Bioinformatics Training & Education Program

The role of computational science in biomedical research has typically been downstream of experiments, where it plays important roles in signal processing, data integration, pattern detection, and hypothesis testing. But this is changing, and predictive models are now being used to generate and test hypotheses in silico. In this talk, Dr. Pollard will share examples from human genetics, where they have built deep learning models of 3D chromatin interactions that take only sequence as input and then used them to interpret disease variants. This strategy leads to causal hypotheses and enables them to prioritize variants with predicted functional effects. Experiments designed using model outputs are accelerating the rate of discoveries, shedding light on genetic mechanisms in cancer and developmental disorders. This prediction-first strategy exemplifies Dr. Pollard's vision for a more proactive, rather than reactive, role for computational science in biomedical research.

This one-hour online training will provide a high-level overview of Python coding concepts, as well as some of the integrative development environments (IDEs, such as Jupyter notebooks) used for Python coding. Python is a programming language used for data science, specifically: data analysis, statistical analysis, and visualization of results. The training will feature the following IDEs: Google Colaboratory: Jupyter Notebook; and Anaconda’s: Spyder, Jupyter Notebook, and JupyterLab. This overview training will demonstrate how these skills can boost productivity, rigor, and transparency in reporting research findings.  

By the end of the training, attendees will be able to: 

• Recognize four freely available IDEs for python coding
• Identify fundamental components of python code
• Understand how and why notebooks support rigor and transparency in analysis 

Attendees are not expected to have any prior knowledge of python coding or the IDEs to be successful in this training.  

If you choose to follow along with Google Colab or Jupyter Notebooks, these IDEs should be installed and ready to go. Code will be provided during the training for this option. 

This one-hour online training provides an introduction on how to sign up and access complimentary SAS training resources available to NIH and HHS employees. 

By the end of this training, attendees will be able to:  

• Enroll in recommended SAS 9.4 trainings and courses
• Navigate complimentary SAS tutorials, programming courses, and eLearning 

Attendees are not expected to have any prior knowledge of SAS to be successful in this training. 

Join us as poster presenters from the ODS Symposium showcase innovative work that highlights powerful resources, inspiring examples, and the far-reaching impact of data sharing and reuse.

Moderators: Ying Huang and Mousumi Ghosh

Partek Flow enables scientists to construct analysis workflows for multi-omics sequencing data including DNA, bulk and single cell RNA, spatial transcriptomics, ATAC and ChIP. It is a point-and-click software suitable for those who wish to avoid the steep learning curve involved with analyzing sequencing data through coding. In this class, taught by Partek scientist, participants will learn about conducting QA/QC, performing cell type classification, obtaining differential analysis results, performing pathway analysis, and creating visualizations for single cell RNA sequencing data. This session is a demonstration and not hands-on. Experience using or access to Partek Flow is not needed for participation. Attendance is restricted to NIH staff.

This talk will focus on analyses of the Patient Outcomes Repository for Treatment (PORT) which is a large registry of chronic pain treatment outcomes from patients seen in the pain clinics at the University Pittsburgh Medical Center (UPMC). Using methods such as propensity scoring, stratified modeling, and supervised machine learning, we can determine which treatments for chronic pain are or are not effective, the phenotypes most responsive to each treatment, and predict which treatments will be most effective in any new patient based on their phenotype (such as medications, injections, physical therapy, or mental health care).

This one and a half-hour online training covers the basic principles of FAIR (Findable, Accessible, Interoperable, Reusable) data and why it is important to make your data FAIR.  

 By the end of this training, attendees will be able to:  

• Define FAIR data   

• Explain what purpose FAIR data serves 

• Apply FAIR data principles to make data findable, accessible, interoperable, and reusable 

This is an introductory level training.