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A Field Guide to Epigenomics

Benjamin Rodriguez, PhD Wei Li and Peggy Goodell Labs Baylor College of Medicine

Molecular Biology Refresher Course with Bioinforma�cs Sept 9th 2016

Course Materials: h�p://dldcc-‐web.brc.bcm.edu/lilab/benji/MBRB_2016/index.html Most up to date slides Supplementary materials

Browsers: h�p://genome.ucsc.edu/ h�p://epigenomegateway.wustl.edu/

Web-‐based analysis: h�p://bejerano.stanford.edu/great/public/html/ h�p://david.abcc.ncifcrf.gov

So�ware, Sites, Materials

Outline

  DNA packaging and accessibility   DNA methyla�on   Histone modifica�ons   Epigene�c inheritance in development and disease   Aberrant epigene�c changes in cancer

DNA is Packaged in Chroma�n

nucleosome histone DNA

chromatin

Chroma�n consists of nucleosomes, DNA wrapped around histone proteins

•  Chroma�n organizes genes to be accessible for transcrip�on, replica�on, and repair

  Regula�on of genes involved in differen�a�on, cell cycle, and cell survival

EPIGENETICS

Normal epigene�c mechanisms

Roles in Normal Development and Cancer

Differen�ated cells

Progenitor cell

  Regula�on of genes involved in differen�a�on, cell cycle, and cell survival

  Through epigene�c silencing of certain genes, affected cells may acquire new phenotypes which promote tumorigenesis

EPIGENETICS

Malignant progenitor cell Tumor

Normal epigene�c mechanisms

Deregulated epigene�c mechanisms

Roles in Normal Development and Cancer

Differen�ated cells

Progenitor cell

Unraveling the package

Graphic from NIH RoadMap Epigenomics Site Epigene�cs as Lobbying

-‐  promoters -‐  enhancers -‐ silencers -‐  insulators -‐  etc.

DNaseI

Genes, regulatory DNA, and epigenetic features

~100,000 – 250,000 DHSs per cell type (0.5-1.5% of genome)

genome.ucsc.edu www.epigenomebrowser.org

DNaseI Hypersensi�ve site (DHS)

Promoters

Enhancers

DNaseI hypersensitive sites mark regulatory DNA

Epigene�c Mechanisms: DNA Methyla�on

1 3 2 4

CG CG CG CG CG MCG MCG

Normal

C: cytosine mC: methylcytosine

Epigene�c Mechanisms: DNA Methyla�on

1 3 2 4


Normal


CpG island

DNA Methyla�on and Gene Silencing

1 3 2 4

1 2 3 4

X


Normal

Cancer

CG CG CG MCG MCG MCG MCG


CpG island

Con�nuum of Methyla�on and Gene Expression

R2 = 0.7817 P < 0.0005

Some genes (e.g. HOXB13 in breast cancer) show strong correla�on of promoter methyla�on with expression

Rodriguez et al. Carcinogenesis, 29(7), 1459-‐1465.

DNA Methyla�on and Regula�on

  Cytosine methyla�on blocks DNA-‐binding proteins’ access to regulatory sites and creates binding sites for repressive proteins   Methyla�on o�en follows decrease in site use

Thurman et tal. Nature, 489(7414), 75-‐82.

Methyla�on gets more complicated!

  Many highly expressed genes have CpG methyla�on on their exons   Genomic imprin�ng (parent of origin DNA methyla�on)   Non-‐CpG cytosine bases are o�en methylated in embryonic stem cells   Hydroxymethylcytosine (5hmC) and demethyla�on

Methyla�on, Retroviruses and Repeats

  Bacteria use DNA methyla�on to limit invasive DNA from viruses   A large frac�on of the human genome consists of carcasses of retro-‐viruses and transposons   Almost all DNA repeats are heavily methylated   If they lose methyla�on they are more likely to be expressed

DNA Methyla�on and Development

  Two major waves of germline demethyla�on   Increasing methyla�on at various �mes during fetal development restrict func�onality – This is why cloning is difficult Lee et al. Cell stem cell, 14(6), 710-‐719

(2014)

