MultiHiCcompare: joint normalization and comparative analysis of complex Hi-C experiments.

ABSTRACT:

Motivation

With the development of chromatin conformation capture technology and its high-throughput derivative Hi-C sequencing, studies of the three-dimensional interactome of the genome that involve multiple Hi-C datasets are becoming available. To account for the technology-driven biases unique to each dataset, there is a distinct need for methods to jointly normalize multiple Hi-C datasets. Previous attempts at removing biases from Hi-C data have made use of techniques which normalize individual Hi-C datasets, or, at best, jointly normalize two datasets.

Results

Here, we present multiHiCcompare, a cyclic loess regression-based joint normalization technique for removing biases across multiple Hi-C datasets. In contrast to other normalization techniques, it properly handle

SUBMITTER: Stansfield JC 

PROVIDER: S-EPMC6736119 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature