@article {Rieber122473, author = {Lila Rieber and Shaun Mahony}, title = {miniMDS: 3D structural inference from high-resolution Hi-C data}, elocation-id = {122473}, year = {2017}, doi = {10.1101/122473}, publisher = {Cold Spring Harbor Laboratory}, 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 A Python implementation of miniMDS is available on GitHub: https://github.com/seqcode/miniMDS.}, URL = {https://www.biorxiv.org/content/early/2017/04/12/122473}, eprint = {https://www.biorxiv.org/content/early/2017/04/12/122473.full.pdf}, journal = {bioRxiv} }