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

Lila Rieber, Shaun Mahony

Research output: Contribution to journalArticle

10 Citations (Scopus)

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).

Original languageEnglish (US)
Pages (from-to)i261-i266
JournalBioinformatics
Volume33
Issue number14
DOIs
StatePublished - Jul 15 2017

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High Resolution
Genes
Partition
Scaling
Data storage equipment
Human Genome
Experiments
Genome
Approximation
Experiment
Datasets
Human

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

Cite this

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MiniMDS : 3D structural inference from high-resolution Hi-C data. / Rieber, Lila; Mahony, Shaun.

In: Bioinformatics, Vol. 33, No. 14, 15.07.2017, p. i261-i266.

Research output: Contribution to journalArticle

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AU - Mahony, Shaun

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