High Throughput Sequencing

Agenda

• Introduction to sequencing• Applications• Bioinformatics analysis pipelines• What should you ask yourself before

planning the experiment

Introduction to sequencing

What is sequencing?

Finding the sequence of a DNA/ RNA molecule

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What can we sequence?

Sanger sequencing

• Up to 1,000 bases molecule• One molecule at a time

• Widely used from 1970-2000• First human genome draft was

based on Sanger sequencing• Still in use for single molecules

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High Throughput SequencingNext Generation Sequencing (NGS) / Massively parallel sequencing

• Sequencing millions of molecules in parallel• Do not need prior knowledge of what you’re

sequencing

Platform Read length No. of reads per run

454 sequencing Up to 1,000 bp ~1 M (1 million)SOLiD 50-75 bp ~1 G (1 billion)HiSeq 100-150 bp ~0.5 G

We will discuss Illumina’s platform only

Sequencing Workflow

1. Extract tissue cells2. Extract DNA/RNA from cells3. Sample preparation for sequencing4. Sequencing5. Bioinformatics analysis

Why is it important to understand the “wet lab” part?

Sample Prep

Random shearing of the DNA

Adding adaptors and barcodes

Size selection Amplification Sequencing

Sequencing process

Sequencing process

Leave only sequences from one direction

Sequencing process

Sequencing process

Sequencing process

Applications

• Resequencing – sequencing the genome of an

organism with a known genome

• Exome sequencing / Targeted sequencing –

sequencing only selected regions from the genome

• De-novo sequencing– sequencing the genome of

an organism with a unknown genome

DNA sequencing

Sequencing of mRNA extracted form the cell to get an estimate of expression levels of genes.

RNA-Seq

Counting vs. Reading

RNA-Seq vs. DNA-sequencing

Sequencing the regions in the genome to which a protein binds to.

ChIP-Seq

Basic concepts

Insert – the DNA fragment that is used for sequencing. Read – the part of the insert that is sequenced. Single Read (SR) – a sequencing procedure by which the insert is sequenced from one end only. Paired End (PE) – a sequencing procedure by which the insert is sequenced from both ends.

Bioinformatics Analysis Pipelines

Demultiplexing

LaneUnknown inserts

Reference Genome

SampleMapping

Demultiplexing

Example of mapping parameters:• Number of mismatches per read • Scores for mismatch or gaps

Mapping parameters affect the rest of the analysis

Removing duplicates and non-unique mappings

Mapping

Demultiplexing

Reference Genome

Reference Genome

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Resequencing/ Exome Pipeline

Coverage profile and variant calling

Removing duplicates and non-unique mappings

Mapping

Demultiplexing

Reference Genome

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Coverage profile and variant calling

Removing duplicates and non-unique mappings

Mapping

Demultiplexing

Variant filtering

Reference Genome

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Reference Genome

…ACTTCGTCGAAAGG…

Frequency >= 20%

Coverage >= 5

Variant calling

Removing duplicates and non-unique mappings

Mapping

Demultiplexing

Variant filtering

Genes and known variants

Reference Genome

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GA

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Resequencing results

Coverage profile and variant calling

Removing duplicates and non-unique mappings

Mapping

Demultiplexing

Variant filtering

Genes and known variants

Finding suspicious variants

Recessive disease:1. Variant not in known databases2. Homozygous variant shared by all

affected individuals3. Same variant appears in healthy parents

at heterozygous state4. Healthy brothers can be heterozygous

to the same variant

Dominant disease:1. Variant not in known databases2. Heterozygous variant shared by all

affected individuals3. The variant doesn’t appear in healthy

individuals

1 Table per project

1 Table per sample

Example for further analysis

Coverage profile and variant calling

Removing duplicates and non-unique mappings

Mapping

Demultiplexing

Variant filtering

Genes and known variants

Finding suspicious variants

QC

QC

QC

QC

QC

Quality control steps in the pipeline

How is de-novo assembly different from resequencing analysis ?

RNA-Seq Pipeline

Gene expression levels

Removing duplicates and non-unique mappings

Mapping

Demultiplexing

FPKM Normalization:

Unannotated genes

Gene expression levels

Removing duplicates and non-unique mappings

Mapping

Demultiplexing

Differential gene expression

Differential expression parameters:• Threshold - Minimum number of reads for pair

testing• Normalization• Replicates

Differential expression parameters affect the results

RNA-Seq results

CoverageCoverage – resequencingNumber of bases that cover each base in the genome in average.

“Coverage” – RNA-Seq• Depends on the expression profile of each

sample.• Highly expressed genes will be detected with less

“coverage” than lowly expressed genes.

What should you ask yourself before sequencing when planning the experiment

• Reference genome:– What is my reference genome? – Does it have updated annotations? – What annotations are known? – Are my samples closely related to the reference genome?

• Do I expect to have contaminations in my sample?• Do I have validations from other technologies? (RT-PCR,

SNPchip…)• Do I have controls and replicates?• RNA-Seq: am I interested in alternative splicing?• Resequencing: What kind of mutations do I expect to find?