Download ChIP-seq

Genomics and High Throughput Sequencing Technologies:
Applications
Jim Noonan
Department of Genetics
Outline
Personal genome sequencing
•Rationale: understanding human disease
•Variant discovery and interpretation
•Genome reduction strategies (exome sequencing)
Functional analysis of biological systems using sequencing
•Transcriptome analysis: RNA-seq
•Regulatory element discovery: ChIP-seq
•Chromatin state profiling and the ‘histone code’
•Large-scale efforts: ENCODE and the NIH Epigenome Roadmap
Whole genome sequencing: 1000 Genomes
Nature 467:1061 (2010)
The genetic architecture of human disease
State, MW. Neuron 68:254 (2010)
Challenge:
Interpreting genetic variation
Cooper and Shendure, Nat Rev Genet 12:628 (2011)
Tools for identifying rare damaging mutations
Protein-sequence based
DNA-sequence based
All humans have rare damaging mutations
Damages protein
Conserved
Cooper and Shendure, Nat Rev Genet 12:628 (2011)
Genome reduction: Exome sequencing
Bamshad et al. Nat Rev Genet 12:745 (2011)
Finding disease-causing rare variants by exome sequencing
De novo mutation
•
•
•
•
Screen unrelated trios for recurrence
Likely to have functional effect
Recurrence in independent affected individuals
Absence in controls
Reveal critical pathways in disease
Sanders et al., Nature 485:237 (2012)
Outline
Personal genome sequencing
•Rationale: understanding human disease
•Variant discovery and interpretation
•Genome reduction strategies (exome sequencing)
•Challenges to de novo genome assembly using short reads
Functional analysis of biological systems using sequencing
•Transcriptome analysis: RNA-seq
•Regulatory element discovery: ChIP-seq
•Chromatin state profiling and the ‘histone code’
•Large-scale efforts: ENCODE and the NIH Epigenome Roadmap
mRNA-seq
workflow
Martin and Wang Nat Rev Genet 12:671 (2011)
Wang et al. Nat Rev Genet 10:57 (2009)
Gene expression profiling by massively parallel
RNA sequencing (RNA-seq)
Mapping RNA-seq reads and quantifying transcripts
Quantifying gene expression by RNA-seq
Use existing gene annotation:
•
•
•
•
•
Align to genome plus annotated splices
Depends on high-quality gene annotation
Which annotation to use: RefSeq, GENCODE, UCSC?
Isoform quantification?
Identifying novel transcripts?
Reference-guided alignments:
• Align to genome sequence
• Infer splice events from reads
• Allows transcriptome analyses of genomes with poor
gene annotation
De novo transcript assembly:
• Assemble transcripts directly from reads
• Allows transcriptome analyses of species without
reference genomes
RNA-seq reads mapped to reference
Normalization methods:
Reads per kilobase of feature length per million mapped reads (RPKM)
•
•
•
What is a “feature?”
What about genomes with poor genome annotation?
What about species with no sequenced genome?
For a detailed comparison of normalization methods, see Bullard et al. BMC Bioinformatics 11:94.
What depth of sequencing is required to characterize a transcriptome?
Wang et al. Nat Rev Genet 10:57 (2009)
Considerations
Gene length:
• Long genes are detected before short genes
Expression level:
• High expressors are detected before low expressors
Complexity of the transcriptome:
• Tissues with many cell types require more sequencing
Feature type
• Composite gene models
• Common isoforms
• Rare isoforms
Detection vs. quantification
• Obtaining confident expression level estimates (e.g.,
“stable” RPKMs) requires greater coverage
Pervasive alternative splicing in humans
Wang et al. Nature 456:470 (2008)
Composite gene model approach
Map reads to genome
Map remaining reads to
known splice junctions
• Requires good gene models
• Isoforms are ignored
• Which annotation to use: RefSeq, GENCODE, UCSC?
Strategies for transcript assembly
Garber et al. Nat Methods 8:469 (2011)
ChIP-seq
• Transcription factors
• General transcription machinery
• Modifications to histone tails
• Methylated DNA
Rationale: identifying regulatory elements in genomes
Noonan and McCallion, Ann Rev Genomics Hum Genet 11:1 (2010)
ChIP-seq peak calling
ChIP-seq is an enrichment method
Requires a statistical framework for determining the significance of enrichment
ChIP-seq ‘peaks’ are regions of enriched read density relative to an input control
Input = sonicated chromatin collected prior to immunoprecipitation
There are many ChIP-seq peak calling methods
Wilbanks and Facciotti PLoS ONE 5:e11471 (2010)
The histone code
Zhou et al. Nat Rev Genet 12:7
Mapping and analysis of chromatin state dynamics in nine human
cell types
Cell types:
Marks:
•
H1 ESC
•
H3K4me3 (promoter/enhancer)
•
K562 (erythrocyte derived)
•
H3K4me2 (promoter/enhancer)
•
GM12878 (B-lymphoblastoid)
•
H3K4me1 (enhancer)
•
HepG2 (hepatocellular carcinoma)
•
H3K9ac (promoter/enhancer)
•
HUVEC (umbilical vein endothelium)
•
H3K27ac (promoter/enhancer)
•
HSMM (skeletal muscle myoblasts)
•
H3K36me3 (transcribed regions)
•
NHLF (lung fibroblast)
•
H4K20me1 (transcribed regions)
•
NHEK (epidermal keratinocytes)
•
H3K27me3 (Polycomb repression)
•
HMEC (mammary epithelium)
•
CTCF
Ernst et al., Nature 473:43 (2011)
Mapping and analysis of chromatin state dynamics in nine human
cell types
Ernst et al., Nature 473:43 (2011)
Chromatin state dynamics at WLS
Ernst et al., Nature 473:43 (2011)
Functions associated with putative promoter and enhancer states
•
Annotation based on nearest TSS
ChIP-seq: enhancer
identification in vivo
Visel et al. Nature 457:854 (2009)
•p300 = enhancer-associated factor
•p300 binding = ~90% predictive of
enhancer activity
Systematic experimental annotation of regulatory functions
Myers, PLoS Biol 9:e1001046 (2011)
The ENCODE Project
http://genome.ucsc.edu/ENCODE/
The NIH Roadmap Epigenomics Project
http://www.roadmapepigenomics.org/
ENCODE cell lines
Myers, PLoS Biol 9:e1001046 (2011)
ENCODE Project data access
http://genome.ucsc.edu/ENCODE/
Genome Browser interface and data types
Genome Viewer
Categories of data: displayed as tracks
Discrete intervals (genes) or continuous (transcription)
Hyperlinks and pulldown tabs for individual tracks
•
•
Go to track description page
Hide or show data in genome viewer
Some tracks include multiple datasets (‘subtracks’)
•Go to track description page to select
ENCODE Transcription track
Display options
Subtracks
Conclusions
Personal genomics is becoming a reality
•Genome sequencing will be a routine diagnostic tool
•$5,000 to sequence single genome; current cost for clinical resequencing of single genes
•Your genome will be sequenced
•Long-read sequencing will solve de novo assembly issues
•Data analysis and interpretation
RNA-seq and ChIP-seq
•Identifying genes and annotating regulatory function within and among genomes
•Computational issues: data normalization, peak calling, differential
expression and binding
•Large-scale studies revealing regulatory architecture of human & model genomes