Format: Webinar

Federated Learning (FL) has emerged as a potential solution due to its capability in training models without sharing data. To enable effective FL in real applications, a robust communication framework is crucial.

Join Drs. Jayashree Kalpathy-Cramer and Ziyue Xu during the May NCI Imaging and Informatics Community Webinar as they cover the open-source NVIDIA Federated Learning Application Runtime (FLAIR) environment infrastructure for orchestrating an FL study. Also discussed will be Project MONAI, a medical imaging use case, recent research towards better performing FL pipelines, and the introduction of a current Medical Image Computing and Computer Assisted Intervention challenge on breast density FL.

The accuracy and robustness of AI algorithms rely heavily on the quantity, quality, and diversity of the training data set. For medical imaging applications, the challenge of constructing such a data set is particularly significant, mainly due to the privacy concerns in data sharing across multiple institutions.

This event is free and open to the public.

Speakers:

Jayashree Kalpathy-Cramer, Ph.D., M.G.H

Dr. Kalpathy-Cramer is an associate professor of radiology at Harvard Medical School, co-director of the QTIM Laboratory and the Center for Machine Learning at the Athinoula A. Martinos Center, and scientific director at the MGH & BWH Center for Clinical Data Science. Her research areas include machine learning (ML), informatics, image analysis, and statistical methods. In addition to developing novel ML algorithms, her lab is also actively engaged in the applications of these to clinical problems in radiology, oncology, and ophthalmology.

Ziyue Xu, Ph.D.

Dr. Xu is a senior scientist at Nvidia Corporation. His research interests lie in image analysis and ML with applications in biomedical and clinical imaging. Before joining Nvidia, Dr. Xu was an NIH staff scientist. He is an associate editor for the IEEE Transactions on Medical Imaging, Journal of Biomedical and Health Informatics, Computerized Medical Imaging and Graphics, and Computers in Biology and Medicine. He also serves as a program chair and committee member for multiple conferences (e.g., MICCAI, AAAI, etc.).

For our next CDSL webinar we will have a guest lecture by Dr. Julio Saez-Rodriguez from the Institute for Computational Biomedicine, Heidelberg.

Bio: Julio Saez-Rodriguez is Professor of Medical Bioinformatics and Data Analysis at the Faculty of Medicine of Heidelberg University, director of the Institute for Computational Biomedicine, group leader of the EMBL-Heidelberg University Molecular Medicine Partnership Unit and a co-director of the DREAM challenges. He holds a PhD (2007) in Chemical Engineering. He was a postdoctoral fellow at Harvard Medical School and M.I.T (2007- 2010), group leader at EMBL-EBI, Cambridge (2010-2015), and professor of Computational Biomedicine at RWTH Aachen (2015-2018). His research focuses on computational methods to understand and treat the deregulation of cellular networks in disease (www.saezlab.org).

Abstract: Multi-omics technologies, and in particular those with single-cell and spatial resolution, provide unique opportunities to study deregulation of intra- and inter-cellular processes in cancer and other diseases. In this talk I will present recent methods and applications from our group towards this aim, with a  focus is on computational approaches that combine data with biological knowledge.

Presenter:

Thomas Gonatopoulos-Pournatzis, Ph.D.

Stadtman Investigator

NIH Distinguished Scholar Head

Functional Transcriptomics Section

RNA Biology

Laboratory NCI-Frederick

Dr. Gonatopoulos-Pournatzis studies the regulatory pathways and functional roles of alternative splicing and other pre-mRNA processing events in mammalian cells. Towards this, he has developed several CRISPR-based screening platforms which are coupled to high-throughput phenotyping and enable systematic exploration of the regulatory and functional complexity of pre-mRNA processing. Dr. Gonatopoulos-Pournatzis’ team combines these functional genomics tools with molecular and biochemical approaches as well as animal models to identify alternative exons and other genetic segments that underlie phenotypes related to normal physiology and disease states. The long-term goal of his research is to contribute to the functional annotation of all exons in the human genome and to map the gene regulatory networks that underlie the complexity of the mammalian transcriptome.

