good gwas intro video https://www.coursera.org/lecture/disease-genes/introduction-to-genome-wide-association-studies-historical-overview-iFQ1J
https://github.com/ucsd-ccbb/network_bio_toolkit
What is network biology?
video I watched: Introduction to Biological Network Analysis I: Network Basics and Properties- https://www.youtube.com/watch?v=qTO_ob5d9EQ
Notes:
biology is evolving and we can start to talk about thing in a very different way. We can talk about how the whole system work together
changing technological environment lets us assess biological systems holistically, looking not just at individual elements but at how they work together
Basic - representation
Biological systems often represented by graphs
vertices may represent:
molecules, genes, non coding RNAs, proteins, drugs...
diseases
people
sometimes different things in graphs, this is just a way for us to
More on learning about the packages of network bio tool kit
the network bio tool kit is a toolbox for network analysis
build upon visJS2jupyter
provide 3 workflow for RNAseq data
only requiring an expression file/list of differentially expressed genes
upstream regulator analysis
localization
transcription factor enrichment
transcription factor activation state prediction methods
network propagation and clustering
network propagation
clustering
annotation
gene set enrichment analysis
ncludes easy-to-use data filtering methods, that help the user prep their data as input for GSEA’s enrichment calculation function
Box1: importing Heat (the network bio toolkit we installed earlier)
box2 importing different packages we will be using (details of each package not entirely understood
Loading AD genes
load in nominated target AD genes from site Agora (index using 'hgnc symbols')
these are genes that have been nominated might be good target for AD treatment or prevention
GWAS
Basics:
https://www.youtube.com/watch?v=KkRLNiRidOM
GWAS stands for genome wide association studies. We are trying to find the relationship between phenotype and genetics. (phenotype: outward appearance of something).
the genome type impacts the phenotype
Case control study.
you look at population of control and cases. see if they have an allele that has an impact on a disease.
can look at a region of a gene to see what is causing the phenotype
Continuous phenotype study
for a continuous trait (like height)
can look at height vs how many copies of an alternative allele someone has
Week 1 paper
polygenicity (many small genetic effect) and confounding bias - can yield inflated distribution in GWAS
trying to distinguish between true polygeneic signal and bias
uncovering disease-disease relationship through the incomplete interactome
disease module hypothesis says: cellular part associated with a disease seperate the same neighborhood of the human interactome - the map of biologically relvant molecular interactions.
limited knowledge of disease associated genes, can't map out modules associated with each genes
Training with Brin
RNA sequencing
https://www.youtube.com/watch?v=tlf6wYJrwKY
normal cells vs mutated cells. behave differently
we want to know what genetic mechanism is causing the difference --> look at the differences in gene expression
Figuring out a way to do this...
each cell has chromosomes
each chromosome has genes
some of the genes are active
active genes will send out mRNA (messenger RNA)
high throughput sequencing tells us which genes are active and how much they are transcribed
we can use RNA sequence to measure gene expression in normal cell and then compare it to mutated cells.
Analyzing differential expression in AD GWAS clusters
is the AD proximal network enriched for up/down regulation in AD brain vs control?
are clusters significant;y up or down regulated?