miniMDS: 3D structural inference from high-resolution Hi-C data - Rieber, Lila (Bioinformatics, 2017)
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.