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Bioinformatics. 2017 Jul 15;33(14):i261-i266. doi: 10.1093/bioinformatics/btx271.

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

Author information

1
Department of Biochemistry and Molecular Biology and Center for Eukaryotic Gene Regulation, The Pennsylvania State University, University Park, PA, USA.

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.

PMID:
28882003
PMCID:
PMC5870652
DOI:
10.1093/bioinformatics/btx271
[Indexed for MEDLINE]
Free PMC Article

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