1. High Throughput Sequencing

2. Agenda Introduction to sequencing Applications
Bioinformatics analysis pipelines
What should you ask yourself before planning the experiment

3. Introduction to sequencing

4. What is sequencing? Finding the sequence of a DNA/ RNA molecule
What can we sequence?

5. Sanger sequencing Up to 1,000 bases molecule One molecule at a time
Widely used from
First human genome draft was based on Sanger sequencing
Still in use for single molecules

6. High Throughput Sequencing
Next 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 bp
~0.5 G
We will discuss Illumina’s platform only

7. Sequencing Workflow Extract tissue cells Extract DNA/RNA from cells
Sample preparation for sequencing
Sequencing
Bioinformatics analysis
Why is it important to understand
the “wet lab” part?

8. Sample Prep Random shearing of the DNA Adding adaptors and barcodes
Size selection
Amplification
Sequencing

9. Sequencing process

10. Leave only sequences from one direction
Sequencing process
Leave only sequences from one direction

11. Sequencing process

12. Sequencing process

13. Sequencing process

14. Applications

15. DNA sequencing
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

16. RNA-Seq
Sequencing of mRNA extracted form the cell to get an estimate of expression levels of genes.

17. RNA-Seq vs. DNA-sequencing
Counting vs. Reading
RNA-Seq vs. DNA-sequencing

18. ChIP-Seq
Sequencing the regions in the genome to which a protein binds to.

19. 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.

20. Bioinformatics Analysis Pipelines

21. Demultiplexing
Lane
Unknown inserts

22. Mapping parameters affect the rest of the analysis
Demultiplexing
Sample
Mapping
Reference Genome
Example of mapping parameters:
Number of mismatches per read
Scores for mismatch or gaps
Mapping parameters affect the rest of the analysis

23. Removing duplicates and non-unique mappings
Demultiplexing
Mapping
Reference Genome
Removing duplicates and non-unique mappings
Reference Genome ?
𝒂𝒗𝒆𝒓𝒂𝒈𝒆 𝒄𝒐𝒗𝒆𝒓𝒂𝒈𝒆= 𝑟𝑒𝑎𝑑 𝑙𝑒𝑛𝑔𝑡ℎ ⋅ 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑟𝑒𝑎𝑑𝑠 ⋅ % 𝑢𝑛𝑖𝑞𝑢𝑒𝑙𝑦 𝑚𝑎𝑝𝑝𝑒𝑑 𝑟𝑒𝑎𝑑𝑠 𝑔𝑒𝑛𝑜𝑚𝑒 𝑠𝑖𝑧𝑒

24. Resequencing/ Exome Pipeline

25. Removing duplicates and non-unique mappings
Demultiplexing
Mapping
Removing duplicates and non-unique mappings
Coverage profile and variant calling
Reference Genome
…ACTTCGTCGAAAGG… G

26. Removing duplicates and non-unique mappings
Demultiplexing
Frequency >= 20%
Mapping
Reference Genome
…ACTTCGTCGAAAGG…
Removing duplicates and non-unique mappings
Coverage profile and variant calling
Coverage >= 5
Variant filtering
Reference Genome
…ACTTCGTCGAAAGG…

27. Removing duplicates and non-unique mappings Genes and known variants
Demultiplexing
Mapping
Removing duplicates and non-unique mappings
Variant calling
Reference Genome
…ACTTCGTCGAAATG… …GTCCCGTGATACTCCGT… G A
Variant filtering
Genes and known variants
rs230985
Gene X

28. Resequencing results

29. Example for further analysis
Recessive disease:
Variant not in known databases
Homozygous variant shared by all affected individuals
Same variant appears in healthy parents at heterozygous state
Healthy brothers can be heterozygous to the same variant
Demultiplexing
Mapping
Removing duplicates and non-unique mappings
Coverage profile and variant calling
Dominant disease:
Variant not in known databases
Heterozygous variant shared by all affected individuals
The variant doesn’t appear in healthy individuals
Variant filtering
Genes and known variants
1 Table per sample
Finding suspicious variants
1 Table per project

30. Quality control steps in the pipeline
Demultiplexing QC
Mapping QC
Removing duplicates and non-unique mappings QC
Coverage profile and variant calling QC
Variant filtering QC
Genes and known variants
Finding suspicious variants

31. How is de-novo assembly different from resequencing analysis ?

32. RNA-Seq Pipeline

33. Removing duplicates and non-unique mappings Gene expression levels
FPKM Normalization:
𝑟𝑎𝑤 𝑐𝑜𝑢𝑛𝑡 𝑔𝑒𝑛𝑒 𝑙𝑒𝑛𝑔𝑡ℎ⋅𝑠𝑎𝑚𝑝𝑙 𝑒 ′ 𝑠 𝑚𝑎𝑝𝑝𝑒𝑑 𝑟𝑒𝑎𝑑𝑠 𝑖𝑛 𝑚𝑖𝑙𝑙𝑖𝑜𝑛𝑠
Demultiplexing
Mapping
Removing duplicates and non-unique mappings
Gene expression levels
Unannotated genes

34. Differential expression parameters:
Demultiplexing
Mapping
Differential expression parameters:
Threshold - Minimum number of reads for pair testing
Normalization
Replicates
Differential expression parameters affect the results
Removing duplicates and non-unique mappings
Gene expression levels
Differential gene expression

35. RNA-Seq results

36. Coverage Coverage – resequencing “Coverage” – RNA-Seq
Number of bases that cover each base in the genome in average.
𝒂𝒗𝒆𝒓𝒂𝒈𝒆 𝒄𝒐𝒗𝒆𝒓𝒂𝒈𝒆= 𝑟𝑒𝑎𝑑 𝑙𝑒𝑛𝑔𝑡ℎ ⋅ 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑟𝑒𝑎𝑑𝑠 ⋅ % 𝑢𝑛𝑖𝑞𝑢𝑒𝑙𝑦 𝑚𝑎𝑝𝑝𝑒𝑑 𝑟𝑒𝑎𝑑𝑠 𝑔𝑒𝑛𝑜𝑚𝑒 𝑠𝑖𝑧𝑒
“Coverage” – RNA-Seq
Depends on the expression profile of each sample.
Highly expressed genes will be detected with less “coverage” than lowly expressed genes.

37. 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?