# Valorisations of epigenomic and transcriptomic data

Guillaume Devailly

► To cite this version:

Guillaume Devailly. Valorisations of epigenomic and transcriptomic data. Animation scientifique d'Unité, Dec 2017, Toulouse, France. hal-01800387

HAL Id: hal-01800387

https://hal.science/hal-01800387v1

Submitted on 4 Jun 2020

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Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
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INRA
SCIENCE & IMPACT

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GenEpi@GenPhySE

# Valorisations of epigenomic and transcriptomic data

Guillaume Devailly

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@G_Devailly

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# Summary

Transcriptional regulation: a 2-slides introduction

* MBD2, a major DNA methylation reader

* Heat seq web app:
http://www.heatstarseq.roslin.ed.ac.uk/

* Functional annotation transfert using co-expression networks

* Epigenomics and the human transcript diversity

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* Same genome

* Distinct cellular phenotypes

* Differential gene expression

* Cellular environment + epigenetics

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Centre Léon Bérard
LYON ET RHÔNE-ALPES

CRCL
CENTRE DE RECHERCHE EN CANCÉROLOGIE DE LYON

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UCL

Lyon 1

CONTRE
LE CANCER

LA LIQUE
101
comités

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Inserm

Institut national
de la santé et de la recherche médicale

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DE LYON

# MBD2 binding dynamics during oncogenic transformation

## PhD at the Cancer Research Center of Lyon

Supervisor: Robert Dante

Patrick Mehlen's team

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INRA
SCIENCE & IMPACT

@G_Devailly

.05

2017-12-19
# Vertebrate DNA methylation
## A prototipical epigenetics mark

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1- Write

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2- Read

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3- Erase

* 5hmC TET + BER

* BER / NER / MMR

* passive demethylation
### MBD family

MECP2 MBD

1

MBD2 MBD

4

### UHRF family

UHRF UHRF

1

2

MBD2 represses
transcription of
methylated plasmids.

Ng et al.,
Nature Genetics, 1999, Guy et al., Nat Genet., 2001

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# DNA methylation alterations in cancer

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HMEC-hTERT

ChIP
MBD 2
MBD2 peaks

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HMLER

ChIP
MBD 2
MBD2 peaks

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HMEC-hTERT

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Immortalized
Non tumorigenic

HMLER

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Tumorigenic

ChIP-seq:

Input
MBD2 ChIP
MeDP (MethylMiner, invitrogen)

**Endogenous proteins**

Pool of 5 experiments

RNA-seq:

RNA extract from
untreated cells or cells
treated with **siMBD2** or
**DAC**

High throughput

30 - 50 millions of 50 bp reads per

sequencing

conditions

Illumina HiSeq 2000

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华大基因

BGI

ProfileXpert - LCMT

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# MBD2 ChIPseq validation

## 1/3:

### ChIP-qPCR

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Gene R
Amplicon localization (green)
CpG island (red)

PANX2

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PRKCH

CDH13

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# MBD2 ChIPseq validation

## 2/3:

<figure><img src="image_45.png" /><figcaption>MBD2 siRNA</figcaption></figure>
# Endogenous MBD2 binds methylated DNA
## *in vivo*

Almost all
MBD2 binding
sites are
methylated

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> 78% of
peaks are
methylated
# Endogenous MBD2 binds methylated DNA
## *in vivo*

Almost all
MBD2 binding
sites are
methylated

1/4th of
methylated DNA
regions are bound
by endogenous
MBD2

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# Endogenous MBD2 binds methylated DNA

## in vivo

Almost all
MBD2 binding
sites are
methylated

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1/4th of
methylated DNA
regions are bound
by endogenous

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MBD2 binding
on CpG island
depends on
their
methylation
levels
# Endogenous MBD2 is a DNA methylation dependent transcriptional repressor

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RNAi after depletion of MBD2 by siRNA, or after DAC (5-aza-deoxycytidine) treatment (DNA methylation enzymes inhibited)

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# MBD2 is a DNA methylation dependent transcriptional repressor

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RNAseq after depletion of MBD2 by siRNA, or after DAC (5-aza-deoxycytidine) treatment (DNA methylation enzymes inhibitor)

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2164 genes up by **siMBD2**
70% also up by DAC

667 genes down by **siMBD2**
40% also up down DAC

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3510 genes up by DAC
43% also up by **siMBD2**

1459 genes down by DAC
18% also up down **siMBD2**

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Redistribution of MBD2 during oncogenic transformation: example of CLDN6/TNFRSF12A

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RNAseq relative expression level

CLDN6 (313 +/- 5.2)

siCtrl - + -
siMBD2 - - + -
DAC - - - +

HMEC-hTERT

HMLER

TNFRSF12A

siCtrl - + -
siMBD2 - - + -
DAC - - - +

HMEC-hTERT

HMLER

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HMEC-hTERT
HMLER

Genes upregulated by siMBD2 in HMLER

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Genes downregulated during transformation

Genes upregulated by DAC in HMLER

<footnote>*p<0.01</footnote>
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DNA methylation (MeDSeq)

MBD2 ChIPseq

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Transcription level
(RNAseq)

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Conclusions & Perspectives

* **MBD2** is a major reader of DNA methylation, at least in studied cell lines.

* **MBD2** is redistributed during oncogenic transformation. This redistribution plays role in the transcriptome modifications of transformed cell lines.

* What are the mechanism implicated in the redistribution of MBD2?

