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1000 Titel
  • biomvRhsmm: Genomic Segmentation with Hidden Semi-Markov Model
1000 Autor/in
  1. Murani, Eduard |
  2. Ponsuksili, Siriluck |
  3. Du, Yang |
  4. Wimmers, Klaus |
1000 Erscheinungsjahr 2014
1000 Art der Datei
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2014-06-03
1000 Erschienen in
1000 Quellenangabe
  • 2014: 910390
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2014
1000 Lizenz
1000 Verlagsversion
  • http://dx.doi.org/10.1155/2014/910390 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4065698/ |
1000 Ergänzendes Material
  • https://www.hindawi.com/journals/bmri/2014/910390/sup/ |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • High-throughput technologies like tiling array and next-generation sequencing (NGS) generate continuous homogeneous segments or signal peaks in the genome that represent transcripts and transcript variants (transcript mapping and quantification), regions of deletion and amplification (copy number variation), or regions characterized by particular common features like chromatin state or DNA methylation ratio (epigenetic modifications). However, the volume and output of data produced by these technologies present challenges in analysis. Here, a hidden semi-Markov model (HSMM) is implemented and tailored to handle multiple genomic profile, to better facilitate genome annotation by assisting in the detection of transcripts, regulatory regions, and copy number variation by holistic microarray or NGS. With support for various data distributions, instead of limiting itself to one specific application, the proposed hidden semi-Markov model is designed to allow modeling options to accommodate different types of genomic data and to serve as a general segmentation engine. By incorporating genomic positions into the sojourn distribution of HSMM, with optional prior learning using annotation or previous studies, the modeling output is more biologically sensible. The proposed model has been compared with several other state-of-the-art segmentation models through simulation benchmarking, which shows that our efficient implementation achieves comparable or better sensitivity and specificity in genomic segmentation.
1000 Fachgruppe
  1. Biologie |
  2. Agrarwissenschaften |
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/creator/TXVyYW5pLCBFZHVhcmQ=|https://frl.publisso.de/adhoc/creator/UG9uc3Vrc2lsaSwgU2lyaWx1Y2s=|http://orcid.org/0000-0002-5707-513X|http://orcid.org/0000-0002-9523-6790
1000 Förderer
  1. German Research Foundation (DFG) |
1000 Fördernummer
  1. DFG Wi 1754/14-1
1000 Förderprogramm
  1. -
1000 Dateien
  1. biomvRhsmm: Genomic Segmentation with Hidden Semi-Markov Model
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer German Research Foundation (DFG) |
    1000 Förderprogramm -
    1000 Fördernummer DFG Wi 1754/14-1
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6405466.rdf
1000 Erstellt am 2017-11-23T16:23:46.456+0100
1000 Erstellt von 218
1000 beschreibt frl:6405466
1000 Bearbeitet von 218
1000 Zuletzt bearbeitet Wed May 02 13:07:17 CEST 2018
1000 Objekt bearb. Wed May 02 13:07:17 CEST 2018
1000 Vgl. frl:6405466
1000 Oai Id
  1. oai:frl.publisso.de:frl:6405466 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
1000 Gegenstand von

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