Bioinformatics Training & Education Program

Partek Flow is your start-to-finish solution for analyzing high dimensional multi-omics sequencing data. It is a point-and-click software and is suitable for those who wish to avoid the steep learning curve associated with analyzing sequencing data through command line and/or code. At NIH, Partek Flow is hosted on the Biowulf high performance computing cluster (HPC). Researchers interact with the software through a web browser using a URL supplied by Biowulf once a Biowulf and Partek Flow account has been set up. This setup allows users to take advantage of the compute power offered by HPC while using a graphical user interface to construct a sequencing data analysis workflow. Partek Flow enables the creation of publication quality visualizations.

This online class is not hands-on. Biowulf and Partek Flow accounts are not required to attend. Participants will be presented with instructions for obtaining access to Partek Flow at the beginning of class.

This training session will address bulk RNA and ATAC sequencing analysis using Partek Flow. Participants will learn how to integrate these two assays to gain insights on the epigenetic regulation of gene expression. Topics covered include:

• Import of fastq files
• Alignment of fastq files to reference genome
• Detect peaks for ATAC sequencing data
• Quantification for RNA sequencing data
• Compare peak regions and gene expression
• Integrate RNA sequencing and ATAC sequencing results

Meeting link:
https://cbiit.webex.com/cbiit/j.php?MTID=mc425f72619b46e5defe804bf67a43b70
 
Meeting number:
2304 390 4110
Password:
JeRmMFa*823

Join by video system
Dial 23043904110@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: 2304 390 4110
Host PIN: 2784

This session focuses on R data types and data structures. In R, more advanced libraries (code) may require data to be a particular data type or data structure to perform a function or analysis. Understanding the foundational concepts of data types and data structures will enable emerging coders to avoid programmatic pitfalls and correct coding errors effectively. This class will demonstrate how to determine data types, and build data structures, and convert data types when needed for functions such as analysis and visualizations.

Zhiyong Lu, Ph.D. will present AI in Medicine: Improving Access to Literature Data for Knowledge Discovery at the monthly Data Sharing and Reuse Seminar. 

The explosion of biomedical big data and information in the past decade or so has created new opportunities for discoveries to improve the treatment and prevention of human diseases. But the large body of knowledge—mostly exists as free text in journal articles for humans to read—presents a grand new challenge: individual scientists around the world are increasingly finding themselves overwhelmed by the sheer volume of research literature and are struggling to keep up to date and to make sense of this wealth of textual information. Our research aims to break down this barrier and to empower scientists towards accelerated knowledge discovery. This seminar will discuss the development of large-scale, AI-based solutions for better understanding scientific text in the biomedical literature. Moreover, I will demonstrate their uses in some real-world applications such as improving PubMed searches (Fiorini et al., Nature Biotechnology 2018), supporting precision medicine with LitVar (Allot et al., Nature Genetics 2023), and taming COVID-19 pandemic paper tsunami in LitCovid (Chen et al., Nature 2000).

About the Speaker

Dr. Zhiyong Lu is a (tenured) Senior Investigator at the National Library of Medicine Intramural Research Program, leading research in biomedical text and image processing, information retrieval, and AI/machine learning. In his role as Deputy Director for Literature Search at National Center of Biotechnology Information (NCBI), Dr. Lu oversees the overall R&D efforts to improve literature search and information access in resources like PubMed and LitCovid, which are used by millions worldwide each day. Dr. Lu also serves as an Associate Editor of Bioinformatics, and Organizer of the BioCreative NLP challenge. Over the last 15 years, Dr. Lu has mentored over 60 trainees, many of whom have gone on to become independent faculty members/researchers at academic institutions in the US, Europe, and Asia. With over 300 peer-reviewed publications, Dr. Lu is a highly cited author, and a Fellow of the American College of Medical Informatics (ACMI) and the International Academy of Health Sciences Informatics (IAHSI).

This class introduces version control and how to use GitHub for project versioning. Participants will have a better understanding of version control, GitHub, and their advantages for managing projects.