* Does targeting MBD2 reduces the transformed phenotype?

http://ngs-qc.org/navi/index.php

<table><thead><tr><th>Accession</th><th>Organism</th><th>Sample</th><th>Experiment</th><th>Mapped Reads</th><th>QC stamp</th></tr></thead><tbody><tr><td>GSM1544114</td><td>Homo sapiens</td><td>N/A</td><td>MBD2 ChIP-seq</td><td>57,009,909</td><td>BBB</td></tr><tr><td>GSM1544111</td><td>Homo sapiens</td><td>N/A</td><td>MBD2 ChIP-seq</td><td>57,767,880</td><td>BBB</td></tr><tr><td>GSM972973</td><td>Mus musculus</td><td>ESC, stem cell</td><td>MBD2 ChIP-seq</td><td>28,211,050</td><td>CCB</td></tr><tr><td>GSM2534656</td><td>Homo sapiens</td><td>immortalized cell</td><td>MBD2 ChIP-seq</td><td>23,401,218</td><td>CCC</td></tr><tr><td>GSM1322266</td><td>Homo sapiens</td><td>N/A</td><td>MBD2 ChIP-seq</td><td>32,869,475</td><td>CCC</td></tr><tr><td>GSM972994</td><td>Mus musculus</td><td>ESC, stem cell</td><td>MBD2 ChIP-seq</td><td>19,671,342</td><td>CDD</td></tr><tr><td>GSM2527610</td><td>Homo sapiens</td><td>K-562</td><td>MBD2 ChIP-seq</td><td>23,414,725</td><td>CDD</td></tr><tr><td>GSM1388122</td><td>Homo sapiens</td><td>N/A</td><td>MBD2 ChIP-seq</td><td>27,212,212</td><td>DCC</td></tr><tr><td>GSM972978</td><td>Mus musculus</td><td>N/A</td><td>MBD2 ChIP-seq</td><td>25,225,988</td><td>DCC</td></tr><tr><td>GSM2527611</td><td>Homo sapiens</td><td>K-562</td><td>MBD2 ChIP-seq</td><td>27,605,362</td><td>DCC</td></tr><tr><td>GSM1077269</td><td>Mus musculus</td><td>ESC, stem cell</td><td>MBD2 ChIP-seq</td><td>27,360,560</td><td>DDC</td></tr><tr><td>GSM1006707</td><td>Homo sapiens</td><td>HeLa</td><td>MBD2-V5 ChIP-seq</td><td>10,943,628</td><td>DDD</td></tr></tbody></table>
Thanks

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Robert Dante
Mélodie Grandin
Pauline Mathot
Véronique Corset
Catherine Guy
Solène Le Guervenel
Duygu Ozmadenci
Laury Perriaud
Anne-Pierre Morel
Patrick Mehlen

Collaborators

PARIS
DIDEROT

Epigenetics
& Cell Fate

CNRS UMR7216, Université Paris 7

Pierre-Antoine Defossez
Olivier Kirsh
Benoit Miotto
Audrey Roussel-Gervais

Centre Léon Bérard
LYON ET RHÔNE-ALPES

CRCL
CENTRE DE RECHERCHE EN CANCEROLOGIE DE LYON

ULB
Lyon 1

CONTRE
LE CANCER

LA LIQUE
101
comités

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Inserm

Institut national
de la santé et de la recherche médicale

UNIVERSITE DE LYON

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Bioconductor
OPEN SOURCE SOFTWARE FOR BIOINFORMATICS

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Heat\*seq

RNA-seq

ChIP-seq datasets

CAGE in

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Compare your HTS
experiment with

Guillaume Devailly

Anagha Joshi's group, The Roslin Institute, University of Edinburgh

@G_Devailly

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by

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HTS is cheap

Big public
datasets

Genome-wide
comparison is
challenging

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1. Visit website: www.heatstarseq.roslin.ed.ac.uk

2. Select dataset

4. Explore results

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3. Upload processed data

<table><thead><tr><th>experiment</th><th>correlation</th></tr></thead><tbody><tr><td>brain GSM1020649</td><td>0.9496700</td></tr><tr><td>Ammon's horn GSM759591</td><td>0.9445169</td></tr><tr><td>brain GSM1015150</td><td>0.9430056</td></tr><tr><td>brain GSM1015151</td><td>0.9426334</td></tr><tr><td>brain GSM1020657</td><td>0.9415478</td></tr><tr><td>Ammon's horn GSM759593</td><td>0.9414239</td></tr><tr><td>Ammon's horn GSM759589</td><td>0.9400874</td></tr><tr><td>Ammon's horn GSM759592</td><td>0.9383826</td></tr><tr><td>brain GSM752615</td><td>0.9375823</td></tr><tr><td>brain GSM1020640</td><td>0.9374279</td></tr></tbody></table>

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female gonad GSM1196047
female gonad GSM1196046
female gonad GSM1064842
brain GSM1069683
brain GSM1064841
brain GSM752616
brain GSM752614
brain GSM752615
brain GSM1020649
brain GSM1020640
brain GSM1020657
brain GSM1015150
brain GSM1015151
my RNA-seq

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Pearson correlation coefficient: 0.9497

Spearman correlation coefficient: 0.902
# Datasets

<table><thead><tr><th>Assay</th><th>Dataset</th><th>Organism</th><th>Number of experiments</th></tr></thead><tbody><tr><td rowspan="2">RNA-seq</td><td rowspan="2">Bgee</td><td>human</td><td>77</td></tr><tr><td>mouse</td><td>109</td></tr><tr><td></td><td>Blueprint epigenome</td><td>human</td><td>163</td></tr><tr><td></td><td>ENCODE</td><td>human</td><td>302</td></tr><tr><td></td><td>Roadmap Epigenomics</td><td>human</td><td>57</td></tr><tr><td></td><td>Flybase</td><td>drosophila</td><td>124</td></tr><tr><td></td><td>GTEx</td><td>human</td><td>8555</td></tr><tr><td rowspan="4">TF ChIP-seq</td><td rowspan="2">ENCODE</td><td>human</td><td>690</td></tr><tr><td>mouse</td><td>156</td></tr><tr><td rowspan="2">CODEX</td><td>human</td><td>238</td></tr><tr><td>mouse</td><td>651</td></tr><tr><td></td><td>modENCODE</td><td>drosophila</td><td>85</td></tr></tbody></table>

