A Brief Introduction to ChIP-Seq

Monica Britton, Ph.D. Sr. Bioinformatics Analyst

March 2015 Workshop

A Word of Apology

This lecture would normally be given by one of the UC Davis ChIP-Seq experts. Unfortunately, this week they are traveling. So, this lecture will mostly cover the basics. But, we will keep track of all questions asked and post the answers as soon as possible!

Functional Elements in the Genome

www.encodeproject.org

Chromatin Structure Determines Gene Status

Transcription factors cannot gain access

gene A gene B

Closed Chromatin

gene A gene B

Promoter and enhancer binding sites accessible

Open chromatin

DNA and Histone Modifications Create an Epigenome

MBD

protein

MBD

protein

meC

protein that creates

and/or binds to meC modified

histone

protein that modifies and/or

binds to a modified histone

Coordinated Efforts to Decipher Epigenomes

• There is a wealth of publicly available data. Don’t be afraid to dig!

• NIH Roadmap Epigenomics Mapping Consortium http://www.roadmapepigenomics.org/

• Encyclopedia of DNA Elements Consortium (ENCODE)

• ENCODE data limited to cell-types at http://genome.ucsc.edu/index.html

ChIP (Before the Seq)

www.whatisepigenetics.com

Comparison of Experimental Protocols

Furey, 2012 Nat. Rev. Gen. 13:840

Key Steps in a ChIP Assay

ChIP DNA Input DNA

Cross-linking

Fragmentation

Immunoprecipitation

DNA purification

Henny’s Parameters for a Successful ChIP

• Cross-linking

• Antibody, antibody, antibody – ChIP-grade antibodies commercially available

– Previously published

– Ideally antibody works in IP

• Amount of starting material

• Chromatin fragmentation – Size does matter

– Just right: not too big and not too small

• IP-matrix – Magnetic beads are easy to handle

• Stringency of washes

Sonication Conditions Vary Between Different Cell Types

Fragments too big:

Reduced signal to noise ratio

in ChIP-seq

Oversonication:

Fragmentation biased towards

promoter regions leading to

ChIP-seq enrichments at

promoters in ChIP AND

control (input) sample

ChIP-Seq QC Check Points

O’Geen et al., 2011, Meth. in Mol. Biol , 791:265

ENCODE Recommendations for Antibody Characterization

Landt, et al., 2012 Genome Res. 22:1813

ENCODE Guidelines For Controls and Replicates

• Controls are Important! – Necessary to avoid non-uniform background (sonication, etc.)

– Many cell lines have aneuploidy (genome size, copy number)

– “Input” controls – similar prep, but no ChIP.

– “IgG” – IP without the specific antibody

– If amplification is done, must be done on all samples, including controls (and complexity needs to be evaluated after sequencing to ensure peaks aren’t due to PCR artifacts)

• Replicates – Minimum of two biological replicates.

– The number of mapped reads and identified targets should be within 2 fold between replicates

– 80% of the top 40% of targets from one replicate should overlap the list of targets from the other replicate. OR

– More than 75% of targets should be in common between each replicate

ENCODE Guidelines for Sequencing Depth

• The number of targets that can be identified varies substantially between cell types and experiments

• Depends on the TF, antibody, and peak-calling algorithm.

• Mammalian cells: – 10M uniquely mapped reads per replicate for point-source peaks

(increased from previous requirement of 3M reads)

– 20M uniquely mapped reads per replicate for broad-source peaks

• Other organisms need fewer reads (insects, yeast, etc.)

• Each replicate should be sequenced to similar depth. Controls to similar or greater depth.

• Complexity is important – low complexity libraries indicate PCR over-amplification, resulting in high false-positive rate (and failed experiment).

• FRiP (Fraction of Reads in Peaks) should be >1% of reads

Called Peaks Increase With Sequencing Depth

Landt, et al., 2012 Genome Res. 22:1813

From Binding Site to Sequence to Peak

Short sequences are generated from each DNA molecule.

When mapped, a tag distribution is seen around a stable binding site.

