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

ABSTRACT: 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.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).A Python implementation of miniMDS is available on GitHub: https://github.com/seqcode/miniMDS .mahony@psu.edu.Supplementary data are available at Bioinformatics online.

SUBMITTER: Rieber L

PROVIDER: S-EPMC5870652 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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

Rieber Lila L Mahony Shaun S

Bioinformatics (Oxford, England) 20170701 14

<h4>Motivation</h4>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.<h4>Results</h4>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 ...[more]

PMID: 28882003

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