DNA Methyla�on at Single Base Resolu�on

  Bisulfite conversion destroys ~ 98% of star�ng material   Conversion efficiency, clonal fragment amplifica�on   Unbalanced genome? Try BSMAP!   Biological interpreta�on

HOXB13 hypermethyla�on in breast cancer cells

From Rodriguez et al Carcinogenesis 2008

Bisulfite sequencing

(Sanger, clone-‐based, very laborious)

Let us switch gears

DNA methyla�on sequencing methodologies

  Enriched for hematopoietic TF binding sites and human leukemia gene expression signatures   Canyon edges eroded in the absence of Dnmt3a

Jeong et al. Nature Gene�cs 46, 17–23 (2014)

Scale 10 kb

Gata2

WT

Methylation Ratio

100 _

0 _

KO3a

Methylation Ratio

100 _

0 _

Large conserved domains of low DNA methyla�on maintained by Dnmt3a

WT

3aKO

2"

4"

6"

8"

10"

12"

14"

AML AML B-ALL ch B-ALL CML Pro-B ALL T-ALL

- log

p-v

alue

Expressed Canyon

Simulated Canyon

Expressed Random

Unexpressed Canyon

Unmethylated Random

  Top 10% of genes over-expressed in disease vs. normal bone marrow (Oncomine database)

  Enrichment in expressed Canyon genes compared to four control gene sets

Canyon genes are enriched in human leukemia gene expression signatures

Jeong et al. Nature Gene�cs (2014)

Canyons conserved in humans and dysregulated in leukemia pa�ents

Rau et al. In Prepara�on

DNA Methyla�on and Inheritance   Humans and mice show epigene�c inheritance apparently mediated by DNA methyla�on   Impact of nutri�onal and environmental influence on the fetal epigenome

  Specific methyla�on changes o�en correlated with clinical features   Poten�al for early detec�on, diagnosis, prognosis, therapeu�c stra�fica�on and post-‐therapeu�c monitoring

DNA methyla�on as biomarker of disease

HOXB13 hypermethyla�on associates with poor disease free survival in ERα-‐posi�ve pa�ents

Rodriguez et al. Carcinogenesis, 29(7), 1459-‐1465.

Epigene�c Mechanisms: Post-‐Transla�onal Modifica�on to Histones

Histone Acetylation

Histone Methylation

Ac Me

•  Epigene�c modifica�ons of Histones include Histone Acetyla�on and Methyla�on

Histone Modifica�ons

  Different modifica�ons at different loca�ons by different enzymes   Poten�al temporal and spa�al specificity

Histone Modifica�ons

  Gene body mark: H3K36me3, H3K79me2   Ac�ve promoter (TSS) mark: H3K4me3   Ac�ve enhancer (TF binding) mark: H3K4me1, H3K27ac   Both enhancers and promoters: H3K4me2, H3/H4ac, H2AZ   Repressive promoter mark: H3K27me3   Repressive mark for DNA methyla�on: H3K9me3

HMT

HMT

  Coordinated ac�vi�es of chroma�n modifying enzymes lead to condensa�on of chroma�n and inhibi�on of gene expression

HDAC

HDAC

Ac

Ac

Ac

Me Me Me

Me

Me

Me

Me

Me

Me

Me

Gene expression

Gene expression

DNMT

Epigene�c Modifica�ons to Histones and DNA Can Cooperate to Silence Gene Expression

Broad peaks for trimethyla�on of histone H3 at lysine 4 (H3K4me3; wider than 4-‐kb)

  first epigene�c signature for tumor suppressors in normal cell types   widespread shortening of broad H3K4me3 in cancers is associated with repression of tumor suppressors

Scale

chr19:

_14563

10 kb mm9

32,825,000 32,830,000 32,835,000 32,840,000RefSeq Genes

User Supplied Track

m24_H3K4me3

m24_RatioDepth

Pten

100 _

0 _

100 _

-10 _

Chen, Kaifu, Zhong Chen, Dayong Wu, Lili Zhang, Xueqiu Lin, Jianzhong Su, Benjamin Rodriguez et al. Nature Gene�cs (24 Aug 2015)

Peak width (kb)

