CIT, one of the 27 Institutes and Centers (ICs) at the National Institutes of Health (NIH), focuses on delivering high-quality IT services and systems to enable NIH staff to perform world-class research.

Each day, approximately 45,000 NIH staff in 400+ buildings and facilities across the Washington, DC and Maryland metropolitan areas, Arizona, Montana, and North Carolina rely on CIT‐managed IT services and infrastructure.

Our videos promote key programs and initiatives in support of the NIH mission. Learn more about CIT at cit.nih.gov.

Unlock the Power of Cloud Computing with NIH Cloud Lab
2:58
Unlock the Power of Cloud Computing with NIH Cloud Lab
Workflow Basics - Transcriptome Assembly Refinement and Applications (2 of 5)
6:42
Workflow Basics - Transcriptome Assembly Refinement and Applications (2 of 5)
Multimodal Genomic Analysis - Metagenomics Analysis of Biofilm Microbiome (1 of 1)
10:07
Multimodal Genomic Analysis - Metagenomics Analysis of Biofilm Microbiome (1 of 1)
Analysis of Biomedical Data for Biomarker Discovery (1 of 1)
4:06
Analysis of Biomedical Data for Biomarker Discovery (1 of 1)
Introduction to Pathway Analysis - Consensus Pathway Analysis in the Cloud (1 of 1)
4:33
Introduction to Pathway Analysis - Consensus Pathway Analysis in the Cloud (1 of 1)
Open-Source Interactive ATAC-Seq Analysis - ATAC-Seq and Single-Cell ATAC-Seq Analysis (1 of 1)
12:00
Open-Source Interactive ATAC-Seq Analysis - ATAC-Seq and Single-Cell ATAC-Seq Analysis (1 of 1)
Integration Module Code Overview - Integrating Multi-Omics Datasets (4 of 4)
4:42
Integration Module Code Overview - Integrating Multi-Omics Datasets (4 of 4)
DNA Methylation Module Code Overview - Integrating Multi-Omics Datasets (3 of 4)
2:45
DNA Methylation Module Code Overview - Integrating Multi-Omics Datasets (3 of 4)
Integration Module Overview - Integrating Multi-Omics Datasets (2 of 4)
12:16
Integration Module Overview - Integrating Multi-Omics Datasets (2 of 4)
Transcriptomic and Epigenetic Data Integration - Integrating Multi-Omics Datasets (1 of 4)
6:34
Transcriptomic and Epigenetic Data Integration - Integrating Multi-Omics Datasets (1 of 4)
Overview - RNA-Seq Differential Expression Analysis (1 of 2)
5:27
Overview - RNA-Seq Differential Expression Analysis (1 of 2)
Walkthrough of Sub-Module 1 - RNA-Seq Differential Expression Analysis (2 of 2)
32:59
Walkthrough of Sub-Module 1 - RNA-Seq Differential Expression Analysis (2 of 2)
Introduction to WGBS - DNA Methylation Sequencing Analysis with WGBS (1 of 1)
9:56
Introduction to WGBS - DNA Methylation Sequencing Analysis with WGBS (1 of 1)
Deep Learning for Biomedical Data Analysis - Biomedical Imaging Analysis using AI/ML (1 of 1)
9:17
Deep Learning for Biomedical Data Analysis - Biomedical Imaging Analysis using AI/ML (1 of 1)
Walkthrough of ML Solution - Introduction to Data Science for Biology (11 of 11)
10:29
Walkthrough of ML Solution - Introduction to Data Science for Biology (11 of 11)
Try Using a Tree Model to Predict Data - Introduction to Data Science for Biology (10 of 11)
13:12
Try Using a Tree Model to Predict Data - Introduction to Data Science for Biology (10 of 11)
See How the Decision Tree Model Performed - Introduction to Data Science for Biology (9 of 11)
1:40
See How the Decision Tree Model Performed - Introduction to Data Science for Biology (9 of 11)
Make Predictions with the Trained Decision Tree - Introduction to Data Science for Biology (8 of 11)
2:48
Make Predictions with the Trained Decision Tree - Introduction to Data Science for Biology (8 of 11)
Visualize the Trained Decision Tree - Introduction to Data Science for Biology (7 of 11)
2:42
Visualize the Trained Decision Tree - Introduction to Data Science for Biology (7 of 11)
Create a Decision Tree Object and Train it - Introduction to Data Science for Biology (6 of 11)
1:09
Create a Decision Tree Object and Train it - Introduction to Data Science for Biology (6 of 11)
Separate Dataset into Features and Labels - Introduction to Data Science for Biology (5 of 11)
1:30
Separate Dataset into Features and Labels - Introduction to Data Science for Biology (5 of 11)
Load Training Data and Ensure it Looks Correct - Introduction to Data Science for Biology (4 of 11)
2:30
Load Training Data and Ensure it Looks Correct - Introduction to Data Science for Biology (4 of 11)
Import Necessary Packages into Google Colab - Introduction to Data Science for Biology (3 of 11)
1:59
Import Necessary Packages into Google Colab - Introduction to Data Science for Biology (3 of 11)
Objectives of Exercise - Introduction to Data Science for Biology (2 of 11)
3:24
Objectives of Exercise - Introduction to Data Science for Biology (2 of 11)
Introduction - Introduction to Data Science for Biology (1 of 11)
13:36
Introduction - Introduction to Data Science for Biology (1 of 11)
Assessing a Transcriptome - Transcriptome Assembly Refinement and Applications (5 of 5)
7:50
Assessing a Transcriptome - Transcriptome Assembly Refinement and Applications (5 of 5)
Annotating a Transcriptome - Transcriptome Assembly Refinement and Applications (4 of 5)
4:25
Annotating a Transcriptome - Transcriptome Assembly Refinement and Applications (4 of 5)
Efficient Cloud Computing - Transcriptome Assembly Refinement and Applications (3 of 5)
3:21
Efficient Cloud Computing - Transcriptome Assembly Refinement and Applications (3 of 5)
Introduction - Transcriptome Assembly Refinement and Applications (1 of 5)
11:39
Introduction - Transcriptome Assembly Refinement and Applications (1 of 5)
Introduction to BASH for Biologists - Fundamentals of Bioinformatics (1 of 6)
3:23
Introduction to BASH for Biologists - Fundamentals of Bioinformatics (1 of 6)