Abstract:
I will illustrate how to view fragments produced in a
high-throughput sequencing experiment as points in the plane. This
collection of points forms a two-dimensional spatial Poisson process,
which yields a null model for random fragment coverage. I will then show
how the successive jumps of the depth coverage function can be encoded as
a random tree that is approximately a Galton-Watson tree with
generation-dependent offspring distributions. Two applications of this
theory will be presented: an algorithm for finding protein binding sites
using ChIP-Seq data and a statistical test to address fragment bias in
RNA-Seq data. |