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## Expression matrix

<table><thead><tr><th>Gene name<br>(~40.000)</th><th>Exp 1</th><th>Exp 2</th><th>Exp 3</th><th>...</th><th>Exp 77</th></tr></thead><tbody><tr><td>ENSG0000000000<br>3</td><td>3.983</td><td>2.361</td><td>10.21<br>6</td><td>...</td><td>80.58<br>3</td></tr><tr><td>ENSG0000000000<br>5</td><td>0.071</td><td>0.260</td><td>0.000</td><td>...</td><td>3.329</td></tr><tr><td>ENSG0000000041<br>9</td><td>10.27<br>7</td><td>2.893</td><td>14.15<br>3</td><td>...</td><td>42.63<br>9</td></tr><tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr><tr><td>ENSG0000027349<br>3</td><td>0.000</td><td>0.000</td><td>0.000</td><td>...</td><td>...</td></tr></tbody></table>

## Correlation matrix

Pearson's, after log10

<table><thead><tr><th>\</th><th>Exp 1</th><th>Exp 2</th><th>Exp 3</th><th>...</th><th>Exp 77</th></tr></thead><tbody><tr><td>Exp 1</td><td>1</td><td>0.94<br>2</td><td>0.93<br>8</td><td>...</td><td>0.66<br>3</td></tr><tr><td>Exp 2</td><td>0.94<br>2</td><td>1</td><td>0.91<br>7</td><td>...</td><td>0.68<br>0</td></tr><tr><td>Exp 3</td><td>0.93<br>8</td><td>0.91<br>7</td><td>1</td><td>...</td><td>0.70<br>6</td></tr><tr><td>...</td><td>...</td><td>...</td><td>...</td><td>1</td><td>...</td></tr><tr><td>...</td><td>0.66<br>3</td><td>0.68<br>0</td><td>0.70<br>6</td><td>...</td><td>1</td></tr></tbody></table>

Clustered heatmap

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Binary peak

<table><thead><tr><th colspan="5">matrix</th></tr><tr><th>Coordinates<br>(~700.000 non<br>overlapping<br>regions)</th><th>Exp 1</th><th>Exp 2</th><th>Exp 3</th><th>...</th><th>Exp<br>690</th></tr></thead><tbody><tr><td>chr1:10073-10413</td><td>F</td><td>T</td><td>F</td><td>...</td><td>F</td></tr><tr><td>chr1:16110-16390</td><td>F</td><td>F</td><td>T</td><td>...</td><td>T</td></tr><tr><td>chr1:29198-29688</td><td>F</td><td>F</td><td>F</td><td>...</td><td>F</td></tr><tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr><tr><td>chrY:28709160-<br>28709494</td><td>T</td><td>T</td><td>F</td><td>...</td><td>...</td></tr></tbody></table>

Correlation

<table><thead><tr><th></th><th colspan="3">matrix</th><th>...</th><th>Exp<br>690</th></tr><tr><th></th><th>1</th><th>2</th><th>3</th><th>...</th><th>Exp<br>690</th></tr></thead><tbody><tr><th>Exp<br>1</th><td>1</td><td>0.05<br>9</td><td>0.78<br>6</td><td>...</td><td>0.03<br>5</td></tr><tr><th>Exp<br>2</th><td>0.05<br>9</td><td>1</td><td>0.05<br>8</td><td>...</td><td>0.11<br>8</td></tr><tr><th>Exp<br>3</th><td>0.78<br>6</td><td>0.05<br>8</td><td>1</td><td>...</td><td>0.04<br>7</td></tr><tr><th>...</th><td>...</td><td>...</td><td>...</td><td>1</td><td>...</td></tr><tr><th>...</th><td>0.03<br>5</td><td>0.11<br>8</td><td>0.04<br>7</td><td>...</td><td>1</td></tr></tbody></table>

Clustered<br>heatmap

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## Tab-delimited text file

### RNA-seq

HeatRNAseq

<table><thead><tr><th>geneID</th><th>tpm</th></tr></thead><tbody><tr><td>ENSG000001340<br>46</td><td>120.12</td></tr><tr><td>ENSG000001416<br>44</td><td>0</td></tr><tr><td>ENSG000001690<br>57</td><td>85.24</td></tr><tr><td>ENSG000001742<br>82</td><td>0.54</td></tr><tr><td>ENSG000001870<br>98</td><td>42</td></tr><tr><td>...</td><td>...</td></tr></tbody></table>

Ensembl or Flybase gene

### ChIP-seq

HeatChIPseq

### CAGE

HeatCAGEseq
Tab-delimited text
file

RNA-seq

HeatRNAseq

ChIP-seq

HeatChIPseq

CAGE

HeatCAGEseq

<table><thead><tr><th>chr</th><th>start</th><th>end</th></tr></thead><tbody><tr><td>chr1</td><td>125423</td><td>125891</td></tr><tr><td>chr1</td><td>854503</td><td>854625</td></tr><tr><td></td><td>2</td><td>4</td></tr><tr><td>chr4</td><td>452369</td><td>452478</td></tr><tr><td></td><td>8</td><td>5</td></tr><tr><td>chr12</td><td>854120</td><td>854870</td></tr><tr><td>chrX</td><td>245875</td><td>245987</td></tr><tr><td></td><td>0</td><td>9</td></tr><tr><td>...</td><td>...</td><td>...</td></tr></tbody></table>