Peak

heig

ht

0 10 20

0200

400

Width

Heig

ht

Broad

Sharp

Excep�onally Broad H3K4me3 signature

Defini�on of H3K4me3 peak height and width

H3K4me3 peak height plo�ed against peak width


2kb 3kb 4kbH3K4me3 width

4167 p

rom

ote

rs

A

B

C

D

E

F

G

H

I

105 samples

A B C D E F G H IGene groups

-log1

0 en

rich

P SuppressorsOncogenesHouse keeping

0

5

10

15

20

B D FA C E G IH

Broad H3K4me3

  Peak widths for 4,167 promoters across ENCODE normal samples

  Segmented into nine groups on basis of H3K4me3 peak width conserva�on level

  Enrichment levels of promoter groups for housekeeping, oncogenes, and tumor suppressors

  Tumor suppressors enriched only in top two groups with most conserved H3K4me3 peaks

Lung normal #1 Lung normal #2 Lung tumor #1 Lung tumor #2 Lung normal

H3K4me3 Expression (TCGA)

Expression

(RNA-Seq)

6 25 90

H3K4me3

1 4 30

Shortening

in tumor

209

Lengthening

in tumor

108

Stable

248

Random

200

Lung tumor

-5kb TSS 5kb

Distance to TSS

Shortening of broad H3K4me3 peaks in lung tumors


  Prominent example of cancer driven by muta�ons involving an epigene�c regulator   MLL-‐AF9 promotes enhanced H3K79me2 at fusion target genes   H3K79me2 specifically abnormal compared to other histone modifica�ons   Loss of Dot1l selec�vely decreases leukemia-‐associated gene expression   Dot1l required for MLL-‐rearranged leukemia cell growth in vitro and in vivo

H3K79 methyla�on and MLL rearranged leukemia

Bernt et al (2011) Cancer cell, 20(1), 66-‐78.

Abnormal H3K79me2 at MLL-‐AF9 targets

Bernt et al (2011) Cancer cell, 20(1), 66-‐78.

We will learn how to work with chroma�n signal data

  Epigene�c inheritance in development and disease

  Aberrant epigene�c changes in cancer   DNA packaging and accessibility   DNA methyla�on

–  Nutri�on and environment -‐> fetal development –  Disease biomarkers (Breast cancer prognosis)

  Histone modifica�ons –  Broad regions of H3K4me3 –  Aberrant H3K79me2 in MLL

Lecture Summary

Any ques�ons?

On to the Laboratory!

Miscellaneous Details

  ChIP Sequencing   UCSC Genome Browser   File formats

– BED – BEDGRAPH – bigWig

  Exercise 1: Epigene�c profiling of HSC and LSC –  Data Visualiza�on, Opera�ng on Genomic Intervals –  Crea�ve problem solving for MLL-‐AF9 target genes –  DAVID func�onal enrichment analysis of target genes

  Exercise 2: Associate broad H3K4me3 peaks in HSC with genes and func�ons –  Send analysis from UCSC Browser to GREAT –  Understanding gene – region associa�ons –  Visualize results

Outline of lab exercises

Chroma�n immunoprecipita�on followed by sequencing (ChIP-‐seq)

  Procedure for genome-‐wide assays of protein-‐DNA interac�on   Mapping histone modifica�ons seminal in epigene�cs research

ChIP Sequencing: Interroga�on of Histone Modifica�ons and Transcrip�on Factor Binding

  Resolu�on needs to be consistent (Covaris Adap�ve Acouts�cs)   An�body specificity, Chroma�n IP is a challenging technique

ChIP Sequencing: Computa�onal Analysis Workflow

Bailey et al. (2013). Prac�cal guidelines for the comprehensive analysis of ChIP-‐seq data. PLOS Computa�onal Biology. DOI: 10.1371/journal.pcbi.1003326

How can we visualize genomic data?

