File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: DDGni: dynamic delay gene-network inference from high-temporal data using gapped local alignment

TitleDDGni: dynamic delay gene-network inference from high-temporal data using gapped local alignment
Authors
Issue Date2014
PublisherOxford University Press. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/
Citation
Bioinformatics, 2014, v. 30 n. 3, p. 377-383 How to Cite?
AbstractMOTIVATION: Inferring gene-regulatory networks is very crucial in decoding various complex mechanisms in biological systems. Synthesis of a fully functional transcriptional factor/protein from DNA involves series of reactions, leading to a delay in gene regulation. The complexity increases with the dynamic delay induced by other small molecules involved in gene regulation, and noisy cellular environment. The dynamic delay in gene regulation is quite evident in high-temporal live cell lineage-imaging data. Although a number of gene-network-inference methods are proposed, most of them ignore the associated dynamic time delay. RESULTS: Here, we propose DDGni (dynamic delay gene-network inference), a novel gene-network-inference algorithm based on the gapped local alignment of gene-expression profiles. The local alignment can detect short-term gene regulations, that are usually overlooked by traditional correlation and mutual Information based methods. DDGni uses 'gaps' to handle the dynamic delay and non-uniform sampling frequency in high-temporal data, like live cell imaging data. Our algorithm is evaluated on synthetic and yeast cell cycle data, and Caenorhabditis elegans live cell imaging data against other prominent methods. The area under the curve of our method is significantly higher when compared to other methods on all three datasets. AVAILABILITY: The program, datasets and supplementary files are available at http://www.jjwanglab.org/DDGni/.
Persistent Identifierhttp://hdl.handle.net/10722/193171
ISSN
2015 Impact Factor: 5.766
2015 SCImago Journal Rankings: 4.643
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYalamanchili, HKen_US
dc.contributor.authorYan, Ben_US
dc.contributor.authorLi, Jen_US
dc.contributor.authorQin, Jen_US
dc.contributor.authorZhao, Zen_US
dc.contributor.authorChin, FYLen_US
dc.contributor.authorWang, JJen_US
dc.date.accessioned2013-12-20T02:27:13Z-
dc.date.available2013-12-20T02:27:13Z-
dc.date.issued2014en_US
dc.identifier.citationBioinformatics, 2014, v. 30 n. 3, p. 377-383en_US
dc.identifier.issn1367-4803en_US
dc.identifier.urihttp://hdl.handle.net/10722/193171-
dc.description.abstractMOTIVATION: Inferring gene-regulatory networks is very crucial in decoding various complex mechanisms in biological systems. Synthesis of a fully functional transcriptional factor/protein from DNA involves series of reactions, leading to a delay in gene regulation. The complexity increases with the dynamic delay induced by other small molecules involved in gene regulation, and noisy cellular environment. The dynamic delay in gene regulation is quite evident in high-temporal live cell lineage-imaging data. Although a number of gene-network-inference methods are proposed, most of them ignore the associated dynamic time delay. RESULTS: Here, we propose DDGni (dynamic delay gene-network inference), a novel gene-network-inference algorithm based on the gapped local alignment of gene-expression profiles. The local alignment can detect short-term gene regulations, that are usually overlooked by traditional correlation and mutual Information based methods. DDGni uses 'gaps' to handle the dynamic delay and non-uniform sampling frequency in high-temporal data, like live cell imaging data. Our algorithm is evaluated on synthetic and yeast cell cycle data, and Caenorhabditis elegans live cell imaging data against other prominent methods. The area under the curve of our method is significantly higher when compared to other methods on all three datasets. AVAILABILITY: The program, datasets and supplementary files are available at http://www.jjwanglab.org/DDGni/.-
dc.languageengen_US
dc.publisherOxford University Press. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/-
dc.relation.ispartofBioinformaticsen_US
dc.titleDDGni: dynamic delay gene-network inference from high-temporal data using gapped local alignmenten_US
dc.typeArticleen_US
dc.identifier.emailQin, J: qinjing@hku.hken_US
dc.identifier.emailChin, FYL: chin@cs.hku.hken_US
dc.identifier.emailWang, JJ: junwen@hku.hken_US
dc.identifier.authorityChin, FYL=rp00105en_US
dc.identifier.authorityWang, JJ=rp00280en_US
dc.identifier.doi10.1093/bioinformatics/btt692en_US
dc.identifier.pmid24285602-
dc.identifier.hkuros226973en_US
dc.identifier.volume30-
dc.identifier.issue3-
dc.identifier.spage377-
dc.identifier.epage383-
dc.identifier.isiWOS:000331271100011-
dc.publisher.placeUnited Kingdom-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats