Intro to Comp Genomics

Lecture 7: Using large scale functional genomics datasets

Your Task

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Preparations:• Get your hand on the ChIP-seq

profiles of CTCF and PolII in hg chr17, bin-size = 50bp

• Cut the data into segments of 50,000 data points

Modeling:• Use EM to build a probabilistic model

for the peak signals and the background.

• Use heuristics for peak finding to initialize the EM

Analysis:• Test if your model for single peak

structure is as good as the model for two peak structures.

• Compute the distribution of peaks relative to transcription start sites

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Your Task

Preparations:• Get your hand on the ChIP-seq

profiles of CTCF and PolII in hg chr17, bin-size = 50bp

• Cut the data into segments of 50,000 data points

Modeling:• Use EM to build a probabilistic model

for the peak signals and the background.

• Use heuristics for peak finding to initialize the EM

Analysis:• Test if your model for single peak

structure is as good as the model for two peak structures.

• Compute the distribution of peaks relative to transcription start sites

Your TaskModeling

Implement HMM inference: forward-backward

Make sure your total probability is the same in the forward and the backward forms!

Implement the EM update rules

Run EM from multiple random points and record the likelihoods you derive

Implement smarter initialization: take the average values around all probes with value over a threshold.

Compute posterior peak probabilities: report all loci with P(Peak)>0.8

Your Task

Preparations:• Get your hand on the ChIP-seq

profiles of CTCF and PolII in hg chr17, bin-size = 50bp

• Cut the data into segments of 50,000 data points

Modeling:• Use EM to build a probabilistic model

for the peak signals and the background.

• Use heuristics for peak finding to initialize the EM

Analysis:• Test if your model for single peak

structure is as good as the model for two peak structures.

• Compute the distribution of peaks relative to transcription start sites

Your TaskAnalysis

Compare the two peak structures you get (from CTCF and PolII)

Retrain a model together on the two datasets

Compute the log-likelihood of the unified model and compare to the sum of likelihood for the two models

Optional: test if the difference is significant by:-sampling data from the unified model-training two models on the synthetic data and compute the likelihood delta as for real data

-Use a set of known TSSs to compute the distribution of peaks relative to genes

Functional genomics

• 10 years after the appearance of microarrays, thousands of experiments were performed on different cells and conditions

• One of the original promises of the technology is that it will for a vast body of data that can serve future modeling and analysis purposes

• Standards have been established, and it is mandatory to deposit data high throughput datasets when publishing papers describing it

• Unlike pubmed for literature or blast/blat for sequence, the functional genomics database is not usable using a single simple tool

• We will discuss and practice some strategies for utilizing this powerful resource

Platform

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NCBI - GEO

Data availability

GEO: 268,611 experiments (!!)5343 platforms

(Any species, condition, experiment)

Mandatory submission for all published papers

Also: EBI-Array express

Challenge: find what you need

Specific databases are curated and organized:

Species: e.g., SGD for yeast

Disease: e.g., Oncomine for cancer – 28,800 arrays organized around specific cancer types

Gene expression:

Different sets of genes or gene model! Still most of the data Conditions are critical

Comparative genomic hybridization (aCGH):

Important for disease with genomic aberrations

TF binding profiles

Old type: gene arrays Currently: Tiling array or ChIP-seq

Phenotype?Other specific assays?

Gene expression data is using different platforms (old cDNA, affy, new long oligo arrays)

Vastly different gene sets and gene models

RNA genes are now on most arrays

Understanding the experimental conditions for each array is a challenge

Avoiding replicates or using them smartly

Be careful from systematic pre-normalization of original data – subtracting the median/mean from a specific dataset introduce a strong bias for all the arrays in it when compared to other datasets!

Transcription factor interactions, histone modifications maps:

Genes bound by certain TFs

Genes (or regions) enriched for specific histone modifications

Hundreds of factors and modificationsDifferent experimental conditionsAbundant data for yeast,flies,mouse and human

Histone modifications

Knock-down/knock-out library phenotype

Library of mutants lacking each of the non-essential yeast genes is available (knockout)

Essential genes can be knocked down using a sepcialized promoter

Libraries can be automatiaclly screened for viability and/or growth rate in different conditions using robotics and 96/384 well plate formats

Libraries of RNAi construct allow similar screens for worms and flies.