UCSC Genome Browser

Tools Covered: Genome Browser

and Table Browser

  Genome Browser zooms and scrolls over chromosomes, showing the work of annotators worldwide

  Table Browser provides convenient access to the underlying database

Bear with me I want to explain about the files we will use

  BED format provides a flexible way to define the data lines that are displayed in an annota�on track

  BED lines have up to 12 tab-‐delimited fields   required fields: chrom, chromStart, chromEnd   op�onal fields: name, score, strand, … and others.   Important, lower-‐numbered fields must always be populated

if higher-‐numbered fields are used.

BED Format

First ten lines of our mouse promoter file. The header line iden�fies the track name. Why am I using the first three op�onal fields? If my promoters are all the same size, what do you suppose is the score field?

  Allows display of con�nuous-‐valued data in track format   Useful for probability scores and transcriptome data

BedGraph Format

BedGraph files are very easy to work with, in my opinion

bigWig Format

The processed data we will work with today are in bigWig format

  For display of dense, con�nuous data   Elements must be equally sized   bigWig files are in an indexed binary format   Only the por�ons of the files needed to display a par�cular region are transferred to UCSC

  bigWig file remains on your web accessible server

Epigene�c profiling of HSC and LSC: Data Visualiza�on, Opera�ng on Genomic Intervals

Bernt et al . MLL-‐rearranged leukemia is dependent on aberrant H3K79 methyla�on by DOT1L. Cancer Cell. 2011 Jul 12;20(1):66-‐78.

  (Mixed Lineage Leukemia) MLL-‐AF9 fusion gene   Histone methyla�on pa�erns


GMP, granulocyte-‐macrophage progenitor, a myeloid precursor for monoblasts and myeloblasts

h�p://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29130


GMP, granulocyte-‐macrophage progenitor, a myeloid precursor for monoblasts and myeloblasts

h�p://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29130

In this exercise, our focus is MLL-‐AF9 fusion methyltransferase and HEK79me2 experiments. We will examine the chroma�n signals and interrogate enrichment at gene promoter regions.

Where did our bigWig files come from?

SRA -‐> fastq -‐> sam -‐> bam -‐> bed -‐> bedgraph -‐> bigWig

I created them from scratch, so to speak The nine job files, from top to bo�om, represent the different steps 9 Jobs x 4 Experiments: MLL-‐AF9 H3K79me2_mLSC H3K79me2_mGMP H3K79me2_mHSC

get.GSE29130.Chip-‐seq.job fastq-‐dump.job extrac�astq.job alignbow�e2.job samtools.sirdu.job btools.bamToBed.job btools.extendBed.job btools.sortBed.job btools.bambgbw.job


Adding Custom Tracks

From mm9 genome browser, choose Tools -‐> Table Browser Click on “add custom tracks”

The mouse gene promoter bed and ChIP-‐seq bigWig tracks should now appear on Manage Custom Tracks

Custom track files can also be uploaded via the “Choose File” op�on To upload many large files, you want to use a web server as we did above

From a separate browser window, copy the bigWig and bed file “UCSC Genome Browser Tracks” lines from h�p://dldcc-‐web.brc.bcm.edu/lilab/benji/MBRB_2015/GSE29130.track.list.txt

and paste them into “Paste URLs” box Click submit to load the tracks

Data Visualiza�on: Changing Track Display Se�ngs

The snapshot depicts the Tg�1 promoter region

Signal intensi�es appear comparable, but the axes have different display scales by default

We need a common scale for the H3K79me2 samples

There are several ways to access individual track se�ngs

0.2

1.7

3.0

2.0

MLL-‐AF9


Hint: use the tab key to

cycle through boxes quickly

(A�er) H3K79me2 experiments ver�cal axis set to V-‐max = 5 Now you can see H3K79me2 enrichment greater in GMP than LSC In contrast, MLL-‐AF9 binding appears minimal (set V-‐max = 1.5)

A�er Before

MLL-‐AF9 MLL-‐AF9

Meis1 is a MLL-‐fusion target iden�fied by Bernt et al as well as a previous study in Genes and Development Let’s use the Meis1 promoter to make a quick and dirty cut-‐off to separate MLL-‐AF9 signal from noise.

