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.
Supplementary information
Supplementary data are available at Bioinformatics online.
© The Author 2017. Published by Oxford University Press.
2017
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