Bioinformatics Training & Education Program

Dear FNL colleagues,
 
It’s time once again for our last in 2024 Biotech Connector event!!
 
The Frederick National Laboratory for Cancer Research together with the Frederick County Chamber of Commerce organizes the quarterly Biotech Connector Speaker Series. This event promotes and supports the Frederick County and surrounding areas’ biotech and bioscience community and provides an inside look at local advances in breakthrough technologies in life sciences to improve human health.
 
Please join us for our fourth quarter Biotech Connector Speaker Series on November 21 at 8am in the main auditorium at the ATRF or connect virtually via Webex. 

Don’t miss this exciting event from 8-9 a.m. focused on advances in next-generation sequencing! You will also have an opportunity to engage in the conversation with our speakers after the presentations and connect with your colleagues.

The event is free. Coffee and pastries will be provided for all in-person participants! 
 
For any questions regarding the event, please contact Lyuba Khavrutskii at the Frederick National Laboratory Partnership Development Office at lyuba.khavrutskii@nih.gov.

The Research to Accelerate Cures and Equity (RACE) for Children Act of 2017 requires companies developing targeted cancer drugs for adults to evaluate those drugs for applicability to pediatric cancer. The NCI-funded Pediatric Preclinical In Vivo Testing (PIVOT) consortium collaborates with industry partners to perform rigorous preclinical testing of novel targeted agents using in vivo models of common pediatric cancers.  As pediatric cancer is rare, assembling sufficient numbers of patients for clinical trials is challenging. It highlights the importance of effective preclinical testing for identifying promising agents and agents with low potential for improving treatment options for children with cancer.

The proliferation of medical data and the advancements of large language models (LLMs) promise to revolutionize healthcare; however, studying and improving health equity for all patients remains a significant challenge. In this talk, I will present recent work on two critical aspects of this evolving landscape. First, I will examine the unexpected consequences of multi-source data scaling. Counter to intuition, adding training data can sometimes reduce overall accuracy, produce uncertain fairness outcomes, and diminish worst-subgroup performance. These findings underscore the complexity of working with disparate data sources in healthcare AI. Next, I will showcase applications of LLMs to improve health equity. Through participatory design with healthcare workers and patients, we developed guiding principles for LLM use in maternal health. Additionally, we demonstrate how LLMs can help understand health disparities in treatment protocols by extracting rationales for treatment protocols using clinical notes. The talk concludes by emphasizing vigilance and ethical considerations as we advance towards more data-driven and AI-assisted healthcare.

Individuals with disabilities who need accommodation to participate in this meeting should contact: rebecca.krupenevich@nih.gov

Dr. Daniel Orringer received his M.D. from The Ohio State University, completed his residency in neurological surgery at the University of Michigan Health System, and fellowship training in neuro-oncology at the Massachusetts General-Brigham and Women's Hospital of Harvard Medical School. He is board certified in neurosurgery.

Dr. Orringer runs a highly interdisciplinary research group focused on three initiatives:

•    improving surgical outcomes for people with brain tumors,
•    using artificial intelligence to support surgical decision-making and brain tumor diagnosis, and
•    conducting clinical and translational trials of novel therapeutics.

Dr. Orringer specializes in brain mapping operations, in which he has extensive experience. Additionally, he has developed a novel laser-based technique—stimulated Raman histology—to detect tumors that were previously undetectable.

Dr. Orringer has received many awards, including the Andrew Parsa Young Investigator Basic/Translational Research Award from the Society for Neuro-Oncology, the Congress of Neurological Surgeons’ Innovator of the Year Award, and the Congress of Neurological Surgeons’ Rosenblum–Mahaley Clinical Research Award.

Dr. Orringer has co-authored over 80 peer-reviewed publications in journals such as Cancer Research, Clinical Cancer Research, Nature Biomedical Engineering, Neuro-Oncology, Molecular Cancer Research, Nature Medicine and Neurosurgery.

For more information, please contact Aniruddha Ganguly, Ph.D.

Meeting Number (access code): 2314 588 1524 
Meeting Password: dFEVsuq*229

In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering classes geared to cover general concepts behind statistics and epidemiology. This four-part lecture series will help participants better understand statistical and epidemiological features in biomedical research, interpret results and findings, design and prepare studies, and understand/critically review the results in published literature. 

