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Title: miniMDS: 3D structural inference from high-resolution Hi-C data
Abstract MotivationRecent 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. ResultsWe 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 implementationA Python implementation of miniMDS is available on GitHub: https://github.com/seqcode/miniMDS. Supplementary informationSupplementary data are available at Bioinformatics online.  more » « less
Award ID(s):
1564466
PAR ID:
10413337
Author(s) / Creator(s):
;
Publisher / Repository:
Oxford University Press
Date Published:
Journal Name:
Bioinformatics
Volume:
33
Issue:
14
ISSN:
1367-4803
Page Range / eLocation ID:
p. i261-i266
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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