miniMDS: 3D structural inference from high-resolution Hi-C data
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 H...
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| Veröffentlicht in: | Bioinformatics (Oxford, England) England), 2017-07, Vol.33 (14), p.i261-i266 |
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| container_title | Bioinformatics (Oxford, England) |
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| creator | Rieber, Lila Mahony, Shaun |
| description | 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. |
| doi_str_mv | 10.1093/bioinformatics/btx271 |
| format | Article |
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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.
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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.</description><subject>Algorithms</subject><subject>bioinformatics</subject><subject>Chromosomes, Human - metabolism</subject><subject>computer software</subject><subject>data collection</subject><subject>genome</subject><subject>Genome, Human</subject><subject>Genomics - methods</subject><subject>Humans</subject><subject>Models, Molecular</subject><subject>Molecular Conformation</subject><subject>multidimensional scaling</subject><subject>Sequence Analysis, DNA - methods</subject><subject>Software</subject><issn>1367-4803</issn><issn>1460-2059</issn><issn>1367-4811</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkc1OwzAQhC0EolB4BFCOXELtuBs7HECo5U8q4gCcLcexW6MkLraD4O0JaqnoidOutDOjWX0InRB8TnBBR6V1tjXONzJaFUZl_MwY2UEHhOYsHXNCdjc7pgN0GMIbxhgw5PtokHHOM4zpAbpqbGsfp88XCZ0mIfpOxc7LOumztdet0onxrkkWdr5IvQ6u7qJ1bXJv00lSySiP0J6RddDH6zlEr7c3L5P7dPZ09zC5nqUKCI4pz7gGzTkw0BiKstKginFVQM6ZYoWkFFMjtTHjHEtjKkKgApOVJjMVlDmjQ3S5yl12ZaMrpdvY1xRLbxvpv4STVmxfWrsQc_chgDOcQ9YHnK0DvHvvdIiisUHpupatdl0QGcVAipxQ8q-UFJQBYbTgvRRWUuVdCF6bTSOCxQ8nsc1JrDj1vtO_72xcv2DoN6Z3lMc</recordid><startdate>20170715</startdate><enddate>20170715</enddate><creator>Rieber, Lila</creator><creator>Mahony, Shaun</creator><general>Oxford University Press</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7S9</scope><scope>L.6</scope><scope>5PM</scope></search><sort><creationdate>20170715</creationdate><title>miniMDS: 3D structural inference from high-resolution Hi-C data</title><author>Rieber, Lila ; Mahony, Shaun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c510t-828e5e88575e059bde5c94d95687c79a3303faeff460affd115d5f2bf2fd5b673</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Algorithms</topic><topic>bioinformatics</topic><topic>Chromosomes, Human - metabolism</topic><topic>computer software</topic><topic>data collection</topic><topic>genome</topic><topic>Genome, Human</topic><topic>Genomics - methods</topic><topic>Humans</topic><topic>Models, Molecular</topic><topic>Molecular Conformation</topic><topic>multidimensional scaling</topic><topic>Sequence Analysis, DNA - methods</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rieber, Lila</creatorcontrib><creatorcontrib>Mahony, Shaun</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Bioinformatics (Oxford, England)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Rieber, Lila</au><au>Mahony, Shaun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>miniMDS: 3D structural inference from high-resolution Hi-C data</atitle><jtitle>Bioinformatics (Oxford, England)</jtitle><addtitle>Bioinformatics</addtitle><date>2017-07-15</date><risdate>2017</risdate><volume>33</volume><issue>14</issue><spage>i261</spage><epage>i266</epage><pages>i261-i266</pages><issn>1367-4803</issn><issn>1460-2059</issn><eissn>1367-4811</eissn><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.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>28882003</pmid><doi>10.1093/bioinformatics/btx271</doi><oa>free_for_read</oa></addata></record> |
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| ispartof | Bioinformatics (Oxford, England), 2017-07, Vol.33 (14), p.i261-i266 |
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| source | Oxford Journals Open Access Collection; MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Alma/SFX Local Collection |
| subjects | Algorithms bioinformatics Chromosomes, Human - metabolism computer software data collection genome Genome, Human Genomics - methods Humans Models, Molecular Molecular Conformation multidimensional scaling Sequence Analysis, DNA - methods Software |
| title | miniMDS: 3D structural inference from high-resolution Hi-C data |
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