miniMDS: 3D structural inference from high-resolution Hi-C data

• DOI: 10.1093/bioinformatics/btx271
• Publication Date: 2017-04-20T07:52:13Z
• Authors: Lila Rieber; Shaun Mahony
• Keywords: Models, Molecular; 0301 basic medicine; Genome, Human; 0206 medical engineering; Molecular Conformation; Genomics; Sequence Analysis, DNA; 02 engineering and technology; Ismb/Eccb 2017: The 25th Annual Conference Intelligent Systems for Molecular Biology Held Jointly with the 16th Annual European Conference on Computational Biology, Prague, Czech Republic, July 21–25, 2017; 03 medical and health sciences; Chromosomes, Human; Humans; Algorithms; Software

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.