Overview

Python is one of the preferred programming languages for scientists to solve a wide variety of biological problems. We find that many scientists who come to Software Carpentry workshops use Python and want to learn more about its capabilities.

If you are a novice and want to learn how to program in Python to help you in your work, please join our six-week series of two-hour workshops! Instructors will use live code and you will follow along on your own computer. You will gain a strong foundation in the fundamentals of Python along with best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

NOTE: This is a six-part course, held weekly for 6 weeks from 11 am – 1 pm, with a 20-minute lunch break. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to all six sessions!

Registration is required.  Register at this link.  Sign-in information for the Workshops will be provided once registered.

• Week 1, May 10, 2022, 11 a.m. – 1 p.m. ET: Introduction to Python & Colab, Running & Quitting, Variables & Assignment
  • NOTE: A one-hour help session will be offered on May 13, 2022, 11 a.m. – 12 p.m. ET: Getting Started with Google Colab
• Week 2, May 17, 2022, 11 a.m. – 1 p.m. ET: Data Types and Type Conversion, Built-in Functions & Help Libraries
• Week 3, May 24, 2022, 11 a.m. – 1 p.m. ET: Reading Tabular Data into DataFrames, Pandas DataFrames, Plotting 1
• Week 4, May 31, 2022, 11 a.m. – 1 p.m. ET: Plotting 2, Lists, For Loops
• Week 5, June 7, 2022, 11 a.m. – 1 p.m. ET: Conditionals, Looping Over Data Sets, Writing Functions
• Week 6, June 14, 2022, 11 a.m. – 1 p.m. ET: Variable Scope, Programming Style, Wrap-Up

Workshop Recordings and Materials:

• Recordings and Files: https://github.com/CBIIT/python-carpentry-workshop. This page contains last year’s material as well.
• Software Carpentry Lesson: Plotting and programming with Python
• Workshop Webpage at the NCI Data Science Learning Exchange: Introduction to Python A Series of Hands-on Software Carpentry Workshops (2022)

Before the workshop: We will use Google Colab for this workshop. Colab allows us to write and execute Python code through the browser. A Google Account is required for using Colab. If you do not have one, please create a Google Account before the workshop. For technical assistance, contact NCI technical support at https://service.cancer.gov/ncisp.

Presenters: Pinyi Lu, PhD, Data Scientist; Andrew Weisman, PhD, Bioinformatics Analyst; and George Zaki, PhD, Bioinformatics Manager, Frederick National Laboratory for Cancer Research (FNLCR)

Questions? Contact the NCI Data Science Learning Exchange

This is the first class in the NIH Library Introduction to R Series. This class provides a basic overview of the functionality of R programming language and RStudio. R is a programming language and open source environment for statistical computing and graphics. The R class series is a comprehensive collection of training sessions offered by the NIH Library Data Services and Bioinformatics programs that is designed to teach non-programmers how to write modular code and to introduce best practices for using R for data analysis and data visualization. Each class uses both evidence-based best practices for programming and practical hands-on lessons.

By the end of this class, students should be able to: list reasons for using R; describe the purpose of the RStudio Script, Console, Environment, and Plots panes; describe the various methods for finding help on R and RStudio; organize files and directories for a set of analyses as an R Project; define the following terms as they relate to R: object, assign, comment, call, function, and arguments; and assign values to objects in R.

Students are encouraged to install R and RStudio before the class so that they can follow along with the instructor. Please bring your laptop with R and RStudio installed.

Many new studies include RNA sequencing data. In this webinar we will go through the process of downloading and importing SRA data using the SRA toolkit, how to use an aligner to convert fastq files into BAM files and then how to import and normalize the BAM files in Omics Explorer. We will also show how you can download GEO soft files, TCGA mRNA data from GDAC and import 10X Genomics Cellranger data.

The Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Office of the Scientific Director invites you to the Division of Intramural Research (DIR) Tenure-Track Investigator Virtual Symposia Series.

Speakers:

Timothy J. Petros, Ph.D.

