MiniMDS: 3D structural inference from high-resolution Hi-C data.

ABSTRACT:

Motivation

Recent experiments have provided Hi-C data at resolution as high as 1 kbp. However, 3D structural inference from high-resolution Hi-C datasets is often computationally unfeasible using existing methods.

Results

We have developed miniMDS, an approximation of multidimensional scaling (MDS) that partitions a Hi-C dataset, performs high-resolution MDS separately on each partition, and then reassembles the partitions using low-resolution MDS. miniMDS is faster, more accurate, and uses less memory than existing methods for inferring the human genome at high resolution (10 kbp).

Availability and implementation

A Python implementation of miniMDS is available on GitHub: https://github.com/seqcode/miniMDS .

Contact

[e-mail].

Supplementary information<

SUBMITTER: Rieber L 

PROVIDER: S-EPMC5870652 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature