing
- Make analysis parameters configurable
- Parallelize bottleneck steps
- Make more AI/ML techniques available
- Migrate existing programs to workflows
- Support public clouds (e.g., AWS, GCP)
- …
Open research problems
19
Chen Li, UCI

- Cloud-computing platform
- GUI-based workflows (no coding needed)
- Collaboration and sharing of data/analyses
- Parallel computing: for big data
- Supporting multiple languages: Python, R, Java, …
- Supporting AI/ML (training, inference, …)
Summary
20
Chen Li, UCI

Prof. Chen Li
Computer Science, UC Irvine
Texera: A Scalable Cloud Computing Platform for
Sharing Data and Workflow-Based Analyses
21
Acknowledgements: Yicong Huang, Sally Lee, Xinyuan Lin, Xiaozhen
Liu, Kun Woo (Chris) Park, Kevin Wu, and the Texera team

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dkNET Webinar: Leveraging Computational Strategies to Identify Type 1 Diabete...

dkNET

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 Webinar: Estimating Relative Beta-Cell Function During Continuous Gluco...

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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 Office Hours - "Are You Ready for 2023: New NIH Data Management and Sha...

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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: Postpartum Glucose Screening Among Homeless Women with Gestati...

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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: Choosing Sample Sizes for Multilevel and Longitudinal Studies ...

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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 Webinar: : FAIR Data Curation of Antibody/B-cell and T-cell Receptor Se...

dkNET

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 Office Hours - "Are You Ready for 2023? New NIH Data Management and Sha...

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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 "The Mission and Progress of the(sugar)science: Helping Scienti...

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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

dkNET Webinar: Discovering and Evaluating Antibodies, Cell Lines, Software To...

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