Cross-correlation is calculated for the distance between positive- and negative-strand peaks

Kharchenko et al., 2008 Nat. Biotech. 26:1351

Library Complexity and Cross-Correlation

Landt, et al., 2012 Genome Res. 22:1813

Characteristics of Good ChIP-Seq Experiments

Successful experiments will identify many thousands of peaks for most TFs. These peaks will also have good cross-correlation

ChIP-Seq Peak Characteristics

Two cross-correlation peaks are usually observed: one corresponding to the read length (‘‘phantom’’ peak) and the other correspoding to the average fragment length of the library.

The absolute and relative height of the two peaks can help determine the success of a ChIP-seq experiment.

In a high-quality IP, the ChIP peak is much higher than the ‘‘phantom’’ peak, while very small or no ChIP peaks are seen in failed experiments.

Comparison of Good vs. Poor ChIP-Seq Experiments

A (Non-Exhaustive) List of Useful References

ENCODE and modENCODE Guidelines For Experiments Generating ChIP, DNase, FAIRE, and DNA Methylation Genome Wide Location Data Version 2.0, July 20, 2011 (www.encodeproject.org)

ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Landt et al., Genome Research, 2012, 22:1813.

ChIP–seq and beyond: new and improved methodologies to detect and characterize protein–DNA interactions. Furey, Nat. Rev. Genetics 2012, 13:840

Using ChIP-Seq Technology to Generate High-Resolution Profiles of Histone Modifications. O’Geen et al., 2011, Methods in Molecular Biology , 791:265

Design and analysis of ChIP-seq experiments for DNA-binding proteins. Kharchenko et al., 2008 Nature Biotechnology 26:1351

Global analysis of ZNF217 chromatin

occupancy in the breast cancer cell genome

reveals an association with ERalpha

Frietze S, O'Geen H, Littlepage LE, Simion C,

Sweeney CA, Farnham PJ, Krig SR.

BMC Genomics. 2014 Jun 24;15:520.

The following slides are from Heny O’Geen’s lecture

at our December Workshop …..

• The 20q13 locus is one of twenty known “hot

spots” found in breast cancer.

• 20q13 is clinically associated with

aggressive disease and poor prognosis.

• ZNF217: multiple zinc finger motifs,

suggested a DNA-binding protein and putative

oncogene

• Hypothesis: ZNF217 overexpression gives a

selective advantage by interfering with

regulation of : cell growth, cell death,

differentiation or DNA repair.

The 20q13.2 locus is amplified

in 20-30% of breast tumors

Collins et al., PNAS 1998

Genome-wide Mapping of ZNF217 in

MCF7 cells to elucidate function

ENCODE data

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ZNF217 binding (33%) at intronic

enhancer regions

Motif analysis: ZNF217 binding localizes to GATA, FOXA1

and ER (endocrine response) motifs

GREAT: Pathway Commons Category

Gene Ontology supports ZNF217

co-binding with ER and FOXA1

Heatmap clustering of ENCODE

ChIP-seq datasets

ZNF217 co-binding with ER, GATA3

and FOXA1

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Mohammed et al., Cell Rep 2013

ER co-immunopreciptation identifies

108 ER-associated proteins

ZNF217

ERα

Actin

ZNF and ER protein correlate in

breast tumors

ERE

ER

CtBP

TSS

ZNF217

RNA Pol II

FoxA1

Chromatin Looping

>5 kb

Model for chromatin looping. Change in the three dimensional

structure between the distal ER-bound enhancer region and the proximal

promoter allows protein interactions with RNA pol II at the TSS.

Hypothesis: ZNF217 regulates chromatin

looping to assist ER gene regulation

Gene expression changes in

ZNF217 knock-down cells

Gene expression changes in ZNF217

knock-down cells

Gene Set Enrichment Analysis (GSEA) compares differences between two biological states

Gene Ontology on differentially expressed

genes co-bound by ZNF217 and ER

Clinical Conclusions:

ZNF217 overexpression is a biomarker for worse prognosis in

breast cancer patients:

- Prognostic: Identify a subset of ZNF217-overexpressing tumors with

worse prognosis in the ER+ Luminal A subtype

- Predictive: Identify tumors with de novo anti-hormone resistance or

more likely to relapse with anti-hormone treatment

Hypothesis: ZNF217 overexpression in ER+ breast cancer leads to aberrant

ER gene regulation

- Knowledge of the ZNF217 role in ER biology will have clinical impact

- ZNF217 overexpression alters ER co-regulator activity at the chromatin level