TY - JOUR T1 - miniMDS: 3D structural inference from high-resolution Hi-C data JF - bioRxiv DO - 10.1101/122473 SP - 122473 AU - Lila Rieber AU - Shaun Mahony Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/04/11/122473.abstract N2 - 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. ER -