Genomics experimental-methods

Genomics: Experimental methods

Slides available www.bioinformatics.be

Lab for Bioinformatics and computational genomics

10 “genome hackers” mostly engineers (statistics)

42 scientiststechnicians, geneticists, clinicians

>100 people hardware engineers,

mathematicians, molecular biologists

Overview

Personalized Medicine,

Biomarkers …

… Molecular Profiling

First Generation Molecular Profiling

Next Generation Molecular Profiling

Next Generation Epigenetic Profiling

Concluding Remarks

Overview


Biomarkers …





Concluding Remarks

Personalized Medicine

• The use of diagnostic tests (aka biomarkers) to identify in advance which patients are likely to respond well to a therapy

• The benefits of this approach are to– avoid adverse drug reactions– improve efficacy– adjust the dose to suit the patient– differentiate a product in a competitive market– meet future legal or regulatory requirements

• Potential uses of biomarkers– Risk assessment– Initial/early detection– Prognosis– Prediction/therapy selection– Response assessment– Monitoring for recurrence

Biomarker

First used in 1971 … An objective and « predictive » measure … at the molecular level … of normal and pathogenic processes and responses to therapeutic interventions

Characteristic that is objectively measured and evaluated as an indicator of normal biologic or pathogenic processes or pharmacologic response to a drug

A biomarker is valid if:– It can be measured in a test system with well

established performance characteristics – Evidence for its clinical significance has been

established

Rationale 1:Why now ? Regulatory path becoming more clear

There is more at stake than efficient drug development. FDA « critical path initiative » Pharmacogenomics guideline

Biomarkers are the foundation of « evidence based medicine » - who should be treated, how and with what.

Without Biomarkers advances in targeted therapy will be limited and treatment remain largely emperical. It is imperative that Biomarker development be accelarated along with therapeutics

Why now ?

First and maturing second generation molecular profiling methodologies allow to stratify clinical trial participants to include those most likely to benefit from the drug candidate—and exclude those who likely will not—pharmacogenomics-based

Clinical trials should attain more specific results with smaller numbers of patients. Smaller numbers mean fewer costs (factor 2-10)

An additional benefit for trial participants and internal review boards (IRBs) is that stratification, given the correct biomarker, may reduce or eliminate adverse events.

Molecular Profiling

The study of specific patterns (fingerprints) of proteins, DNA, and/or mRNA and how these patterns correlate with an individual's physical characteristics or symptoms of disease.

Generic Health advice

• Exercise (Hypertrophic Cardiomyopathy)• Drink your milk (MCM6 Lactose intolarance)• Eat your green beans (glucose-6-phosphate

dehydrogenase Deficiency)• & your grains (HLA-DQ2 – Celiac disease)• & your iron (HFE - Hemochromatosis)• Get more rest (HLA-DR2 - Narcolepsy)

Generic Health advice (UNLESS)

• Exercise (Hypertrophic Cardiomyopathy)• Drink your milk (MCM6 Lactose intolarance)• Eat your green beans (glucose-6-phosphate



• Exercise (Hypertrophic Cardiomyopathy)• Drink your milk (MCM6 Lactose intolerance)• Eat your green beans (glucose-6-phosphate



• Exercise (Hypertrophic Cardiomyopathy)• Drink your milk (MCM6 Lactose intolerance)• Eat your green beans (glucose-6-phosphate


EGFR based therapy in mCRC

Overview


Biomarkers …





Concluding Remarks

Before molecular profiling …




• Flow cytometry correlates surface markers, cell size and other parameters

• Circulating tumor cell assays (CTC’s) quantitate the number of tumor cells in the peripheral blood.

• Exosomes are 30-90 nm vesicles secreted by a wide range of mammalian cell types.

• Immunohistochemistry (IHC) measures protein expression, usually on the cell surface.


• Gene sequencing for mutation detection

• Microarray for m-RNA message detection • RT-PCR for gene expression

• FISH analysis for gene copy number • Comparative Genome Hybridization (CGH) for

gene copy number

Basics of the “old” technology

• Clone the DNA.• Generate a ladder of labeled (colored)

molecules that are different by 1 nucleotide.• Separate mixture on some matrix.• Detect fluorochrome by laser.• Interpret peaks as string of DNA.• Strings are 500 to 1,000 letters long• 1 machine generates 57,000 nucleotides/run• Assemble all strings into a genome.