Investigator, Unit on Cellular and Molecular Neurodevelopment, NICHD, NIH

“A comprehensive spatial epigenome atlas of the embryonic mouse brain”

Maria K. Lehtinen, Ph.D.

Hannah C. Kinney, M.D. Chair in Pediatric Pathology Research, Boston Children’s Hospital

Associate Professor, Harvard Medical School

“Signals making a splash: Choroid plexus – cerebrospinal fluid contributions to brain development”

Flora M. Vaccarino, M.D.

Professor, Child Study Center and Department of Neuroscience, Yale School of Medicine

“Organoid modeling of gene regulatory events during forebrain development”

Bing Ren, Ph.D.

Member of the Ludwig Institute for Cancer Research

Director of the Center for Epigenomics, Professor of Cellular and Molecular Medicine, University of California, San Diego

“Single cell epigenome atlases of the brain”

Arnold Kriegstein M.D., Ph.D.

Professor of Neurobiology, University of California, San Francisco

“Development and evolution of the human brain revealed by single cell transcriptomics”

About the series:

The NICHD DIR Tenure-Track Investigator Virtual Symposia Series provides tenure-track investigators within NICHD the opportunity to organize a virtual mini-symposium to showcase their area of science to the NICHD DIR and larger NIH intramural community. Symposia are held monthly on the second Thursday of the month at 1 pm ET, and are open to all NIH faculty, trainees, and staff.

American Sign Language interpreting services will be available only upon request. Individuals who need interpreting services and/or other reasonable accommodations to participate in this event, should contact Amaressa Abiodun (amaressa.abiodun@nih.gov), 301-435-6994.  Requests should be made five days in advance of the event.

About the Seminar

Artificial intelligence and machine learning have the potential to greatly transform healthcare. Although these techniques have shown remarkable performance for many tasks including medical image analysis, we will share some of the challenges that we have faced in developing robust and trustworthy algorithms including a lack of repeatability, explainability, generalizability, and the potential for bias. Access to large, representative, diverse, and well curated datasets is vital to improving the performance of machine learning algorithms. Historically, concerns related to patient privacy, regulations, cost and logistical challenges have limited data-sharing. Approaches such as federated learning can improve the robustness of algorithms by providing a framework where the trained models have been exposed to multi-institutional datasets without the need for data-sharing. We will review examples of privacy preserving learning from multi-institutional datasets and discuss successes as well as directions for future research.

About the Speaker

Jayashree Kalpathy-Cramer is currently an Associate Professor of Radiology at Harvard Medical School, and a Co-Director of the QTIM lab and the Center for Machine Learning at the Martinos Center. She is the incoming chief of the new Division of Artificial Medical Intelligence in Ophthalmology at the University of Colorado (CU) School of Medicine. An electrical engineer by training, she worked in the semiconductor industry for several years. After returning to academia, she is now focused on the applications of machine learning and modeling in healthcare. Her research interests include medical image analysis, machine learning and artificial intelligence for applications in radiology, oncology, and ophthalmology. The work in her lab spans the spectrum from novel algorithm development to clinical deployment. She is passionate about the potential that these techniques have to improve access to healthcare in the US and worldwide. Dr. Kalpathy-Cramer has authored over 200 peer-reviewed publications and has written over a dozen book chapters.

About the Seminar Series

The seminar is open to the public and registration is required each month. Individuals who need interpreting services and/or other reasonable accommodations to participate in this event should contact Rachel Pisarski(link sends e-mail) at 301-670-4990. Requests should be made at least five days in advance of the event.

Presenters:  Dr. W. Lee Pang, Principal Developer Advocate, Amazon Web Services, HealthAI

Web Ex Details:
https://cbiit.webex.com/cbiit/j.php?MTID=mc7e3cc7cb71241b833b1be0aaaaffee5

Friday, May 13, 2022 3:00 pm | 1 hour | (UTC-04:00) Eastern Time (US & Canada)

Meeting number: 2300 677 6825
Password: HpX4MWfT*77

Join by video system
Dial 23006776825@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 toll number (US/Canada)
Access code: 230 067 76825

