Structural Variation Detection with Read Pair Information: An Improved Null-Hypothesis Reduces Bias

Published in Journal of Computational Biology, 2017

This paper highlights size bias in calling structural variations (specifically insertions and deletions) from mate-pair and paired-end sequencing data. The paper presents a model to correct for this bias. The model allows for fast computiation of unique optima, introducing minimal ovehead to methods that does not model this.

Download the preprint version here