Same genome version
... than the dataset
Tab-delimited text
file

RNA-seq

HeatRNAseq

ChIP-seq

HeatChIPseq

CAGE

HeatCAGEseq

<table><thead><tr><th>chr</th><th>start</th><th>end</th><th>name</th><th>rpm</th><th>strand</th></tr></thead><tbody><tr><td>chr2</td><td>25486325</td><td>25486487</td><td>CAGEpeak_1</td><td>458.12</td><td>+</td></tr><tr><td>chr6</td><td>5896321</td><td>5896380</td><td>CAGEpeak_2</td><td>25.03</td><td>+</td></tr><tr><td>chr6</td><td>223541</td><td>223602</td><td>CAGEpeak_3</td><td>1.23</td><td>-</td></tr><tr><td>chr17</td><td>5012035</td><td>5012100</td><td>CAGEpeak_4</td><td>45.3</td><td>+</td></tr><tr><td>chr21</td><td>960032</td><td>960098</td><td>CAGEpeak_5</td><td>8.70</td><td>-</td></tr><tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr></tbody></table>
**Source code:**

https://github.com/gdevailly/HeatStarSeq_gh

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www.heatstarseq.roslin.ed.ac.uk
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Heat*seq

An interactive web tool for high-throughput sequencing experiment comparison with public data

RNA-seq

HeatRNAseq

ChIP-seq

HeatChIPseq

CAGE

HeatCAGEseq

<figure><img src="image_91.png" /></figure>
<figure><img src="image_92.png" /></figure>

Heat*seq

Use application

Instructions

# HeatRNAseq

## 1 - Select a dataset

Bgee RNA-seq (mouse)

## 2 - Load your data (optional)

File formatting instructions

<figure><img src="image_93.png" /></figure>

Upload your expression file

<figure><img src="image_94.png" /></figure>

Use the example file

Choose a file:

Browse...

No file selected.

<figure><img src="image_95.png" /></figure>

The expression file contains a header.

Name of your experiment:

my RNA-seq

## 3 - Plot customization

<figure><img src="image_96.png" /></figure>

Highlight my experiment in the heatmap.

Tissue (empty to select all):

Developmental stage (empty to select all):

Library type (empty to select all):

Uploaded experiment correlation correction:

None

Advanced clustering options

My expression file

Correlation table

Static heatmap

Responsive heatmap

Tree

Pairwise plot

Samples metadata

<figure><img src="image_97.png" /></figure>
<figure><img src="image_98.png" /></figure>

# HeatRNAseq

## 1 - Select a dataset

Bgee RNA-seq (mouse)

## 2 - Load your data (optional)

File formatting instructions

<figure><img src="image_99.png" /></figure>

Upload your expression file

<figure><img src="image_100.png" /></figure>

Use the example file

Choose a file:

Browse...

No file selected.

<figure><img src="image_101.png" /></figure>

The expression file contains a header.

Name of your experiment:

my RNA-seq

## 3 - Plot customization

<figure><img src="image_102.png" /></figure>

Highlight my experiment in the heatmap.

Tissue (empty to select all):

Developmental stage (empty to select all):

Library type (empty to select all):

Uploaded experiment correlation correction:

None

Advanced clustering options
<figure><img src="image_103.png" /></figure>

Heat\*seq

Use application

Instructions

# HeatRNAseq

## 1 - Select a dataset

Bgee RNA-seq (mouse)

## 2 - Load your data (optional)

File formatting instructions

<figure><img src="image_104.png" /></figure>

Upload your expression file

<figure><img src="image_105.png" /></figure>

Use the example file

Choose a file:

Browse...

No file selected.

<figure><img src="image_106.png" /></figure>

The expression file contains a header.

Name of your experiment:

my RNA-seq

## 3 - Plot customization

<figure><img src="image_107.png" /></figure>

Highlight my experiment in the heatmap.

Tissue (empty to select all):

Developmental stage (empty to select all):

Library type (empty to select all):

Uploaded experiment correlation correction:

None

Advanced clustering options

My expression file

Correlation table

Static heatmap

Responsive heatmap

Tree

Pairwise plot

Samples metadata

<figure><img src="image_108.png" /></figure>
# HeatRNAseq

## 1 - Select a dataset

Bgee RNA-seq (mouse)

## 2 - Load your data (optional)

File formating instructions

* Upload your expression file

* Use the example file

Choose a file:

Browse...

mk3_1.txt

Upload complete

☑ The expression file contains a header.

Name of your experiment:

kidney mk3 cell line

## 3 - Plot customization

☑ Highlight my experiment in the heatmap.

Tissue (empty to select all):

Developmental stage (empty to select all):

Library type (empty to select all):

My expression file

Correlation table

Static heatmap

Responsive heatmap

Tree

Pairwise plot

Samples metadata

<figure><img src="image_109.png" /></figure>
## 3 - Plot customization

<figure><img src="image_110.png" /></figure>

Highlight my experiment in the heatmap.

Tissue (empty to select all):

<figure><img src="image_111.png" /></figure>

Developmental stage (empty to select all):

<figure><img src="image_112.png" /></figure>

Library type (empty to select all):

<figure><img src="image_113.png" /></figure>

Uploaded experiment correlation correction:

None

<figure><img src="image_114.png" /></figure>

Advanced clustering options

Label size:

Automatic

Adjust manually

Show dendrogram(s)?

both

<figure><img src="image_115.png" /></figure>

Show labels?