Genetic Variation Among People

0.1% difference among people

GATTTAGATCGCGATAGAGGATTTAGATCTCGATAGAG

Single nucleotide polymorphisms(SNPs)

The genome fits as an e-mail attachment





gene copy number

mRNA Expression Microarray





gene copy number

Overview


Biomarkers …





Concluding Remarks

Basics of the “new” technology

• Get DNA.• Attach it to something.• Extend and amplify signal with some color

scheme.• Detect fluorochrome by microscopy.• Interpret series of spots as short strings of

DNA.• Strings are 30-300 letters long• Multiple images are interpreted as 0.4 to 1.2

GB/run (1,200,000,000 letters/day). • Map or align strings to one or many genome.

Next Generation Technologies

• Roche (454)–Emulsion PCR–Polymerase–Natural Nucleotides

• 100-500 Mb for 5-15k –1% error rate–Homopolymers

One additional insight ...

Read Length is Not As Important For Resequencing

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

8 10 12 14 16 18 20

Length of K-mer Reads (bp)

% o

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aire

d K

-mer

s w

ith

Un

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ely

Ass

ign

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Lo

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E.COLI

HUMAN

Jay Shendure

Short Read Techologies

• Illumina GA (HiSeq, MySeq)

• ABI SOLID

Other second generation technology: (ABI) SOLID

So what ?

Second generation DNA/RNA profiling

Second Generation DNA profiling

• Enrichment Sequencing• ChIP-Seq (Chromosome

Immunoprecipitation)• A substitute for ChIP-chip• Eg. to find the binding sequence of

proteins (TFBS)

Paired End Reads are Important!

Repetitive DNAUnique DNA

Single read maps to multiple positions

Read 1 Read 2

Known Distance

Paired End Reads are Important!

Repetitive DNAUnique DNA

Single read maps to multiple positions

Read 1 Read 2

Known Distance


• Exome Sequencing (aka known as targeted exome capture) is an efficient strategy to selectively sequence the coding regions of the genome to identify novel genes associated with rare and common disorders.

• 160K exons

http://en.wikipedia.org/wiki/DNA_sequencing

http://en.wikipedia.org/wiki/Genome

http://en.wikipedia.org/wiki/Genes





Bioinformatics tools

Bioinformatics tools

Con

tent

s-S

ched

ule

Besides the 6000 protein coding-genes …

140 ribosomal RNA genes275 transfer RNA gnes40 small nuclear RNA genes>100 small nucleolar genes

Function of RNA genes

pRNA in 29 rotary packaging motor (Simpson et el. Nature 408:745-750,2000)Cartilage-hair hypoplasmia mapped to an RNA (Ridanpoa et al. Cell 104:195-203,2001)The human Prader-Willi ciritical region (Cavaille et al. PNAS 97:14035-7, 2000)

Second Generation RNA profiling

RNA genes can be hard to detects

UGAGGUAGUAGGUUGUAUAGU

C.elegans let-27; 21 nt (Pasquinelli et al. Nature 408:86-89,2000)

Often smallSometimes multicopy and redundantOften not polyadenylated (not represented in ESTs)Immune to frameshift and nonsense mutationsNo open reading frame, no codon biasOften evolving rapidly in primary sequence


Although details of the methods vary, the concept behind RNA-seq is simple:

• isolate all mRNA• convert to cDNA using reverse transcriptase• sequence the cDNA• map sequences to the genome

The more times a given sequence is detected, the more abundantly transcribed it is. If enough sequences are generated, a comprehensive and quantitative view of the entire transcriptome of an organism or tissue can be obtained.


• Comparing to microarray– Microarray

• Closed technology: Prior knowledge required• Affected by pseudo-genes (homologous of real genes)• Low sensitivity

– RNA-Seq• Open technology: No prior knowledge required• Not affected by pseudo-genes because exact

sequence is measured• Other information could be yielded (SNP, Alternative

splicing)


ncRNAs in human genome

tRNA 60018S rRNA 2005.8S rRNA 20028S rRNA 2005S rRNA 200snoRNA 300miRNA 250U1 40U2 30U4 30U5 30U6 20U4atac 5U6atac 5U11 5U12 5

SRP RNA 1

RNase P RNA 1

Telomerase RNA 1

RNase MRP 1

Y RNA 5

Vault 4

7SK RNA 1

Xist1

H191

BIC1

Antisense RNAs 1000s?

Cis reg regions 100s?

Others ?

