This lesson provides an overview of the database of Genotypes and Phenotypes (dbGaP), which was developed to archive and distribute the data and results from studies that have investigated the interaction of genotype and phenotype in humans.
This lecture covers a wide range of aspects regarding neuroinformatics and data governance, describing both their historical developments and current trajectories. Particular tools, platforms, and standards to make your research more FAIR are also discussed.
Introduction of the Foundations of Machine Learning in Python course - Day 01.
High-Performance Computing and Analytics Lab, University of Bonn
EyeWire is a game to map the brain. Players are challenged to map branches of a neuron from one side of a cube to the other in a 3D puzzle. Players scroll through the cube and reconstruct neurons with the help of an artificial intelligence algorithm developed at Seung Lab in Princeton University. EyeWire gameplay advances neuroscience by helping researchers discover how neurons connect to process visual information.
This lecture focuses on how the immune system can target and attack the nervous system to produce autoimmune responses that may result in diseases such as multiple sclerosis, neuromyelitis, and lupus cerebritis manifested by motor, sensory, and cognitive impairments. Despite the fact that the brain is an immune-privileged site, autoreactive lymphocytes producing proinflammatory cytokines can cause active brain inflammation, leading to myelin and axonal loss.
In this lesson, you will learn about how genetics can contribute to our understanding of psychiatric phenotypes.
This lesson provides an overview of GeneWeaver, a web application for the integrated cross-species analysis of functional genomics data to find convergent evidence from heterogeneous sources.
This lesson provides a demonstration of GeneWeaver, a system for the integration and analysis of heterogeneous functional genomics data.
This lecture outlines GeneNetwork.org, a group of linked data sets and tools used to study complex networks of genes, molecules, and higher order gene function and phenotypes.
This tutorial shows how to use the UCSC genome browser to find a list of genes in a given genomic region.
This tutorial shows how to find all the single nucleotide polymorphisms (SNPs) upstream from genes using the UCSC Genome Browser.
This tutorial demonstrates how to find all the single nucleotide polymorphisms (SNPs) in a gene using the UCSC Genome Browser.
The Saved Sessions feature of the Browser has been around for quite some time, but many of our users have not made full use of it. This feature offers a great way to keep track of your thinking on a particular topic.
The Track Collection Builder is a new tool in the UCSC Genome Browser that provides a way to create grouped collections of sub-tracks with native tracks, custom tracks, or hub tracks of continuous value graphing data types.
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.