If you have any questions, please email: NCICWIGUserMail@mail.nih.gov

Metabolomic and multi-omic data are increasingly being collected in basic, preclinical, and clinical research studies. Interpretation of these data though remains challenging.  Common challenges include the difficulty in identifying metabolites and assigning unique identifiers, and the scarcity of resources that provide up-to-data comprehensive annotations and analysis tools on integrated genes/proteins and metabolites. To aid in interpreting these complex data, we developed RaMP-DB 2.0, a public resource that contains comprehensive biological, structural/chemical, disease, and ontology annotations for human metabolites and metabolic genes/proteins. The associated RaMP-DB 2.0 framework provides the ability to query those annotations and to perform pathway and chemical enrichment analysis on input multi-omic datasets. Since our first release, RaMP-DB 2.0 has been substantially upgraded and now includes an expanded breadth and depth of functional and chemical annotations, and a reproducible and semi-automated method for entity resolution of analytes across the different source databases pulled.  The usability of the RaMP-DB 2.0 has also been improved through updates of pathway and chemical enrichment analysis methods, and a completely revamped web interface and associated public API for programmatic access. RaMP-DB 2.0 currently pulls information from HMDB, KEGG (through HMDB), Reactome, WikiPathways, Lipid-MAPS, and ChEBI and includes 254,860 chemical structures, of which 43,338 are lipids, 15,389 genes, 53,745 pathways, 807,362 metabolic enzyme/metabolite reactions, and 699 functional ontologies (biofluid, health condition, etc.).  RaMP-DB 2.0 is available at https://rampdb.nih.gov/.

Speaker:

Ewy Mathé, Ph.D., Director of Informatics, Division of Preclinical Innovation, National Center for Advancing Translational Sciences, NIH

Python is one of the preferred programming languages for scientists to solve a wide variety of biological problems. We find that many scientists who come to Software Carpentry workshops use Python and want to learn more about its capabilities.

If you are a novice and want to learn how to program in Python to help you in your work, please join our six-week series of two-hour workshops! Instructors will use live code and you will follow along on your own computer. You will gain a strong foundation in the fundamentals of Python along with best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

NOTE: This is a six-part course, held weekly for 6 weeks from 11 am – 1 pm, with a 20-minute lunch break.

Workshop Recordings and Materials:

• Recordings and Files: https://github.com/CBIIT/python-carpentry-workshop. This page contains last year’s material as well.
• Software Carpentry Lesson: Plotting and programming with Python

Before the workshop: We will use Google Colab for this workshop. Colab allows us to write and execute Python code through the browser. A Google Account is required for using Colab. If you do not have one, please create a Google Account before the workshop. For technical assistance, contact NCI technical support at https://service.cancer.gov/ncisp.

Presenters: Pinyi Lu, PhD, Data Scientist; Andrew Weisman, PhD, Bioinformatics Analyst; and George Zaki, PhD, Bioinformatics Manager, Frederick National Laboratory for Cancer Research (FNLCR)

Questions? Contact the NCI Data Science Learning Exchange

Powerful and Intuitive Gene Expression Visualization Tools to Interpret Biological Signals – Bulk and Single Cell Data

The increasing use of genomic technologies, such as RNA-Seq and single cell RNA-Seq, to assess gene expression patterns has led to an unprecedented amount of data. For many, the size and complexity of these data sets make it challenging to see the biological signals. But not anymore.

The visualization tools in Partek^® Flow^® provide the flexibility needed to display gene expression results ready for publication. Our powerful, interactive plots also facilitate novel discovery and provide fast and accurate QAQC. Together with the easy-to-use point and click interface, Partek Flow allows you to answer more questions and move forward with your research.

In this webinar, you will learn how to visualize gene expression data using:

• Feature distribution plots
• Sample correlation plots
• Volcano plots
• Chromosome view
• Dot plots
• Violin plots
• Hierarchical clustering
• PCA
• t-SNE
• Customizable scatterplots

WebEx link: https://cbiit.webex.com/cbiit/j.php?MTID=m634db94c7c42f1eb6dcf1850c27bd2c6

During the May Accelerating Therapeutics for Opportunities in Medicine (ATOM) Webinar Series, discover how computing and machine learning can accelerate molecular optimization for cancer and infectious disease therapeutics.