both

<figure><img src="image_116.png" /></figure>

Customise colours

Tissue (empty to select all):

adult mammalian kidney b

brain

bone marrow

brown adipose tissue

bone marrow macrophage

cerebellum

fibroblast

cerebral cortex

embrvonic stem cell

<figure><img src="image_117.png" /></figure>

<figure><img src="image_118.png" /></figure>

<figure><img src="image_119.png" /></figure>

<figure><img src="image_120.png" /></figure>

<figure><img src="image_121.png" /></figure>

adult mammalian kidney GSM929706

kidney mk3 cell line

adult mammalian kidney GSM1015153

adult mammalian kidney GSM1020660

adult mammalian kidney GSM1020643

adult mammalian kidney GSM1020651

adult mammalian kidney GSM752624

adult mammalian kidney GSM752623

adult mammalian kidney GSM752625

spleen GSM759610

spleen GSM759607

spleen GSM759612

spleen GSM759611

spleen GSM759608

spleen GSM759609

spleen GSM1020655

spleen GSM1020664

spleen GSM1020647

spleen GSM929720

<figure><img src="image_122.png" /></figure>
<figure><img src="image_123.png" /></figure>

<figure><img src="image_124.png" /></figure>
<figure><img src="image_125.png" /></figure>

## 3 - Plot customization

<figure><img src="image_126.png" /><figcaption>Correlation coefficient</figcaption></figure>

<figure><img src="image_127.png" /></figure>

<figure><img src="image_128.png" /></figure>
<figure><img src="image_129.png" /></figure>

<figure><img src="image_130.png" /></figure>
HeatRNAseq

My expression file

Correlation table

Static heatmap

Responsive heatmap

Tree

Pairwise plot

Samples metadata

<figure><img src="image_131.png" /></figure>

<figure><img src="image_132.png" /></figure>
<figure><img src="image_133.png" /></figure>

<figure><img src="image_134.png" /></figure>

<figure><img src="image_135.png" /></figure>
<figure><img src="image_136.png" /></figure>

<figure><img src="image_137.png" /></figure>

<figure><img src="image_138.png" /></figure>

Mouse hovering widget

Zoom by drag and drop

Send plot to plotly and share with
the world
Show 25 entries

Search:

<figure><img src="image_139.png" /></figure>

<figure><img src="image_140.png" /></figure>
HeatRNAseq

My expression file

Correlation table

Static heatmap

Responsive heatmap

Tree

Pairwise plot

Samples metadata

<figure><img src="image_141.png" /></figure>

Pearson correlation coefficient: 0.833
Spearman correlation coefficient: 0.7625

<figure><img src="image_142.png" /></figure>

Pearson correlation coefficient: 0.944
Spearman correlation coefficient: 0.835

## 3 - Plot customization

### Experiment 1:

adult mammalian kidney GSM1015153

### Experiment 2:

adult mammalian kidney GSM1020643

### Plot type:

XY

### Data scaling:

log10(e + 1)

<figure><img src="image_143.png" /></figure>

Add regression line (blue)

<figure><img src="image_144.png" /></figure>

Add guide line (red)
* A lightweight web application

* Quick comparison of RNA-seq, ChIP-seq and CAGE experiments

* First step to guide you before in depth analysis
* Other datasets (suggestions?)

* Other data types (Histones, DNAse1, Gene Lists/Ontologies)

* Multiple user files

* Gene name converter / liftover integration
<figure><img src="image_145.png" /></figure>

<figure><img src="image_146.png" /></figure>

Functional annotation transfert
using co-expression networks

Pía Francesca Loren Reyes, Tom Michoel, Anagha Joshi,
Guillaume Devailly

<figure><img src="image_147.png" /></figure>
<figure><img src="image_148.png" /></figure>

* Plenty of omic data

* Not enough functional annotations :(

* Can we use omic data to improve functional
  annotations?

<figure><img src="image_149.png" /></figure>
* Functional annotations are often inferred from other species

* This lead to over-annotations mistakes, notably in the cases of one-to-many or many-to-many homology groups

**Hypothesis**: if two orthologs are in the **same** **co-expression cluster** in different species, then functional annotation are likely to be transferable.

And co-expression cluster are easy to build from public omic data! \o/

<figure><img src="image_150.png" /></figure>
# Public microarray expression data

<figure><img src="image_151.png" /></figure>

<figure><img src="image_152.png" /></figure>

<table><thead><tr><th colspan="2">Mous</th></tr><tr><th>Series ID</th><th>n</th></tr></thead><tbody><tr><td>total</td><td>920</td></tr></tbody></table>

<table><thead><tr><th colspan="2">Rat</th></tr><tr><th>Series ID</th><th>n</th></tr></thead><tbody><tr><td>total</td><td>620</td></tr></tbody></table>

<table><thead><tr><th colspan="2">Mous</th></tr><tr><th>Series ID</th><th>n</th></tr></thead><tbody><tr><td>total</td><td>248</td></tr></tbody></table>

<table><thead><tr><th colspan="2">Rat</th></tr><tr><th>Series ID</th><th>n</th></tr></thead><tbody><tr><td>total</td><td>1202</td></tr></tbody></table>

Affymetrix Mouse Genome 430 2.0

Affymetrix Rat Genome 230 2.0
Data normalization

Identification of variable probes

<figure><img src="image_153.png" /></figure>

A

before normalisation (mouse)

<figure><img src="image_154.png" /></figure>

B

before normalisation (rat)

<figure><img src="image_155.png" /></figure>

C

Lower quartile trimming (mouse)

<figure><img src="image_156.png" /></figure>

D

Lower quartile trimming (rat)

<figure><img src="image_157.png" /></figure>

E

Quantile normalisation (mouse)

<figure><img src="image_158.png" /></figure>

F

Quantile normalisation (rat)

