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• W3137968765 abstract "The identification of upstream open reading frames (uORFs) using ribosome profiling data is complicated by several factors such as the noise inherent to the procedure, the substantial increase in potential translation initiation sites (and false positives) when one includes non-canonical start codons, and the paucity of molecularly validated uORFs. Here we present uORF-seqr, a novel machine learning algorithm that uses ribosome profiling data, in conjunction with RNA-seq data, as well as transcript aware genome annotation files to identify statistically significant AUG and near-cognate codon uORFs." @default.
• W3137968765 created "2021-03-29" @default.
• W3137968765 creator A5029328053 @default.
• W3137968765 creator A5068771069 @default.
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• W3137968765 date "2021-01-01" @default.
• W3137968765 modified "2023-09-23" @default.
• W3137968765 title "uORF-seqr: A Machine Learning-Based Approach to the Identification of Upstream Open Reading Frames in Yeast" @default.
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• W3137968765 doi "https://doi.org/10.1007/978-1-0716-1150-0_15" @default.
• W3137968765 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33765283" @default.
• W3137968765 hasPublicationYear "2021" @default.
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