The ATOM Consortium is a public-private partnership whose mission is to transform drug discovery by accelerating the development of more effective therapies for patients.

This webinar will demonstrate how ATOM strives to:

• speed up molecular optimization for applications ranging from cancer to infectious disease therapeutics with the help of computing and machine learning,
• establish multiparameter property optimization across efficacy, safety, pharmacokinetics, and developability, and,
• develop systems with the potential to guide and optimize experimental data collection and design validation.

Presenter:

Mr. Jim Brase is the deputy associate director for computing at Lawrence Livermore National Laboratory (LLNL). He leads LLNL research in the application of high-performance computing, large-scale data science, and simulation to a broad range of national security and science missions. Mr. Brase is also co-lead of the ATOM Consortium for computational acceleration of drug discovery and on the leadership team of the COVID-19 HPC Consortium. His research interests focus on the intersection of machine learning, simulation, and high-performance computing. He is currently leading efforts on large-scale computing for life science, biosecurity, and nuclear security applications.

All Biowulf users, and all those interested in using the systems, are invited to stop by our Zoom session to discuss problems and concerns, from scripting problems to node allocation, to strategies for a particular project, to anything that is affecting your use of the HPC systems. Users will be assigned to a breakout-session with a member of the HPC staff to discuss the problem 1-on-1.  We’ll try to address simpler issues on the spot and follow up on more complex questions after the Zoom-In.

At the Zoom-In: You will initially join the  main lobby and triage area.  There, you can briefly describe your issue, and then will be invited to join a 1-on-1 breakout room with a staff member.

Once you are finished with your focused consultation you can return to the main meeting room if you have additional questions or topics to discuss.  Please

– mute when not speaking

– refrain from screen sharing until asked to do so in the breakout room

– screen share as you would in a public space with the understanding that other NIH HPC staff may join and view what you are sharing (i.e. look over your shoulder)

– be prepared to wait your turn if staff are already helping other users

For inquiries please email to: staff@hpc.nih.gov

Data is everywhere! In cancer research, data has had an impact on everything from how cancer is diagnosed to how decisions are made about prognosis and treatment. To illustrate the ubiquity of data and its influence on cancer research, Dr. Jill Barnholtz-Sloan will present, “DataMatters—Leveraging Big Data for Impact on Cancer.”

In her lecture, she’ll:

•  focus on how big data has impacted cancer to date and its impact on future research,
•  use specific examples from her work researching brain tumors, and
•  discuss big data resources available through NCI with detailed descriptions.

This presentation is part of the 2022 Special Lecture Series hosted by Big Data Training for Cancer Research, a program of Purdue University’s Center for Cancer Research.

Presenter:

Jill Barnholtz-Sloan, Ph.D.

Dr. Barnholtz-Sloan is the associate director for the Informatics and Data Science Program at NCI CBIIT and a senior investigator for NCI’s Division of Cancer Epidemiology and Genetics.

Python is one of the preferred programming languages for scientists to solve a wide variety of biological problems. We find that many scientists who come to Software Carpentry workshops use Python and want to learn more about its capabilities.

If you are a novice and want to learn how to program in Python to help you in your work, please join our six-week series of two-hour workshops! Instructors will use live code and you will follow along on your own computer. You will gain a strong foundation in the fundamentals of Python along with best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

NOTE: This is a six-part course, held weekly for 6 weeks from 11 am – 1 pm, with a 20-minute lunch break. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to all six sessions

Workshop Recordings and Materials:

• Recordings and Files: https://github.com/CBIIT/python-carpentry-workshop. This page contains last year’s material as well.
• Software Carpentry Lesson: Plotting and programming with Python
• Workshop Webpage at the NCI Data Science Learning Exchange: Introduction to Python A Series of Hands-on Software Carpentry Workshops (2022)

Before the workshop: We will use Google Colab for this workshop. Colab allows us to write and execute Python code through the browser. A Google Account is required for using Colab. If you do not have one, please create a Google Account before the workshop. For technical assistance, contact NCI technical support at https://service.cancer.gov/ncisp.

