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Finding differentially expressed regions of arbitrary length in quantitative genomic data based on marked point process model
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Hatsuda, H. (2012) Finding differentially expressed regions of arbitrary length in quantitative genomic data based on marked point process model. Bioinformatics, Vol.28 (No.18). i633-i639. doi:10.1093/bioinformatics/bts371 ISSN 1367-4803.
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Official URL: http://dx.doi.org/10.1093/bioinformatics/bts371
Abstract
Motivation: High-throughput nucleotide sequencing technologies provide large amounts of quantitative genomic data at nucleotide resolution, which are important for the present and future biomedical researches; for example differential analysis of base-level RNA expression data will improve our understanding of transcriptome, including both coding and non-coding genes. However, most studies of these data have relied on existing genome annotations and thus are limited to the analysis of known transcripts.
Results: In this article, we propose a novel method based on a marked point process model to find differentially expressed genomic regions of arbitrary length without using genome annotations. The presented method conducts a statistical test for differential analysis in regions of various lengths at each nucleotide and searches the optimal configuration of the regions by using a Monte Carlo simulation. We applied the proposed method to both synthetic and real genomic data, and their results demonstrate the effectiveness of our method.
Item Type: | Journal Article | ||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||
Journal or Publication Title: | Bioinformatics | ||||
Publisher: | Oxford University Press | ||||
ISSN: | 1367-4803 | ||||
Official Date: | 2012 | ||||
Dates: |
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Volume: | Vol.28 | ||||
Number: | No.18 | ||||
Page Range: | i633-i639 | ||||
DOI: | 10.1093/bioinformatics/bts371 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access |
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