General Information
| Description | 3D structural inference from high-resolution Hi-C data |
| Homepage | https://github.com/seqcode/miniMDS |
Publications
miniMDS: 3D structural inference from high-resolution Hi-C data
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.
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