Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Accounting for genome region specific coverage biases #17

Open
AyushSaxena opened this issue Jul 10, 2018 · 0 comments
Open

Accounting for genome region specific coverage biases #17

AyushSaxena opened this issue Jul 10, 2018 · 0 comments

Comments

@AyushSaxena
Copy link

We have observed in our data (generated through multiple different Illumina machines and library prep methods), that local coverage density varies across the genome, predictably so, across all genotypes. When we calculate read coverage by bin size in any two genotypes, we observe a correlation between the two read coverage in two genotypes in a specific bin. Ideally, if sampling across the genome is random, we should see no correlation. Also, in the real data, the correlation coefficient stays the same regardless of the bin size.

Reads produced through wg-sim also produce this correlation, albeit the correlation coefficient is smaller, and approaches the correlation coefficient of real data at bin sizes of >100kb. Is there a way to manipulate this correlation coefficient ourselves?

Ayush

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

1 participant