miniMDS: 3D structural inference from high-resolution Hi-C data.
basic_science · Level V
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- Record sourced from PubMed, PMID 28882003.
- Also identified by DOI 10.1093/bioinformatics/btx271 and PMC identifier 5870652.
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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.
Medical subject headings
- Chromosomes, Human
- Genome, Human
- Genomics
- Models, Molecular
- Software