Mapping Structural Variation in Humans

- Thought to be Common 12% of the genome (Redon et al. 2006)

- Likely involved in phenotype variation and disease

- Until recently most methods fordetection were low resolution (>50 kb)

CNVs

>1 kb segments

Size Distribution of CNV in a Human Genome

Next next generation sequencing

Third generation sequencing

Now sequencing

Ultra-low-cost SINGLE molecule sequencing

Pacific Biosciences: A Third Generation Sequencing Technology

Eid et al 2008

Complete genomics

Nanopore Sequencing

Second Generation Protein profiling

• Proteomics MS-MS-based exclusively in discovery mode

• Automate diagnostics assay generation (next generation proteomics)• Aptamers as alternative to antibodies• ImmunoPCR

MS/MS identification pipeline

pipeline overview

Bonanza

Bonanza + IggyPep

Goaldefine PTMs profile

prior to database

search

Goalmulti-tiered

database search

Goalfilter

dataset prior to

database search

Second Generation Protein profiling

• Proteomics MS-MS-based exclusively in discovery mode

• Automate diagnostics assay generation (next generation proteomics)• Aptamers as alternative to antibodies• ImmunoPCR

Overview


Biomarkers …





Concluding Remarks

CONFIDENTIAL# samples

# markers

MethylCap_Seq

Deep_Seq

Genome-wide methylation …. by next generation sequencing

Discovery Verification Validation

3 000 000

5

6 000

50EpiHealth

<50 only models

and fresh frozen

> 50

CONFIDENTIALSequencing

DeepSeq

Molecular Unification

107 106 105 104 103 102 101 1108109

Full genome bp

Whole-genomeBisulphite seq

RUO Clinical

EPI

GENETIC

Whole-genomesequencing

Enrichment seq(MBD, RRBS)

Enrichment seq(Exome)

Probes(450-27K)

Enrichment Targeted Panels

Enrichment Targeted Panels

UltraDeep

Overview


Biomarkers …





Concluding Remarks

Math

Informatics

Bioinformatics, a life science discipline …

(Molecular)Biology

Math

Informatics


Theoretical Biology

Computational Biology

(Molecular)Biology

Computer Science

Math

Informatics


Theoretical Biology


(Molecular)Biology

Computer Science

Bioinformatics

Math

Informatics

Bioinformatics, a life science discipline … management of expectations

Theoretical Biology


(Molecular)Biology

Computer Science

Bioinformatics

Interface Design

AI, Image Analysisstructure prediction (HTX)

Sequence Analysis

Expert Annotation

NPDatamining

Math

Informatics

Bioinformatics, a life science discipline … management of expectations

Theoretical Biology


(Molecular)Biology

Computer Science

BioinformaticsDiscovery Informatics – Computational Genomics

Interface Design

AI, Image Analysisstructure prediction (HTX)

Sequence Analysis

Expert Annotation

NPDatamining

Translational Medicine: An inconvenient truth

• 1% of genome codes for proteins, however more than 90% is transcribed

• Less than 10% of protein experimentally measured can be “explained” from the genome

• 1 genome ? Structural variation• > 200 Epigenomes ??

• Space/time continuum …

Translational Medicine: An inconvenient truth

• 1% of genome codes for proteins, however more than 90% is transcribed

• Less than 10% of protein experimentally measured can be “explained” from the genome

• 1 genome ? Structural variation• > 200 Epigenomes …

• “space/time” continuum

Epigenetic (meta)information = stem cells

Cellular programming

Cellular reprogramming

Tumor

Epigenetically altered, self-renewing cancer stem cells

Tumor Development and Growth

Gene-specificEpigeneticreprogramming

Cellular reprogramming

Wobblebase Mission

provide tools to both specialists (researchers, bioinformaticians, health care providers) and individual consumers that unlock the power of genomic data to the USER

enable personalized genomics today by simplifying the way we organize, visualize and manage genomic data.

PGM: Personal Genomics Manifesto

Everybody who wants to get his genome sequenced has the human right to do so. No third party can own your genetic data, your genetic data is exclusively yours.

Nobody can be forced to get his genome analyzed or to reveal his genome to a third party.

Your genome should allways be treated as confidential, private information.

People should be advised not to share their identity AND their entire genome on a public forum.

People should be advised to use secure technologies that allow to maximally protect phenotypic and/or genotype data.

People should be able to actively explore, manage and get updated interpretation on their genomic data.

• change the diagnostic/healthcare industry forever by setting a new standard and empowering the user

Wobblebase Mission

Choosing the Red Pill

The Technical Feasibility Argument

The Quality Argument

The Price Argument

The Logistics around the sample on howto manage the data Argument

The Ethical debate

The Privacy/Security concern

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Genomics experimental-methods