This tutorial demonstrates the visibility controls on the Genome Browser, showing the effect on BED tracks, wiggle tracks, and Conservation tracks. It also discusses supertracks and composite tracks.
This tutorial describes the isPCR tool and demonstrates how to use it for predicting the size and location of PCR products and visualizing the genomic location on the genome. The tool operates on DNA templates for all organisms, and on human and mouse DNA/RNA. It also demonstrates how to use the Browser to obtain DNA sequences from the genome.
This tutorial describes the dbSNP resources in the UCSC Genome Browser, including display conventions and the subdivision of the data into several useful subset tracks, especially the Common SNPs. There is also a discussion about changes to the genome assemblies from one version to another, and of two ways to navigate between different assemblies of the human genome in the Browser.
This tutorial demonstrates the UCSC Genome Browser Data Integrator, a tool that allows combination and intersection of data from up to five primary tables. In the example, data are extracted showing SNPs, genes, and phenotypes from a genomic region.
This tutorial shows how to obtain coordinates of genes, then input those coordinates into the UCSC Genome Browser for display. The regions do not have to be continuous in the genome.
This tutorial demonstrates viewing alternate haplotypes with the UCSC Genome Browser.
The Genome Browser in the Cloud (GBiC) program is a convenient tool that automates the setup of a UCSC Genome Browser mirror on a cloud instance or a dedicated physical server.
This tutorial demonstrates how to get the coordinates and sequences of exons using the UCSC Genome Browser.
This tutorial will demonstrate how to find the tables in the UCSC database that are associated with the data tracks in the Genome Browser graphical viewer.
Fibr is an app for quality control of diffusion MRI images from the Healthy Brain Network, a landmark mental health study that is collecting MRI images and other assessment data from 10,000 New York City area children. The purpose of the app is to train a computer algorithm to analyze the Healthy Brain Network dataset. By playing fibr, you are helping to teach the computer which images have sufficiently good quality and which images do not.
Neuronify is an educational tool meant to create intuition for how neurons and neural networks behave. You can use it to combine neurons with different connections, just like the ones we have in our brain, and explore how changes on single cells lead to behavioral changes in important networks. Neuronify is based on an integrate-and-fire model of neurons. This is one of the simplest models of neurons that exist. It focuses on the spike timing of a neuron and ignores the details of the action potential dynamics. These neurons are modeled as simple RC circuits. When the membrane potential is above a certain threshold, a spike is generated and the voltage is reset to its resting potential. This spike then signals other neurons through its synapses.
Neuronify aims to provide a low entry point to simulation-based neuroscience.
This module covers many of the types of non-invasive neurotech and neuroimaging devices including electroencephalography (EEG), electromyography (EMG), electroneurography (ENG), magnetoencephalography (MEG), and more.
This video introduces the key principles for data organization and explains how you could make your data FAIR for data sharing on EBRAINS.