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Printable Handouts
Navigable Slide Index
- Introduction
- The human genome
- The ENCODE project
- Accessing and using ENCODE data
- Advantages of studying genome in a consortium
- ChIP-seq guidelines
- ENCODE assays
- ENCODE assay: RNA-seq
- ENCODE assay: ChIP-seq
- Evolution of the ChIP assay
- ENCODE assays: regulatory chromatin
- ChIP-seq of modified histones
- Open chromatin and TF binding sites
- ENCODE assays: 3D looping
- What has been learned so far
- An integrated encyclopedia of DNA elements
- Insights from ENCODE
- Density of information encoded in human genome
- Protein-coding transcripts
- Comparison of coding and non-coding RNAs
- Open chromatin
- Chromatin modifications that influence expression
- Classification of transcription factors
- Transcription factor binding preferences
- Chromosomal loops
- ENCODE elements are linked to genetic variation
- The ENCODE explorer
- How to access ENCODE data
- Visualizing ENCODE data
- ENCODE data on the UCSC genome browser
- Integrated ENCODE regulation tracks
- Snapshot of ENCODE tracks
- Expression and regulation ENCODE tracks
- How to access tables listing ENCODE data
- ENCODE experiment matrix
- ENCODE experiment summary
- Downloading ENCODE data
- ENCODE resources
- ENCODE user’s guide
- Using ENCODE data
- Understanding ChIP-seq results
- ENCODE standards for ChIP-seq
- Reproducibility and peak-cut off
- Comparing a ChIP-seq peak file to other datasets
- Tag density plots of ChIP-seq data
- Analysis of target genes
- Motif analysis: Factorbook
- Example from Factorbook
- ZBTB33 sequence logos
- Understanding GWAS results
- The genomic landscape around a risk SNP
- Tag SNPs and functional SNPs
- FunciSNP
- Example of FunciSNP workflow
- Identification of functional SNPs
- Summary
Topics Covered
- A description of the ENCODE Consortium
- Experimental assays used by Consortium members
- Overview of genome-wide methods to study gene expression, chromatin, and transcription factors
- A summary of insights from ENCODE experiments
- How to access and download ENCODE data
- How to use ENCODE data in your own research
Links
Series:
- Epigenetics, Chromatin, Transcription and Cancer
- Eukaryotic Gene Regulation
- Introduction to Human Genetics
- Using Bioinformatics in the Exploration of Genetic Diversity
Categories:
Talk Citation
Farnham, P. (2015, November 5). Accessing and using ENCODE data [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved November 7, 2024, from https://doi.org/10.69645/XKBZ1075.Export Citation (RIS)
Publication History
Financial Disclosures
- Prof. Peggy Farnham has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.
A selection of talks on Genetics & Epigenetics
Transcript
Please wait while the transcript is being prepared...
0:00
Hello. I am Peggy Farnham,
the William M Keck Professor of Biochemistry
and the Associate Dean of Graduate Studies
at the Keck School of Medicine at the University of Southern California.
I previously held professorships at the University of Wisconsin in Madison, Wisconsin,
and at the University of California in Davis, California,
where I was also the Associate Director of the UC Davis Genome Center.
I'm a member of the ENCODE Consortium.
I would like to tell you about how the data produced by this consortium
can aid in understanding gene regulation.
0:33
The Human Genome Project was launched in 1990
with the specific goal of identifying all the human genes.
This led to a push for sequencing the entire genome,
which began in 1996,
with a draft version of the human genome, published in 2001.
One of the first big surprises that came from this era of human genomics
had to do with how many genes we have.
Based on the fact that genomic sequencing of a worm which has
only 959 cells and 1 x 10^8 nucleotides of DNA,
they identified approximately 20,000 genes.
It was assumed that humans would have approximately 150,000 genes.
After all, we are much more complicated than a microscopic worm.
However, after the human genome was sequenced,
the results suggested that we might have at most 30,000 genes.
Refinement of the analyses have since reduced this number to 20,000.
In other words, we do have the same number of genes as a worm.
In fact, only five percent of our genome is covered by exons,
that is DNA segments that encode proteins.
This realization led to the question,
if 95 percent of the genome is not involved encoding for proteins,
then what does it do?
To address this question,