<figure><img src="image_159.png" /></figure>

A

mouse

3777
41260

<figure><img src="image_160.png" /></figure>

B

rat

2116
28926

<figure><img src="image_161.png" /></figure>
<table><thead><tr><th>Species</th><th>Category</th><th>Term</th><th>Gene</th><th>FE</th><th>P-value</th></tr></thead><tbody><tr><td rowspan="8">Mouse</td><td rowspan="8">Reactome</td><td>Synthesis of (16-20)-hydroxyeicosatetraenoic acids (HETE)</td><td>11</td><td>4.78</td><td>4.29E-02</td></tr><tr><td>Activation of gene expression by SREBF (SREBP)</td><td>15</td><td>4.34</td><td>5.18E-03</td></tr><tr><td>Regulation of cholesterol biosynthesis by SREBP (SREBF)</td><td>17</td><td>3.94</td><td>4.36E-03</td></tr><tr><td>Cytochrome P450 - arranged by substrate type</td><td>27</td><td>2.72</td><td>7.78E-03</td></tr><tr><td>Phase 1 - Functionalization of compounds</td><td>37</td><td>2.55</td><td>7.58E-04</td></tr><tr><td>fatty acid metabolic process</td><td>52</td><td>2.26</td><td>2.95E-05</td></tr><tr><td>steroid metabolic process</td><td>50</td><td>2.18</td><td>1.31E-04</td></tr><tr><td rowspan="12">Rat</td><td rowspan="12">Reactome</td><td>Synthesis of bile acids and bile salts via 24-hydroxycholesterol</td><td>7</td><td>8.63</td><td>2.95E-02</td></tr><tr><td>Endosomal/Vacuolar pathway</td><td>10</td><td>7.93</td><td>1.15E-03</td></tr><tr><td>Striated Muscle Contraction</td><td>11</td><td>6.78</td><td>1.48E-03</td></tr><tr><td>ER-Phagosome pathway</td><td>10</td><td>6.53</td><td>6.29E-03</td></tr><tr><td>Activation of gene expression by SREBF (SREBP)</td><td>10</td><td>6.53</td><td>6.29E-03</td></tr><tr><td>Antigen Presentation: Folding, assembly and peptide loading of class I MHC</td><td>13</td><td>6.27</td><td>3.84E-04</td></tr><tr><td>Regulation of cholesterol biosynthesis by SREBP (SREBF)</td><td>10</td><td>5.55</td><td>2.51E-02</td></tr><tr><td>Biological oxidations</td><td>25</td><td>2.95</td><td>3.58E-03</td></tr><tr><td>Metabolism of lipids and lipoproteins</td><td>68</td><td>2.13</td><td>1.15E-05</td></tr><tr><td>GO slim BP response to biotic stimulus</td><td>12</td><td>4.16</td><td>1.12E-02</td></tr><tr><td>GO slim BP fatty acid metabolic process</td><td>22</td><td>2.52</td><td>2.52E-02</td></tr></tbody></table>
<figure><img src="image_162.png" /></figure>

Mouse Experiments

<figure><img src="image_163.png" /></figure>

Rat Experiments

<figure><img src="image_164.png" /></figure>

<figure><img src="image_165.png" /></figure>
SCHype hyper-graph
clustering tool

<figure><img src="image_166.png" /></figure>

<figure><img src="image_167.png" /><figcaption>A</figcaption></figure>

<figure><img src="image_168.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_169.png" /></figure>

NCBI

HomoloGene

<figure><img src="image_170.png" /></figure>
<figure><img src="image_171.png" /></figure>

<figure><img src="image_172.png" /></figure>

<figure><img src="image_173.png" /></figure>

Rat
Experiments

C

<figure><img src="image_174.png" /></figure>

<figure><img src="image_175.png" /></figure>

Mouse
Experiments

<figure><img src="image_176.png" /></figure>

<figure><img src="image_177.png" /></figure>

<figure><img src="image_178.png" /></figure>

<figure><img src="image_179.png" /></figure>

<figure><img src="image_180.png" /></figure>

<figure><img src="image_181.png" /></figure>

cluster 1

cluster 2

cluster 3

cluster 4
<table><thead><tr><th>Homology group</th><th>Species</th><th>Gene name</th><th>SCHype cluster</th></tr></thead><tbody><tr><td rowspan="3">137229</td><td>mouse</td><td>Anp32a</td><td>cluster 69</td></tr><tr><td>rat</td><td>Anp32a</td><td>✓</td></tr><tr><td>rat</td><td>LOC100909983</td><td>✓</td></tr><tr><td rowspan="3">68982</td><td>mouse</td><td>Ccnb1</td><td>cluster 7</td></tr><tr><td>mouse</td><td>Gm5593</td><td>cluster 30</td></tr><tr><td>rat</td><td>Ccnb1</td><td>✓</td></tr><tr><td rowspan="3">10699</td><td>mouse</td><td>Cd248</td><td>cluster 2</td></tr><tr><td>rat</td><td>Cd248</td><td>✓</td></tr><tr><td>rat</td><td>LOC100911932</td><td>✓</td></tr><tr><td rowspan="3">3938</td><td>mouse</td><td>LOC100911882</td><td>cluster 1</td></tr><tr><td>rat</td><td>Ppp1r3c</td><td>✓</td></tr><tr><td>rat</td><td>LOC100910671</td><td>✓</td></tr><tr><td rowspan="3">14108</td><td>mouse</td><td>Rasl10b</td><td>cluster 2</td></tr><tr><td>rat</td><td>Rasl10b</td><td>✓</td></tr><tr><td>rat</td><td>LOC100912246</td><td>✓</td></tr></tbody></table>

