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3' Untranslated Regions

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A deep boosting based approach for capturing the sequence binding preferences of RNA-binding proteins from high-throughput CLIP-seq data.

Nucleic acids research 28575488
Characterizing the binding behaviors of RNA-binding proteins (RBPs) is important for understanding their functional roles in gene expression regulation. However, current high-throughput experimental methods for identifying RBP targets, such as CLIP-s...
3' Untranslated Regions Algorithms Base Sequence Binding Sites Binding, Competitive Computational Biology Gene Expression Regulation High-Throughput Nucleotide Sequencing Humans Internet Machine Learning Mutation Nucleotide Motifs Protein Binding Reproducibility of Results RNA-Binding Proteins
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miRAW: A deep learning-based approach to predict microRNA targets by analyzing whole microRNA transcripts.

PLoS computational biology 30005074
MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression by binding to partially complementary regions within the 3'UTR of their target genes. Computational methods play an important role in target prediction and assume that the miR...
3' Untranslated Regions Binding Sites Computer Simulation Datasets as Topic Deep Learning Gene Expression Regulation Gene Targeting Humans MicroRNAs Neural Networks, Computer Reproducibility of Results RNA, Messenger Thermodynamics
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Recognition of 3'-end L1, Alu, processed pseudogenes, and mRNA stem-loops in the human genome using sequence-based and structure-based machine-learning models.

Scientific reports 31076573
The role of 3'-end stem-loops in retrotransposition was experimentally demonstrated for transposons of various species, where LINE-SINE retrotransposons share the same 3'-end sequences, containing a stem-loop. We have discovered that 62-68% of proces...
3' Untranslated Regions Alu Elements Area Under Curve Genome, Human Humans Long Interspersed Nucleotide Elements Machine Learning Pseudogenes RNA, Messenger ROC Curve
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A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation.

Cell 31178116
Alternative polyadenylation (APA) is a major driver of transcriptome diversity in human cells. Here, we use deep learning to predict APA from DNA sequence alone. We trained our model (APARENT, APA REgression NeT) on isoform expression data from over ...
3' Untranslated Regions Base Sequence Databases, Genetic Deep Learning Gene Expression HEK293 Cells Humans Models, Genetic Mutagenesis Polyadenylation RNA Cleavage RNA-Seq RNA, Messenger Synthetic Biology Transcriptome
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Min3: Predict microRNA target gene using an improved binding-site representation method and support vector machine.

Journal of bioinformatics and computational biology 31856668
MicroRNAs are single-stranded noncoding RNAs known to down-regulate target genes at the protein or mRNA level. Computational prediction of targets is essential for elucidating the detailed functions of microRNA. However, prediction specificity and se...
3' Untranslated Regions Binding Sites Computational Biology Databases, Genetic Gene Knockdown Techniques Gene Knockout Techniques HeLa Cells Humans MicroRNAs Models, Genetic RNA, Messenger Software Support Vector Machine
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DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning.

Genomics, proteomics & bioinformatics 33662629
Alternative polyadenylation (APA) is a crucial step in post-transcriptional regulation. Previous bioinformatic studies have mainly focused on the recognition of polyadenylation sites (PASs) in a given genomic sequence, which is a binary classificatio...
3' Untranslated Regions Computational Biology Deep Learning Gene Expression Regulation Neural Networks, Computer Polyadenylation
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Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation.

Nucleic acids research 36155798
As the most pervasive epigenetic mark present on mRNA and lncRNA, N6-methyladenosine (m6A) RNA methylation regulates all stages of RNA life in various biological processes and disease mechanisms. Computational methods for deciphering RNA modification...
3' Untranslated Regions Deep Learning Methylation Protein Isoforms RNA RNA, Long Noncoding RNA, Messenger
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Deciphering 3'UTR Mediated Gene Regulation Using Interpretable Deep Representation Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) 39159140
The 3' untranslated regions (3'UTRs) of messenger RNAs contain many important cis-regulatory elements that are under functional and evolutionary constraints. It is hypothesized that these constraints are similar to grammars and syntaxes in human lang...
3' Untranslated Regions Computational Biology Deep Learning Gene Expression Regulation Humans RNA, Messenger
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