I show some examples of how to use BEDTools to solve different tasks realted to copy-number variation analysis.

1. The use of BEDTools to analyze
CNV regions
Leandro Lima
CAG analytical meeting - Dec 10, 2014
Center for Applied Genomics
The Children’s Hospital of Philadelphia

2. Motivation
• In genetics, many analyses are a subtype of
set theory or intervals arithmetic
• Examples:
1. Check coverage in a genome / exome
2. Find de novo CNVs
3. Find novel CNVs in a database

3. The BEDTools suite
(some examples)
• intersect

4. The BEDTools suite
(some examples)
• cluster

5. The BEDTools suite
(some examples)
• merge

6. The BEDTools suite
(some examples)
• genomecov

7. Evolution of DGV
(Database of Genomic Variants)
• DGV is a curated catalogue of human genomic
structural variation
• The content of the database is only
representing structural variation identified in
healthy control samples
• The Database of Genomic Variants provides a
useful catalog of control data for studies
aiming to correlate genomic variation with
phenotypic data

12. Increase in Variation Data
Source: http://dgv.tcag.ca/dgv/app/statistics

13. Example 1 – Evolution of DGV
per year, by chromosome
• First, we have to select regions of the references with
year of publication less or equal to a specific year
• Then, use bedtools genomecov to get the percentage of
each chromosomes covered by the variant regions

14. Increase in Variation Data
(by chromosome)

15. Increase in Variation Data
(by chromosome)

16. Increase in Variation Data
(by chromosome)

17. Increase in Variation Data
(by chromosome)

18. Increase in Variation Data
(by chromosome)

19. Increase in Variation Data
(by chromosome)

20. Increase in Variation Data
(by chromosome)

21. Increase in Variation Data
(by chromosome)

22. Increase in Variation Data
(by chromosome)

23. Increase in Variation Data
(by chromosome)

24. Increase in Variation Data
(by chromosome)

25. Increase in Variation Data
(by chromosome)

26. Example 2: find de novo CNVs
• Step 1: merge parents CNVs
• Step 2: get regions that do not overlap with
CNVs from child

27. Example 2: find de novo CNVs
• Merge parents CNVs

28. Example 2: find de novo CNVs
• intersect -v Child CNVs
Parents CNVs
inherited
de novo

29. Example 2: find de novo CNVs
• cluster (to find de novo CNVs that happen in
more than one family)

30. Example 3: find novel CNVs
• intersect -v De novo CNVs
DGV
previously
reported
novel (never reported)

31. Questions?