<figure><img src="image_182.png" /></figure>
<table><thead><tr><th>Homology group</th><th>Species</th><th>Gene name</th><th colspan="3">SCHype cluster</th></tr></thead><tbody><tr><td rowspan="3">128630</td><td>mouse</td><td>Ceacam1</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>mouse</td><td>Ceacam2</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>rat</td><td>Ceacam1</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td rowspan="3">11456</td><td>mouse</td><td>Elovl6</td><td>✓</td><td></td><td></td></tr><tr><td>rat</td><td>Elovl6</td><td>✓</td><td></td><td></td></tr><tr><td>rat</td><td>LOC102549542</td><td>✓</td><td></td><td></td></tr><tr><td rowspan="3">20277</td><td>mouse</td><td>Rrm2</td><td>✓</td><td></td><td></td></tr><tr><td>rat</td><td>Rrm2</td><td>✓</td><td></td><td></td></tr><tr><td>rat</td><td>LOC100359539</td><td>✓</td><td></td><td></td></tr><tr><td rowspan="3">55991</td><td>mouse</td><td>Tmed2</td><td>✓</td><td>✓</td><td></td></tr><tr><td>mouse</td><td>Gm21540</td><td>✓</td><td>✓</td><td></td></tr><tr><td>rat</td><td>Tmed2</td><td>✓</td><td>✓</td><td></td></tr><tr><td rowspan="3">11890</td><td>mouse</td><td>Tnks2</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>rat</td><td>LOC100910717</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>rat</td><td>Tnks2</td><td>✓</td><td>✓</td><td>✓</td></tr></tbody></table>
<table><thead><tr><th>Homology group</th><th>Species</th><th>Gene name</th><th>SCHype cluster</th></tr></thead><tbody><tr><td rowspan="5">117948</td><td>mouse</td><td>Cyp2c38</td><td>✓</td></tr><tr><td>mouse</td><td>Cyp2c29</td><td></td></tr><tr><td>mouse</td><td>Cyp2c39</td><td></td></tr><tr><td>rat</td><td>Cyp2c7</td><td>✓</td></tr><tr><td>rat</td><td>Cyp2c7</td><td>✓</td></tr><tr><td rowspan="7">104115</td><td>mouse</td><td>Hsd3b5</td><td>✓</td></tr><tr><td>mouse</td><td>Gm10681</td><td></td></tr><tr><td>mouse</td><td>Hsd3b4</td><td></td></tr><tr><td>mouse</td><td>Gm4450</td><td></td></tr><tr><td>rat</td><td>Hsd3b5</td><td>✓</td></tr><tr><td>rat</td><td>LOC100911116</td><td>✓</td></tr><tr><td>rat</td><td>LOC100911116</td><td>✓</td></tr><tr><td rowspan="5">137425</td><td>mouse</td><td>Lce3c</td><td>✓</td></tr><tr><td>rat</td><td>LOC100361951</td><td>✓</td></tr><tr><td>rat</td><td>LOC100911982</td><td>✓</td></tr><tr><td>rat</td><td>Lce3d</td><td></td></tr><tr><td>rat</td><td>Lce3d</td><td></td></tr><tr><td rowspan="7">129514</td><td>mouse</td><td>Rdh9</td><td>✓</td></tr><tr><td>mouse</td><td>Rdh1</td><td></td></tr><tr><td>mouse</td><td>Rdh16</td><td></td></tr><tr><td>mouse</td><td>Rdh19</td><td></td></tr><tr><td>mouse</td><td>BC089597</td><td></td></tr><tr><td>rat</td><td>Rdh16</td><td>✓</td></tr><tr><td>rat</td><td>LOC100365958</td><td>✓</td></tr><tr><td>rat</td><td>LOC100365958</td><td>✓</td></tr></tbody></table>

<figure><img src="image_183.png" /></figure>
<figure><img src="image_184.png" /></figure>

Conclusions & perspectives

* Provide evidence for resolving functional annotation transfer in 28 complex homology groups (>100 genes)

* Using free data!

* Different array, RNA-seq

* More tissues!

* More species!

* Still need functional annotation in at least one species
<figure><img src="image_185.png" /></figure>

<figure><img src="image_186.png" /></figure>

# Epigenomics and the human transcript diversity

## using Roadmap Epigenomics data

<figure><img src="image_187.png" /></figure>

ROADMAP

epigenomics
PROJECT

<figure><img src="image_188.png" /></figure>

INRA
SCIENCE & IMPACT

@G_Devailly

061

2017-12-19
<figure><img src="image_189.png" /></figure>

* Epigenetic marks role at TSS and enhancer is (quite) well characterized

* Putative roles in TES, exon recognition, cryptic TSS repression?

* Plenty of data generated by Roadmaps Epigenomics (+ENCODE)

* RNA-seq, WGBS, DNAse1, plenty of HisMod in > 30 cell types

<figure><img src="image_190.png" /></figure>

<figure><img src="image_191.png" /></figure>
<figure><img src="image_192.png" /></figure>

<figure><img src="image_193.png" /></figure>

Version 27 (January 2017 freeze, GRCh38) - Ensembl 90

General stats

Total No of Genes

58288

<figure><img src="image_194.png" /></figure>
# What's a gene?

<figure><img src="image_195.png" /></figure>

<figure><img src="image_196.png" /></figure>

<figure><img src="image_197.png" /></figure>
<figure><img src="image_198.png" /></figure>

< AC135721.4

AC060780.18 >

<figure><img src="image_199.png" /></figure>

<figure><img src="image_200.png" /></figure>

<figure><img src="image_201.png" /></figure>

<figure><img src="image_202.png" /></figure>

<figure><img src="image_203.png" /></figure>

<figure><img src="image_204.png" /></figure>

Default
GENCODE
'gene' TSS

What's a gene TSS?

<figure><img src="image_205.png" /></figure>

<figure><img src="image_206.png" /></figure>

<figure><img src="image_207.png" /></figure>

<figure><img src="image_208.png" /></figure>

INRA
SCIENCE & IMPACT

@G_Devailly
Which metric for DNA methylation?

<figure><img src="image_209.png" /></figure>

<figure><img src="image_210.png" /></figure>

CpG
density

mCpG
density

4
0.5
2

2
1
2

mCpG density main driver of methylation
reader?