Posi�ve controls can show us the difference between signal and noise in ChIP-‐seq data

MLL-‐AF9

Meis1 gene

Summary of MLL-‐AF9 protein binding signal at Meis1 promoter

Meis1 promoter

From Table Browser, select “create filter” [ 1 ] to bring up “Filter on Fields” [ 2 ] Set dataValue > 0.318759 (mean of Meis1 promoter) Set data output 10E7 lines Press submit [ 3 ] Returning to Table Browser, set output format to “custom track”

Quick and Dirty MLL-‐AF9 Signal Filter

1 2

3

Output custom track with with a name (no spaces), descrip�on Select “BED format” for output Get custom track in browser (table or genome)


3

Genome-‐wide summary sta�s�cs show 1.16 million of 2.62 billion bases have signal > mean of the Meis1 promoter


1 2

3

Examining Our Custom MLL-‐AF9 Signal Track

3

chr6:52,155,000-‐52,187,500

The four Hoxa genes and Mir196b were predicted by the Bernt et al paper’s empirical null distribu�on model Pre�y cool for arbitrary, eh?

MLL-‐AF9 protein binding signal (Filtered)

Unfiltered MLL-‐AF9

  We’ve isolated strong MLL-‐AF9 protein binding signals

  We know the physical loca�ons of gene promoter regions

  Can we use the table browser to iden�fy promoters with MLL-‐AF9?

  Yes, by opera�ng on genomic intervals

Crea�ve solu�ons to complex problems

Intersec�ons of genomic intervals

Opera�ng on genomic intervals From Table Browser, choose mm9_promoter track Click on “create intersec�on” to bring up the Intersect window Select your MLL-‐AF9 custom track Select “all records” overlap op�on Click submit Screen returns to Table Browser

Click on summary sta�s�cs to see the number of intersec�ng promoters. Press back to return to Table Browser

On Table Browser, select output format “BED – browser extensible data” Click on “get output” On the next screen, choose “get BED”

Opera�ng on genomic intervals

  Our BED file of MLL-‐AF9 bound promoters contains the informa�on necessary for func�onal enrichment analyses as well as addi�onal intersec�ons with other data, such as H3K79me2 levels   BED files can be created from almost any annota�on track in the UCSC browser   Propose a query you would like to make on the MLL-‐AF9 promoters and plan your a�ack   Alterna�vely, take the fi�h column of the promoter file (Entrez gene iden�fiers) and run an enrichment analysis at h�p://david.abcc.ncifcrf.gov

Func�onal enrichment analyses with DAVID

  Comprehensive set of func�onal annota�on tools for inves�gators to understand biological meaning behind large list of genes

  We will use DAVID to analyze our MLL-‐AF9 target genes

Open h�p://david.abcc.ncifcrf.gov and choose “Start Analysis”

  “Upload Gene List” Dialog box   Step 1: Copy and Paste the entrez gene ID’s from MLL-‐

AF9_promoters.bed file (column 5)   Step 2: On “Select Iden�fier”, choose “ENTREZ_GENE_ID”

Step 3: Choose “Gene List” on “List Type”   Step 4: Submit List

DAVID: Upload Gene List

Note: Entrez Gene ID’s are a preferred way to search for gene func�ons They can account for the fact that a gene may go by several different names

For species, highlight Mus musculus and click “Select Species”

Rename the list

DAVID: Analyze Gene List

Choose “Func�onal Annota�on Tool”

DAVID: What does all this stuff mean????

Each Annota�on Category on the le� can be expanded to reveal a number of op�onal databases to query This allows for powerful customiza�on For this exercise, we will accept the default op�ons

Choose “Func�onal Annota�on Chart”

Hint: Rerun using op�ons Count >= 4 removes weak results Fold enrichment provides you a new, valuable metric FDR gives you another choice mul�ple tes�ng correc�on

DAVID: Don’t drown in the details

  A func�onal annota�on tool will present you with many choices   Stay focused, take notes of what parameters you tried

DAVID: Examine enriched terms Top enriched biological processes pertain to transcrip�onal regula�on and cell cycle

Func�onal Annota�on Chart default fields are: category, term, related term (RT), genes, count, percentage, p-‐value (univariate modified Fisher’s), and Benjamini p-‐value (correc�on for mul�ple tes�ng)

Terms with arrows can be sorted

Hint. If you have a long list of records, try sor�ng by Fold Enrichment and FDR fields to get a different sense of the data

DAVID: Explore a par�cular result Clicking on the genes list bar for GO BP term “regula�on of transcrip�on”

Hint: If you want to capture the list of genes, click on download file

Text file easier to paste into Excel than html!

