Dataset Information
MiniMDS: 3D structural inference from high-resolution Hi-C data.
Ontology highlight
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
mahony@psu.edu.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Rieber L
PROVIDER: S-EPMC5870652 | biostudies-literature | 2017 Jul
REPOSITORIES: biostudies-literature
Publications
miniMDS: 3D structural inference from high-resolution Hi-C data.
Bioinformatics (Oxford, England) 20170701 14
<h4>Motivation</h4>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.<h4>Results</h4>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 ...[more]