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  • Selpi, Selpi,1977Chalmers tekniska högskola,Chalmers University of Technology (author)

Predicting functional upstream open reading frames in Saccharomyces cerevisiae

  • Article/chapterEnglish2009

Publisher, publication year, extent ...

  • 2009-12-30
  • Springer Science and Business Media LLC,2009
  • electronicrdacarrier

Numbers

  • LIBRIS-ID:oai:gup.ub.gu.se/105191
  • https://gup.ub.gu.se/publication/105191URI
  • https://doi.org/10.1186/1471-2105-10-451DOI
  • https://research.chalmers.se/publication/105191URI

Supplementary language notes

  • Language:English

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  • Subject category:ref swepub-contenttype
  • Subject category:art swepub-publicationtype

Notes

  • Background: Some upstream open reading frames (uORFs) regulate gene expression (i.e., they are functional) and can play key roles in keeping organisms healthy. However, how uORFs are involved in gene regulation is not yet fully understood. In order to get a complete view of how uORFs are involved in gene regulation, it is expected that a large number of experimentally verified functional uORFs are needed. Unfortunately, wet-experiments to verify that uORFs are functional are expensive. Results: In this paper, a new computational approach to predicting functional uORFs in the yeast Saccharomyces cerevisiae is presented. Our approach is based on inductive logic programming and makes use of a novel combination of knowledge about biological conservation, Gene Ontology annotations and genes' responses to different conditions. Our method results in a set of simple and informative hypotheses with an estimated sensitivity of 76%. The hypotheses predict 301 further genes to have 398 novel functional uORFs. Three (RPC11, TPK1, and FOL1) of these 301 genes have been hypothesised, following wet-experiments, by a related study to have functional uORFs. A comparison with another related study suggests that eleven of the predicted functional uORFs from genes LDB17, HEM3, CIN8, BCK2, PMC1, FAS1, APP1, ACC1, CKA2, SUR1, and ATH1 are strong candidates for wet-lab experimental studies. Conclusions: Learning based prediction of functional uORFs can be done with a high sensitivity. The predictions made in this study can serve as a list of candidates for subsequent wet-lab verification and might help to elucidate the regulatory roles of uORFs.

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Added entries (persons, corporate bodies, meetings, titles ...)

  • Bryant, Christopher H.University of Salford (author)
  • Kemp, Graham,1965Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)kemp (author)
  • Sarv, Janeli,1981Gothenburg University,Göteborgs universitet,Institutionen för matematiska vetenskaper, matematisk statistik,Department of Mathematical Sciences, Mathematical Statistics,Chalmers tekniska högskola,Chalmers University of Technology,University of Gothenburg(Swepub:cth)sarv (author)
  • Kristiansson, Erik,1978Gothenburg University,Göteborgs universitet,Zoologiska institutionen,Department of Zoology,University of Gothenburg(Swepub:cth)erikkr (author)
  • Sunnerhagen, Per,1959Gothenburg University,Göteborgs universitet,Institutionen för cell- och molekylärbiologi,Department of Cell and Molecular Biology,University of Gothenburg(Swepub:gu)xsuper (author)
  • Chalmers tekniska högskolaUniversity of Salford (creator_code:org_t)

Related titles

  • In:BMC Bioinformatics: Springer Science and Business Media LLC101471-2105

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