Clicking on the gene link for Meis homeobox 1 brings up gene informa�on, links to publica�ons, database entries

DAVID: Explore a par�cular result deeper

DAVID: Addi�onal direc�ons to take

Func�on annota�on clustering provides another way to explore rela�onships between related terms in your results Help get at the underlying biology Genes enriched in TF regulatory ac�vity, cell cycle, SAND domain

Exercise 1 Summary

  Access, load data to UCSC genome browser   Importance of viewing scales   We are not limited to simple, passive browsing of chromosomes (Empowered by Table Browser)   Intersect MLL-‐AF9 enriched signal and promoters   Func�onal enrichment analyses in DAVID   Biological inferences: 1.  MLL-‐AF9 target genes o�en involved in cell cycle,

regula�on of transcrip�on

Exercise 2: Associate Broad H3K4me3 peaks in

HSC with genes and func�ons

Scale

chr19:

_14563

10 kb mm9

32,825,000 32,830,000 32,835,000 32,840,000RefSeq Genes

User Supplied Track

m24_H3K4me3

m24_RatioDepth

Pten

100 _

0 _

100 _

-10 _

  The MLL-‐AF9 target gene exercise relied on assump�on that promoters were the only region of interest

  Protein binding can occur outside of promoters or gene bodies

  How can iden�fy the genes a DNA sequence might regulate?

  Use GREAT to associate broad H3K4me3 peaks with nearby genes

What if we don’t know the genes?

Explore broad H3K4me3 peaks of HSC in GREAT

Defining a Gene regulatory domain

GREAT Interface from UCSC Table Browser

Sending Custom Track Data to GREAT

Select your custom track containing broad H3K4me3 peaks Set region to genome Set output format to BED and checkmark send output to GREAT

Sending Custom Track Data to GREAT

The next dialog box allows you to modify our regions of interest Leave default “Whole Gene” checked and click send query to GREAT

GREAT Job Descrip�on Screen

GREAT analysis returns a html page with mul�ple sec�ons Descrip�on does a pre�y thorough job of explaining how you got here

GREAT: Viewing Region – Gene Associa�ons

GREAT: Sta�s�cal test results of 18 different gene annota�ons

Results table for the first ontology GO MF

Review tabular results for enriched term “transcrip�on cofactor ac�vity”

Review predicted region – gene associa�ons therein

Can download tables

Ontologies maybe visualized in different ways

  Sta�c bar plot   customize appearance

  Save file   Perfect for Lab mee�ng presenta�ons!!

Detailed explana�on of plot

Display op�ons

0 2 4 6 8 10 12 14 16 18m alignant neoplasm of ... nd hem opoiet ic t issue

hem atologic cancerleukem ia

lym phoid cancerlym phoproliferat ive disease

im m unoproliferat ive diseaseDNA virus infect ious disease

bone m arrow cancerbone m arrow disease

18.0117.64

15.7911.71

11.2111.20

10.218.91

8.61

Disease Ont ology-log10(Binom ial p value)

Job ID: 20150902-public-3.0.0-0QMl7gDisplay nam e: H3K4m e3_m 24_b19_run03_peaks.clean.cutoff.4kb.bed5

Top enriched diseases in broad H3K4me3 peaks of HSC

Example of customizing plot appearance Exported as PDF

aabbnnoorrmm aall bblloooodd cceellllabnorm al blood cellmm oorrpphhoollooggyy//ddeevveellooppmm eem orphology/developm e

ttt

2 .3 6

aabbnnoorrmm aallabnorm alhheemm aattooppooiieessiisshem atopoiesis

2 .3 6

aabbnnoorrmm aallabnorm alhheemm aattooppooiieett iicchem atopoiet ic

ssyysstteemmsystemmm oorrpphhoollooggyy//ddeevveellooppmm eem orphology/developm e

ttt

2 .2 1

hheemm aattooppooiieett iicchem atopoiet icssyysstteemm pphheennoottyyppeesystem phenotype

2 .1 9

ddeeccrreeaasseedddecreasedhheemm aattooppooiieett iicc cceellllhem atopoiet ic cell

nnuumm bbeerrnum ber

2 .8 4

aabbnnoorrmm aall lleeuukkooccyytteeabnorm al leukocytemm oorrpphhoollooggyym orphology

2 .4 8

aabbnnoorrmm aallabnorm alhheemm aattooppooiieett iicc cceellllhem atopoiet ic cell


2 .5 4

aabbnnoorrmm aall iimm mm uunneeabnorm al im m unessyysstteemm cceellllsystem cellmm oorrpphhoollooggyym orphology

2 .4 7

aabbnnoorrmm aallabnorm almm oonnoonnuucclleeaarr cceellllm ononuclear cell

mm oorrpphhoollooggyym orphology

2 .5 7

aabbnnoorrmm aall iimm mm uunneeabnorm al im m unessyysstteemm mm oorrpphhoollooggyysystem m orphology

2 .2 6

aabbnnoorrmm aall llyymm pphhooccyytteeabnorm al lym phocytemm oorrpphhoollooggyym orphology

2 .7 2

aabbnnoorrmm aall lleeuukkooccyytteeabnorm al leukocytecceellll nnuumm bbeerrcell num ber

2 .5 8

aabbnnoorrmm aall bboonneeabnorm al bonemm aarrrrooww cceellllm arrow cell

mm oorrpphhoollooggyy//ddeevveellooppmm eem orphology/developm ettt

2 .7 3

aabbnnoorrmm aall llyymm pphhooccyytteeabnorm al lym phocytecceellll nnuumm bbeerrcell num ber

2 .7 3

ddeeccrreeaasseedd lleeuukkooccyytteedecreased leukocytecceellll nnuumm bbeerrcell num ber

2 .7 7

ddeeccrreeaasseedddecreasedllyymm pphhooccyyttee cceelllllym phocyte cell


2 .9 5

aabbnnoorrmm aallabnorm allleeuukkooppooiieessiissleukopoiesis

2 .6 1

aabbnnoorrmm aall iimm mm uunneeabnorm al im m unessyysstteemm oorrggaannsystem organmm oorrpphhoollooggyym orphology

2 .3 9

aabbnnoorrmm aall mm yyeelloobbllaassttabnorm al m yeloblastmm oorrpphhoollooggyy//ddeevveellooppmm eem orphology/developm e

ttt

2 .6 2

aabbnnoorrmm aall BB cceellllabnorm al B cellmm oorrpphhoollooggyym orphology

2 .9 6

Very General: Hematopoie�c

system phenotype

More Specific: Abnormal

hematopoie�c cell number

Very Specific: Decreased lymphocyte cell number

Visualize hierarchical rela�onships in DAG

  Most gene annota�on systems contain an organiza�onal hierarchy

  We are examining Mouse Phenotypes set

  DAG plots systems of events and rela�onships between them

  Nodes (circles) are the enriched terms sized according to fold-‐enrichment

Exercise 2 Summary

  Easy to pass a dataset from UCSC browser to GREAT   How to define gene regulatory domains with dataset   Results can be visualized in different ways   Biological inferences: 1.  Broad H3K4me3 peaks are highly enriched for

genes involved in regula�on of TF binding 2.  Subset of genes func�on in hematopoiesis,

implicated in cancers of bone marrow

  Exercise 1: Epigene�c profiling of HSC and LSC –  Data Visualiza�on, Opera�ng on Genomic Intervals –  Crea�ve problem solving for MLL-‐AF9 target genes –  DAVID func�onal enrichment analysis

  Exercise 2: Associate broad H3K4me3 peaks in HSC with genes and func�ons –  Send analysis from UCSC Browser to GREAT –  Understanding gene – region associa�ons –  Visualize results

  Meaningful Biological Inference!

Summary of lab exercises

Thanks for your �me and a�en�on

a field guide to epigenomics - research informatics in … field guide to epigenomics benjamin...

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