Part 3 will describe the basic concepts for using common statistical tests such as Chi-square, paired and two-sample t-tests, ANOVA, correlations, simple and multiple regression, logistic regression, and survival analysis. During the class, time will be devoted to questions from attendees and references will be provided for in-depth self-study.

The first part of the class will be 10:00 a.m. to 12:00 p.m. followed by a break from 12:00-1:00 p.m. The class resumes at 1:00 p.m. and concludes at 4:30 p.m.

Although you may attend any part of this series by itself, attending all four parts will provide a more comprehensive review of important statistical and epidemiological considerations that build on each other.

You must register separately for each part of this class series.

This one and a half hour online training  will provide a demonstration of how to build a Bulk RNA-Seq data analysis pipeline using a fastq file. Partek Flow is a web-based application for the analysis of next generation sequencing (NGS) including RNA, small RNA, and DNA sequencing.  With an easy-to-use graphical interface and the ability to build custom analysis pipelines, Partek Flow enables users to carry out routine NGS data analysis using dozens of popular algorithms without writing codes or running command lines tools.

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

• Describe how to access Partek Flow from the NIH Library
• Discuss the Quality Control (QC) and Quality Assurance (QA) tools
• Identify pre- and post-alignment tools
• Describe options for quantification and normalization
• Perform pathway analysis and visualization

Registrants will receive an email with information and instructions to verify Partek Flow before the class. If you register the day before the class, you may not have time to secure access to Partek Flow. If you do not have the software installed, this training will be demo only.

This one and a half hour online training will provide a demonstration of how to identify cell types based on statistics, visualization, and canonical markers. One Peripheral blood mononuclear cells (PBMCs) sample will be used to illustrate a basic Single Cell RNA-Seq analysis pipeline. Partek Flow is a web-based application for the analysis of next generation sequencing (NGS) including RNA, small RNA, and DNA sequencing. With an easy-to-use graphical interface and the ability to build custom analysis pipelines, Partek Flow enables users to carry out routine NGS data analysis using dozens of popular algorithms without writing codes or running command lines tools.  Attendees will need to have taken the Partek Flow Basic Components class before registering or be comfortable with Partek Flow.

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

• Describe how to access Partek Flow from the NIH Library
• Discuss the Quality Control (QC) and Quality Assurance (QA) tools
• Normalize data
• Descriptions options for cell type classification
• Perform differential analysis

Access to the NIH Library Partek Flow requires two accounts:

Registrants will receive an email with information and instructions to verify Partek Flow before the class. If you register the day before the class, you may not have time to secure access to Partek Flow. If you do not have the software installed, this training will be demo only.

In partnership with the NIH Clinical Center's Biostatistics and Clinical Epidemiology Service (BCES), the NIH Library is offering classes geared to cover general concepts behind statistics and epidemiology. This four-part lecture series will help participants better understand statistical and epidemiological features in biomedical research, interpret results and findings, design and prepare studies, and understand/critically review the results in published literature. 

Part 4 will provide a brief review of the principles of epidemiology, outbreak investigations, implications in public health, key concepts and terms, and commonly used statistics in epidemiology (e.g., morbidity and mortality rates; incidence and prevalence; relative risk; odds ratio; sensitivity and specificity). The instructor will present a set of exercises to work through during the lesson (a calculator will be needed). 

Although you may attend any part of this series by itself, attending all four parts will provide a more comprehensive review of important statistical and epidemiological considerations that build on each other.

You must register separately for each part of this class series.

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 will cover how to sign up and access complimentary SAS training resources available to NIH and HHS employees. The instructor will demonstrate how to enroll in recommended SAS 9.4 trainings. 

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

• Enroll in recommended SAS 9.4 trainings 

• Navigate complimentary tutorials, programming courses, and eLearning for SAS 

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

This class will introduce bulk RNA sequencing analysis using Qiagen software. Participants will learn how to process FASTQ files and obtain differential expression using CLC Genomics Workbench as well as extract biological insight using Ingenuity Pathway Analysis.

Meeting link:
https://cbiit.webex.com/cbiit/j.php?MTID=mc5435dc253dc80f408270b9432a56bf6
Meeting number:
2307 183 2951
Password:
9u6pBmhCX?9

Join by video system
Dial 23071832951@cbiit.webex.com
You can also dial 173.243.2.68 and enter your meeting number.

Join by phone
1-650-479-3207 Call-in number (US/Canada)
Access code: 2307 183 2951