AIMC Topic: 3' Untranslated Regions

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Deciphering 3'UTR Mediated Gene Regulation Using Interpretable Deep Representation Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Aug 19, 2024
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...
RNA, Messenger Gene Expression Regulation 3' Untranslated Regions Humans Deep Learning Computational Biology
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DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning.

Genomics, proteomics & bioinformatics Mar 2, 2021
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...
Computational Biology 3' Untranslated Regions Gene Expression Regulation Neural Networks, Computer Polyadenylation Deep Learning
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A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation.

Cell Jun 6, 2019
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 ...
RNA Cleavage HEK293 Cells Base Sequence Mutagenesis Polyadenylation 3' Untranslated Regions Databases, Genetic Gene Expression Synthetic Biology RNA, Messenger Models, Genetic Humans Transcriptome Deep Learning RNA-Seq
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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 May 10, 2019
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...
Humans Machine Learning ROC Curve Pseudogenes 3' Untranslated Regions Alu Elements RNA, Messenger Area Under Curve Long Interspersed Nucleotide Elements Genome, Human
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miRAW: A deep learning-based approach to predict microRNA targets by analyzing whole microRNA transcripts.

PLoS computational biology Jul 13, 2018
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...
Thermodynamics Gene Targeting 3' Untranslated Regions Computer Simulation Reproducibility of Results Datasets as Topic Deep Learning Humans Gene Expression Regulation RNA, Messenger Neural Networks, Computer Binding Sites MicroRNAs
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Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation.

Nucleic acids research Oct 14, 2022
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...
Methylation Protein Isoforms 3' Untranslated Regions RNA, Long Noncoding RNA Deep Learning RNA, Messenger
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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 Oct 1, 2019
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...
Software Computational Biology RNA, Messenger Humans MicroRNAs HeLa Cells Gene Knockdown Techniques Models, Genetic Support Vector Machine Databases, Genetic 3' Untranslated Regions Binding Sites Gene Knockout Techniques
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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 Aug 21, 2017
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...
Binding Sites Computational Biology Gene Expression Regulation 3' Untranslated Regions Algorithms Nucleotide Motifs Mutation Reproducibility of Results Binding, Competitive Humans Internet Protein Binding Machine Learning High-Throughput Nucleotide Sequencing RNA-Binding Proteins Base Sequence
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