Whole genome sequencing has become popular due to decreasing costs and time required. It sequences the entire human genome, about 80% compared to 1% for exome sequencing. Technologies like Illumina and Ion Torrent are used. Bioinformatics analysis of whole genome data identifies single nucleotide polymorphisms (SNPs), insertions/deletions (indels), copy number variations (CNVs), and structural variations (SVs) which can provide insights into disease. Public databases and manual annotation are used to understand the impact of these variations.

1. Whole Genome Analysis
Bone-Net Workshop - 21 January 2012
Ir Stéphane Wenric – s.wenric@dnavision.be

2. Whole Genome Analysis
Whole genome analysis:
why is it such a hot topic today?

3. Whole Genome Analysis

4. Whole Genome Analysis
human whole genome sequencing
Price
Time

5. Whole Genome Analysis
Microarray Exome Whole Genome
Only known SNPs Only the coding regions The complete DNA
(~ 900 000) of the genome sequences
Up to 0.0003 % of the ~ 1 % of the human ~ 80 % of the human
human genome genome genome

6. Whole Genome Analysis
What are the technologies involved ?
•
Illumina (Solexa)
•
ABI SOLiD
•
Ion Proton (2013)

7. Whole Genome Analysis
Illumina (Solexa) :
•
cluster generation by bridge amplification
•
sequencing by synthesis

8. Whole Genome Analysis

9. Whole Genome Analysis
ABI SOLiD :
•
amplification on magnetic beads
•
ligation + fluorescence detection
•
2-base color encoding

10. Whole Genome Analysis

11. Whole Genome Analysis
Ion semiconductor sequencing :
•
PCR amplification
•
dNTP incorporation w/ H+ release

14. Whole Genome Analysis
whole genome analysis = whole genome sequencing
+
bioinformatics

15. Whole Genome Analysis
What kind of bioinformatics analyses are
best suited for whole genome data?
•
SNP
•
Indels
•
CNV
•
SV

16. Whole Genome Analysis
Single Nucleotide Polymorphisms
•
What?
•
How?
•
Impact?

17. Whole Genome Analysis

18. Whole Genome Analysis
SNPs : easy to detect
1. Map sequenced reads to reference sequence
2. See how the 'consensus sequence' differs from the reference
3. 1-base difference between consensus sequence
and reference = SNP

19. Whole Genome Analysis
SNPs: impact?
•
Public databases:
•
DbSNP (ncbi)
•
OMIM (Johns Hopkins University School of Medicine)
•
SIFT (Craig Venter Institute)
•
…
•
'Manual' annotation

20. Whole Genome Analysis
Insertions and Deletions
•
What?
•
How?
•
Impact?

21. Whole Genome Analysis
Insertion or deletion of a sequence of DNA of arbitrary length

22. Whole Genome Analysis
Indels detection
Small Indels :
•
Detection of small gaps in the alignment
•
Combination of the gapped alignments based on proximity
•
Filtering (read pos., coverage, quality)
Large Indels :
•
Use of the reads pairing info (Illumina and SOLiD only)

23. Whole Genome Analysis
Indels: impact?
•
Public databases:
•
dbSNP for small indels (ncbi)
•
dbVar for large indels (ncbi)
•
'Manual' annotation

24. Whole Genome Analysis
Copy Number Variations
•
What?
•
How?
•
Impact?

25. Whole Genome Analysis
CNV:
•
variation in terms of copy number of a sequence
•
>= 1 kb
•
up to n Mb

26. Whole Genome Analysis
CNV detection methods
•
Statistical models:
•
Depth-of-reads/coverage
•
Pairing information

27. Whole Genome Analysis
CNVs: impact?
•
Public databases:
•
Database of Genomic Variants (Center for Applied genomics, Canada)
•
CNV Project (Sanger)
•
...
•
'Manual' annotation

28. Whole Genome Analysis
Structural Variations
•
What?
•
How?
•
Impact?

29. Whole Genome Analysis
Structural Variations
•
>= 1 kb
•
Inversions
•
Translocations
•
(Large indels)
•
(CNVs)

30. Whole Genome Analysis
Inversions

31. Whole Genome Analysis
Translocations

32. Whole Genome Analysis
SVs: detection methods
•
Geometric approach (GASV)
•
Takes pairing info into account
•
Compares potential variants to geometric models

33. Whole Genome Analysis
SVs: impact?
•
Public databases:
•
Database of Genomic Variants (Center for Applied genomics, Canada)
•
dbVar (ncbi)
•
...
•
'Manual' annotation

34. Whole Genome Analysis
Questions?