Genomics and High Throughput Sequencing Technologies:Applications

Jim NoonanDepartment 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)

Cooper and Shendure, Nat Rev Genet 12:628 (2011)

Challenge:Interpreting genetic variation

Protein-sequence based

DNA-sequence based

Tools for identifying rare damaging mutations

Damages protein

Conserved

Cooper and Shendure, Nat Rev Genet 12:628 (2011)

All humans have rare damaging mutations

Genome reduction: Exome sequencing

Bamshad et al. Nat Rev Genet 12:745 (2011)

De novo mutation

• Likely to have functional effect• Recurrence in independent affected individuals• Absence in controls• Reveal critical pathways in disease

Screen unrelated trios for recurrence

Finding disease-causing rare variants by exome sequencing

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 parallelRNA 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

Normalization methods:Reads per kilobase of feature length per million mapped reads (RPKM)

RNA-seq reads mapped to reference

• 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.

Wang et al. Nat Rev Genet 10:57 (2009)

What depth of sequencing is required to characterize a transcriptome?

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)

Map reads to genome

Map remaining reads to known splice junctions

Composite gene model approach

•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

• General transcription machinery

• Transcription factors

• Modifications to histone tails

• Methylated DNA

Noonan and McCallion, Ann Rev Genomics Hum Genet 11:1 (2010)

Rationale: identifying regulatory elements in genomes

ChIP-seq peak calling

ChIP-seq is an enrichment methodRequires a statistical framework for determining the significance of enrichment

ChIP-seq ‘peaks’ are regions of enriched read density relative to an input controlInput = sonicated chromatin collected prior to immunoprecipitation

There are many ChIP-seq peak calling methods

Wilbanks and Facciotti PLoS ONE 5:e11471 (2010)

Zhou et al. Nat Rev Genet 12:7 (2011)

The histone code

Mapping and analysis of chromatin state dynamics in nine human cell types

Ernst et al., Nature 473:43 (2011)

Cell types:•H1 ESC•K562 (erythrocyte derived)•GM12878 (B-lymphoblastoid)•HepG2 (hepatocellular carcinoma)•HUVEC (umbilical vein endothelium)•HSMM (skeletal muscle myoblasts)•NHLF (lung fibroblast)•NHEK (epidermal keratinocytes)•HMEC (mammary epithelium)

Marks:•H3K4me3 (promoter/enhancer)•H3K4me2 (promoter/enhancer)•H3K4me1 (enhancer)•H3K9ac (promoter/enhancer)•H3K27ac (promoter/enhancer)•H3K36me3 (transcribed regions)•H4K20me1 (transcribed regions)•H3K27me3 (Polycomb repression)•CTCF

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)

• Annotation based on nearest TSS

Functions associated with putative promoter and enhancer states

ChIP-seq: enhancer identification in vivo

•p300 = enhancer-associated factor

Visel et al. Nature 457:854 (2009)

•p300 binding = ~90% predictive of enhancer activity

Myers, PLoS Biol 9:e1001046 (2011)

Systematic experimental annotation of regulatory functions

http://genome.ucsc.edu/ENCODE/

The ENCODE Project

http://www.roadmapepigenomics.org/

The NIH Roadmap Epigenomics Project

Myers, PLoS Biol 9:e1001046 (2011)

ENCODE cell lines

http://genome.ucsc.edu/ENCODE/

ENCODE Project data access

Genome Browser interface and data types

Genome Viewer

Categories of data: displayed as tracksDiscrete 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