<figure><img src="image_211.png" /></figure>
CpG
density

<figure><img src="image_212.png" /></figure>

<figure><img src="image_213.png" /></figure>

<figure><img src="image_214.png" /></figure>

CpG:111

<figure><img src="image_215.png" /></figure>

CpG:86

<figure><img src="image_216.png" /></figure>

CpG:49

<figure><img src="image_217.png" /></figure>

H1-
hESC

<figure><img src="image_218.png" /></figure>
CpG
density

mCpG
ratio

<figure><img src="image_219.png" /></figure>

H1-
hESC

<figure><img src="image_220.png" /></figure>
CpG
density

mCpG
ratio

mCpG
density

<figure><img src="image_221.png" /></figure>

H1-
hESC

<figure><img src="image_222.png" /></figure>
CpG
density

mCpG
ratio

mCpG
density

coverage

<figure><img src="image_223.png" /></figure>

H1-
hESC

<figure><img src="image_224.png" /></figure>
CpG
density
mCpG
ratio
mCpG
density
coverage

<figure><img src="image_225.png" /></figure>

H1-
hESC

<figure><img src="image_226.png" /></figure>
<figure><img src="image_227.png" /></figure>

<figure><img src="image_228.png" /></figure>
<figure><img src="image_229.png" /></figure>

<figure><img src="image_230.png" /></figure>
<figure><img src="image_231.png" /></figure>

<figure><img src="image_232.png" /></figure>

<figure><img src="image_233.png" /></figure>

<figure><img src="image_234.png" /></figure>

<figure><img src="image_235.png" /></figure>

<figure><img src="image_236.png" /></figure>

<figure><img src="image_237.png" /></figure>
H1-hESC

<figure><img src="image_238.png" /></figure>

<figure><img src="image_239.png" /></figure>

INRA
SCIENCE & IMPACT

@G_De
H1-hESC

<figure><img src="image_240.png" /></figure>

<figure><img src="image_241.png" /></figure>

INRA
SCIENCE & IMPACT

@G_De
* Features × marks × cell types × gene types

* Combinatorial explosion: >5000

<figure><img src="image_242.png" /><figcaption>Figure 1: Epigenetic marks at promoters and transcription level in H1 embryonic stem cells</figcaption></figure>

<figure><img src="image_243.png" /></figure>

INRA

SCIENCE & IMPACT
<figure><img src="image_244.png" /></figure>

GJA1 (ENSG00000152661)

<figure><img src="image_245.png" /></figure>

<figure><img src="image_246.png" /></figure>
<figure><img src="image_247.png" /></figure>

<figure><img src="image_248.png" /></figure>

<figure><img src="image_249.png" /></figure>
<figure><img src="image_250.png" /></figure>

Promoter DNA methylation

<figure><img src="image_251.png" /></figure>

one dot = one cell type

<figure><img src="image_252.png" /></figure>

<figure><img src="image_253.png" /></figure>

<figure><img src="image_254.png" /></figure>
<figure><img src="image_255.png" /></figure>

<figure><img src="image_256.png" /></figure>

<figure><img src="image_257.png" /></figure>

<figure><img src="image_258.png" /></figure>

<figure><img src="image_259.png" /></figure>

<figure><img src="image_260.png" /></figure>

<figure><img src="image_261.png" /></figure>

<figure><img src="image_262.png" /></figure>
<figure><img src="image_263.png" /></figure>

Epigenetic marks
at intron/exon
boundaries?

Histone methylation

log10(exon FPKM+1)

<figure><img src="image_264.png" /></figure>

<figure><img src="image_265.png" /></figure>

<figure><img src="image_266.png" /></figure>

H3K4me3

H3K36me3

<figure><img src="image_267.png" /></figure>

ChIP input

<figure><img src="image_268.png" /></figure>

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<figure><img src="image_274.png" /></figure>

Epigenetic marks
at intron/exon
boundaries?

Histone methylation

log10(exon FPKM+1)

<figure><img src="image_275.png" /></figure>

<figure><img src="image_276.png" /></figure>

<figure><img src="image_277.png" /></figure>

H3K4me3

H3K36me3

<figure><img src="image_278.png" /></figure>

<figure><img src="image_279.png" /></figure>

ChIP input

<figure><img src="image_280.png" /></figure>

<figure><img src="image_281.png" /></figure>

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Perspectives

* Epigenetic marks and alternative splicing?

* Gene by gene tool in webapp

* Polish, write and submit

<figure><img src="image_286.png" /></figure>
Thanks

<figure><img src="image_287.png" /></figure>

<figure><img src="image_288.png" /></figure>

ROSLIN

<figure><img src="image_289.png" /><figcaption>Anagha Jos</figcaption></figure>

<figure><img src="image_290.png" /><figcaption>Anna Mantsok</figcaption></figure>

<figure><img src="image_291.png" /><figcaption>Barry Horne</figcaption></figure>

<figure><img src="image_292.png" /><figcaption>Tom Michael</figcaption></figure>

<figure><img src="image_293.png" /><figcaption>Angeles Arzalluz-Luque</figcaption></figure>

<figure><img src="image_294.png" /><figcaption>Deepti Vipin</figcaption></figure>

<figure><img src="image_295.png" /><figcaption>Pia Francesca Loren Reyes</figcaption></figure>

<figure><img src="image_296.png" /></figure>

BBSRC

<figure><img src="image_297.png" /></figure>

CASCADE-FELLOWS

<figure><img src="image_298.png" /></figure>

<figure><img src="image_299.png" /></figure>
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GenEpi@GenPhySE

Epigenetics and
heritability

* missing heritability

* Improving livestock epigenome

Non-heritable
epigenetics

* Early prediction?

<figure><img src="image_302.png" /></figure>

FAANG
Functional Annotation of Animal Genomes

* Variant annotation

(physiology and
nutrition)

<figure><img src="image_303.png" /></figure>