Mammalian screens are becoming possible as well

Genetic interactions

Testing the phenotype of multi-gene knockout provide key insights into the genetic network

A gene may be essential fro growth under some condition, but become dispensable when another gene is knocked-down

A mutation can be lethal only in the presence of another knockout (synthetic lethality)

In yeast, systematic screens for synthetic lethality are practical for over 5 years.

Genetic interactions

Improved technology provide more quantitative measurement of the growth phenotype of double knock-down

Matching all pairs of a genes in a large subset of the genome is practical, and the resulted EMAP provide qunatitative estimate to the epistasis in the group (e.g., Schuldiner lab here at WIS)

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Protein interactions

Physcial interaction between proteins highlight post-translational regulatory networks and structural organization of key organelles

Data comes from several technologies:

most reliably techniques involving Mass spectrometry and isolation of protein complexes.

Indirect techniques involving transcriptional assays (yeast-two hybrid)

And more..

Data is partial and sometime difficult to interpret (what do we mean by interaction?)

A large body of literature is dealing with speculation on protein network – relevance to actual biology is questionable…

Array CGH/genetic aberrations

Data on deletion/insertion and copy number variation is generated by hybridization to arrays or more recently through sequencing

Data is critical for studies of cancer .

Databases also incule lists of genomic loci that are known to be instable in (specific types of) cancer.

Gene ontology

Hierarchical vocabulary (GO terms)

Unifying different research communities

Process-…Function-…Component-..

Annotations: association of term with gene in a specific species

Also associating all super-terms

GO-Slim is a flat version of the ontologies

Z-scores, T-test – the basics

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You want to test if the mean (RNA expression) of a gene set A is significantly different than that of a gene set B.

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In a common scenario, you have a small set of genes, and you screen a large set of conditions for interesting biases.

You need a quick way to quantify deviation of the mean

For a set of k genes, sampled from a standard normal distribution, how would the mean be distributed?

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You can quickly compute the sum of values over your set and generate a z-score

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Testing hypotheses on interaction graphs

Given your gene set and a set of gene-gene or protein-protein interactions.

How can you test if your set is enriched in intra- interactions?

Criterion for an additional gene that is strongly interaction with your set?

Node’s degree in the graph?

Overall network density?

Are complex tend to be split by your set or maybe tend to be contained in the set?

The iterative signature algorithm

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Iterate until convergence (Small changes in gene/condition sets)

Convergence is not guaranteed..

Try starting from your target gene set or from random sets.

Thresholds are critical

Variants: use a weighted average instead of plain average

Allow signs for conditions

Different statistics for thresholding (a-parametericKS/MW? Parameteric non-normal?

Can you think of a probabilistic version?

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Pros and cons?

Playing with the condition/gene means?

Convergence?

Multiple-testing

Testing for high mean of your gene set in 100,000 conditions in the database.

You expect to get one case with p<0.00001 !

Stringent correction: multiply the p-value by the number of tests

A rational alternative: control the false-discovery rate (FDR):

10 times “hits” than expected errors

In many cases, your tests are not really independent

For example, testing enrichment for functional annotations that are hierarchical

Another example are multiple gene expression conditions that are very similar (same tumor type)

You can estimate the empirical distribution of your statistics on random sets of the same size and use this as your p-value

This should be done with care: making sure your sampled sets are really similar in nature to your true sets and controlling for effects you want to factor out.

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Your Task

• Download the GNF human expression atlas from UCSC genome browser or GEO• Find 1-5 datasets on breast cancer in GEO• Combine IDs, merge the dataset• Download gene ontologies human associations. Extract gene set(s) related to

apoptosis and to cell cycle.• Use your previous analysis of chromosome 17 to generate the set of 40 genes for

which the 20k window containing their promoter had the lowest correlation to the overall k-mer spectrum

• Also generate a set of 40 chr17 genes with the highest G+C content on the 1kb upstream their promoter (you can use the Genome browser tools for that)

• Implement your version of the iterative signature algorithm (you are free to select the statistics you are using). You can implement the deterministic or probabilistic version.

• Starting from the above gene set, see if and how your algorithm is converging. Compute the intersection of the converged set with the original sets and report the conditions you found

• Change your algorithm parameters to get smaller or larger biclusters, plot the size of the resulted sets as a function of the parameter you are changing

Your Task