Reading Between the Genes: Computational Models to Discover Function from Noncoding DNA
AffiliationUniv Arizona, UA Canc Ctr, Ctr Biomed Informat & Biostat, BIO5 Inst,Ctr Appl Genet & Genom Med
Univ Arizona, Dept Med
MetadataShow full item record
PublisherWORLD SCIENTIFIC PUBL CO PTE LTD
CitationLussier, Y. A., Berghout, J., Vitali, F., Ramos, K. S., Kann, M., & Moore, J. H. (2017). Reading Between the Genes: Computational Models to Discover Function from Noncoding DNA.
Rights© 2017 The Authors. Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BYNC) 4.0 License.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractNoncoding DNA - once called "junk" has revealed itself to be full of function. Technology development has allowed researchers to gather genome-scale data pointing towards complex regulatory regions, expression and function of noncoding RNA genes, and conserved elements. Variation in these regions has been tied to variation in biological function and human disease. This PSB session tackles the problem of handling, analyzing and interpreting the data relating to variation in and interactions between noncoding regions through computational biology. We feature an invited speaker to how variation in transcription factor coding sequences impacts on sequence preference, along with submitted papers that span graph based methods, integrative analyses, machine learning, and dimension reduction to explore questions of basic biology, cancer, diabetes, and clinical relevance.
NoteOpen access journal
VersionFinal published version
SponsorsUniversity of Arizona Health Sciences CB2, the BIO5 Institute; NIH [U01AI122275, HL132532, CA023074, 1UG3OD023171, 1R01AG053589-01A1, 1S10RR029030]
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