Presenters: Pinyi Lu, PhD, Data Scientist; Andrew Weisman, PhD, Bioinformatics Analyst; and George Zaki, PhD, Bioinformatics Manager, Frederick National Laboratory for Cancer Research (FNLCR)

Questions? Contact the NCI Data Science Learning Exchange

This training will provide an introduction to RNA-seq data analysis followed by tutorials showing the use of popular RNA-seq analysis packages and preparing participants to independently run basic RNA-Seq analysis for expression profiling. The exercise will run on the Galaxy platform using Illumina paired-end RNA-seq data.

Participants will have a chance to: run quality control check on sequencing data; align the sequencing reads to a reference genome; generate alignment statistics and check mapping quality; measure abundance of transcripts; perform differential expression analysis; and visualize the output of RNA-seq analyses.

Python is one of the preferred programming languages for scientists to solve a wide variety of biological problems. We find that many scientists who come to Software Carpentry workshops use Python and want to learn more about its capabilities.

If you are a novice and want to learn how to program in Python to help you in your work, please join our six-week series of two-hour workshops! Instructors will use live code and you will follow along on your own computer. You will gain a strong foundation in the fundamentals of Python along with best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

NOTE: This is a six-part course, held weekly for 6 weeks from 11 am – 1 pm, with a 20-minute lunch break. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to all six sessions!

Workshop Recordings and Materials:

• Recordings and Files: https://github.com/CBIIT/python-carpentry-workshop. This page contains last year’s material as well.
• Software Carpentry Lesson: Plotting and programming with Python
• Workshop Webpage at the NCI Data Science Learning Exchange: Introduction to Python A Series of Hands-on Software Carpentry Workshops (2022)

Before the workshop: We will use Google Colab for this workshop. Colab allows us to write and execute Python code through the browser. A Google Account is required for using Colab. If you do not have one, please create a Google Account before the workshop. For technical assistance, contact NCI technical support at https://service.cancer.gov/ncisp.

Presenters: Pinyi Lu, PhD, Data Scientist; Andrew Weisman, PhD, Bioinformatics Analyst; and George Zaki, PhD, Bioinformatics Manager, Frederick National Laboratory for Cancer Research (FNLCR)

Questions? Contact the NCI Data Science Learning Exchange

Python is one of the preferred programming languages for scientists to solve a wide variety of biological problems. We find that many scientists who come to Software Carpentry workshops use Python and want to learn more about its capabilities.

If you are a novice and want to learn how to program in Python to help you in your work, please join our six-week series of two-hour workshops! Instructors will use live code and you will follow along on your own computer. You will gain a strong foundation in the fundamentals of Python along with best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

NOTE: This is a six-part course, held weekly for 6 weeks from 11 am – 1 pm, with a 20-minute lunch break. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to all six sessions!

Workshop Recordings and Materials:

• Recordings and Files: https://github.com/CBIIT/python-carpentry-workshop. This page contains last year’s material as well.
• Software Carpentry Lesson: Plotting and programming with Python
• Workshop Webpage at the NCI Data Science Learning Exchange: Introduction to Python A Series of Hands-on Software Carpentry Workshops (2022)

Before the workshop: We will use Google Colab for this workshop. Colab allows us to write and execute Python code through the browser. A Google Account is required for using Colab. If you do not have one, please create a Google Account before the workshop. For technical assistance, contact NCI technical support at https://service.cancer.gov/ncisp.

Presenters: Pinyi Lu, PhD, Data Scientist; Andrew Weisman, PhD, Bioinformatics Analyst; and George Zaki, PhD, Bioinformatics Manager, Frederick National Laboratory for Cancer Research (FNLCR)

Questions? Contact the NCI Data Science Learning Exchange

Python is one of the preferred programming languages for scientists to solve a wide variety of biological problems. We find that many scientists who come to Software Carpentry workshops use Python and want to learn more about its capabilities.

If you are a novice and want to learn how to program in Python to help you in your work, please join our six-week series of two-hour workshops! Instructors will use live code and you will follow along on your own computer. You will gain a strong foundation in the fundamentals of Python along with best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

NOTE: This is a six-part course, held weekly for 6 weeks from 11 am – 1 pm, with a 20-minute lunch break. When you register, you are registering for all 6 weeks. Please make sure you have time in your schedule to commit to all six sessions!

Workshop Recordings and Materials:

• Recordings and Files: https://github.com/CBIIT/python-carpentry-workshop. This page contains last year’s material as well.
• Software Carpentry Lesson: Plotting and programming with Python
• Workshop Webpage at the NCI Data Science Learning Exchange: Introduction to Python A Series of Hands-on Software Carpentry Workshops (2022)

Before the workshop: We will use Google Colab for this workshop. Colab allows us to write and execute Python code through the browser. A Google Account is required for using Colab. If you do not have one, please create a Google Account before the workshop. For technical assistance, contact NCI technical support at https://service.cancer.gov/ncisp.

Presenters: Pinyi Lu, PhD, Data Scientist; Andrew Weisman, PhD, Bioinformatics Analyst; and George Zaki, PhD, Bioinformatics Manager, Frederick National Laboratory for Cancer Research (FNLCR)

Questions? Contact the NCI Data Science Learning Exchange

Learn how to get started on using QIAGEN’s Ingenuity Pathway Analysis (IPA) to quickly identify biological relationships, mechanisms, pathways, functions and diseases most relevant to experimental datasets. IPA is cited in tens of thousands of peer-reviewed articles. The class will demonstrate how to explore IPA’s knowledge & discovery tools to relate the most recent literature findings to your research. The demo session will mainly focus on biological interpretation of expression data but will also cover multi-omics analysis including variant data and phosphoproteomics.

Getting Started: fundamentals of IPA; overview of key features; search & pathway building; advanced search; building & editing pathways; using Build & Overlay tools. Dataset Analysis: data upload & analysis; interpretation of gene, transcript, protein & metabolite data; pathway analysis & canonical pathways; downstream effects & interpreting the heat map; causal regulators and their directional effect on genes, functions and diseases across multiple time points or doses; interpreting networks; comparison & multiple observations analysis; miRNA and isoform analysis; BioProfiler: define relationships between molecule activity and diseases/processes in the literature. Q & A session: A QIAGEN Ingenuity Field Applications Scientist will be available to answer individual questions or discuss specific projects.

This webinar will discuss the various single cell RNA-Seq data formats and how to process them with Partek Flow.

You will learn about:

• Single cell data file formats .h5, .fastq, .tsv, .mtx, .bcl
• Trimming tags
• Alignment
• Deduplicating UMI
• Filtering barcodes (cells)
• Knee plots

During this seminar, Washington University in St. Louis’ Dr. Obi L. Griffith will present CIViC: an open access, open source, community-driven web resource for clinical interpretation of variants in cancer. The goal of this resource is to enable precision medicine by providing an educational forum for dissemination of knowledge and active discussion of the clinical significance of cancer genome alterations.

The Data Science Seminar Series presents talks from innovators in the cancer research and informatics communities both within and outside of NCI.

Presenter:

Obi L. Griffith, Ph.D.

Dr. Griffith is an associate professor of medicine (oncology) and the assistant director of the McDonnell Genome Institute at Washington University in St. Louis School of Medicine. He has his doctorate in medical genetics from the University of British Columbia in Vancouver, Canada. Dr. Griffith’s research interests include cancer informatics, clinical statistics, and breast cancer.

Sarah Teichmann, Ph.D., Fellow of the Academy of Medical Sciences (UK FMedSci), Fellow of the Royal Society (FRS), Wellcome Sanger Institute

Sarah Teichmann is co-founder and principal leader of the Human Cell Atlas (HCA) international consortium. The International Human Cell Atlas initiative aims to create comprehensive reference maps of all human cells to further understand health and disease.

The team’s research focuses on four complementary study areas:

• Transcriptional regulation and gene expression
• Single-cell genomics
• Immunology
• Protein complexes