Exploring the cooccurrence patterns of multiple sets of genomic intervals.
Background. Exploringthe spatial relationship of different genomic features has been of great interest since the early days of genomic research. The relationship sometimes provides useful information for understanding certain biological processes. Recent advances in high-throughput technologies such...
| Publicado en: | BioMed Research International Vol. 2013; pp. 617545 - 617546 |
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| Autores principales: | , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103807903&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103807903 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2013 vid: 2013 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 103807903 103807903 2012159784 NLM23781505 PMC3679813 103807903 ppf: 617545 ppct: 1 formats: fmt: @attributes: type: P tig: atl: Exploring the cooccurrence patterns of multiple sets of genomic intervals. aug: au: Wu, Hao Qin, Zhaohui S affil: Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA. sug: subj: Genome Genomics Animal Studies Cells Cluster Analysis Genes Human Lysine Metabolism Mice Models, Biological Proteins Proteins Metabolism Software Stem Cells Metabolism ab: Background. Exploringthe spatial relationship of different genomic features has been of great interest since the early days of genomic research. The relationship sometimes provides useful information for understanding certain biological processes. Recent advances in high-throughput technologies such as ChIP-seq produce large amount of data in the form of genomic intervals. Most of the existing methods for assessing spatial relationships among the intervals are designed for pairwise comparison and cannot be easily scaled up. Results. We present a statistical method and software tool to characterize the cooccurrence patterns of multiple sets of genomic intervals. The occurrences of genomic intervals are described by a simple finite mixture model, where each component represents a distinct cooccurrence pattern. The model parameters are estimated via an EM algorithm and can be viewed as sufficient statistics of the cooccurrence patterns. Simulation and real data results show that the model can accurately capture the patterns and provide biologically meaningful results. The method is implemented in a freely available R package gi Clust. Conclusions. The method and the software provide a convenient way for biologists to explore the cooccurrence patterns among a relatively large number of sets of genomic intervals. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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