Upon completion of this class participants should be able to recognize why version control is useful, discuss the difference between Git and GitHub, list the options for authenticating to GitHub, and list the options for creating a personal access token (PAT).

Labeling signal data is a very important step in creating AI-based signal processing solutions.  However, this step can be very time consuming and manual. In this beginner/intermediate one-hour session, the attendees will be introduced to signal labeling for use in AI applications and discuss how MATLAB can be used to speed up and simplify the process, from preprocessing to extracting information from signals. The session will cover different approaches for signal labeling, including using algorithms and automating with deep learning models. It will also discuss an iterative method of building deep learning models and reducing human effort in labeling. 

Labeling signal data is a very important step in creating AI-based signal processing solutions.  However, this step can be very time consuming and manual. In this beginner/intermediate one-hour session, the attendees will be introduced to signal labeling for use in AI applications and discuss how MATLAB can be used to speed up and simplify the process, from preprocessing to extracting information from signals. The session will cover different approaches for signal labeling, including using algorithms and automating with deep learning models. It will also discuss an iterative method of building deep learning models and reducing human effort in labeling. 

This October session of the BTEP Coding club will feature a tutorial on how to access data from GEO as well as how to submit data to GEO. 

Data Wrangling is another class in the NIH Library Introduction to R and RStudio Series. A basic understanding of using R and RStudio to manage data is expected. This one-hour class will introduce the R tidyverse package and how to use it to manipulate, analyze and export data. This class will explore options for using the tidyverse functions select, filter, pipes, mutate, head, is.na, and export. Learning these functions will enable users to have well formatted data frames for future clean and consistent analysis and visualizations.

Data Wrangling is another class in the NIH Library Introduction to R and RStudio Series. A basic understanding of using R and RStudio to manage data is expected. This one-hour class will build on the introduction provided in Data Wrangling Part 1 to further explore the R tidyverse package and how to use it to manipulate, analyze and export data. This class will explore options for using the tidyverse functions group_by, summarize, count, arrange, and export. Learning these functions will enable users to have well formatted data frames for future clean and consistent analysis and visualizations.

Python is a programming language used for data science, specifically: data analysis, statistical analysis, and visualization of results. This class will demonstrate integrated development and learning (IDE) platforms for learning Python, the fundamentals of Python coding, and why it is advantageous to develop these skills.  The session will feature the following IDEs: Google Colaboratory: Jupyter Notebook; and Anaconda’s: Spyder, Jupyter Notebook, and JupyterLab.  It will also provide an overview of programming constructs needed to learn Python. Finally, this class will demonstrate why these skills can boost productivity, rigor, and transparency in reporting research findings. 

Partek Flow is your start-to-finish solution for analyzing high dimensional multi-omics sequencing data. It is a point-and-click software and is suitable for those who wish to avoid the steep learning curve associated with analyzing sequencing data through command line and/or code. At NIH, Partek Flow is hosted on the Biowulf high performance computing cluster (HPC). Researchers interact with the software through a web browser using a URL supplied by Biowulf once a Biowulf and Partek Flow account has been set up. This setup allows users to take advantage of the compute power offered by HPC while using a graphical user interface to construct a sequencing data analysis workflow. Partek Flow enables the creation of publication quality visualizations.

This online class is not hands-on. Biowulf and Partek Flow accounts are not required to attend. Participants will be presented with instructions for obtaining access to Partek Flow at the beginning of class.

This training session will address RNA and ATAC sequencing analysis on the single cell level using Partek Flow. Participants will learn how to integrate these two assays to gain insights to the epigenetic regulation of gene expression at the single cell level. Topics covered include:

• Import single cell ATAC sequencing Cellranger output
• QA/QC
• Visualization
• Compare peak regions
• Motif detection
• Integration with single cell RNA-seq data

Meeting link:

https://cbiit.webex.com/cbiit/j.php?MTID=m53264dc1ee781615a0d289b1678feefc

Meeting number:
2300 641 2315
Password:
mcCG3P8kh@2

Join by video system
Dial 23006412315@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: 2300 641 2315
Host PIN: 2784