AIMC Topic: Polyadenylation
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Delineating yeast cleavage and polyadenylation signals using deep learning.
Genome research
Aug 20, 2024
3'-end cleavage and polyadenylation is an essential process for eukaryotic mRNA maturation. In yeast species, the polyadenylation signals that recruit the processing machinery are degenerate and remain poorly characterized compared with the well-defi...
Context-Aware Poly(A) Signal Prediction Model via Deep Spatial-Temporal Neural Networks.
IEEE transactions on neural networks and learning systems
Jun 3, 2024
Polyadenylation [Poly(A)] is an essential process during messenger RNA (mRNA) maturation in biological eukaryote systems. Identifying Poly(A) signals (PASs) from the genome level is the key to understanding the mechanism of translation regulation and...
PolyAMiner-Bulk is a deep learning-based algorithm that decodes alternative polyadenylation dynamics from bulk RNA-seq data.
Cell reports methods
Feb 6, 2024
Alternative polyadenylation (APA) is a key post-transcriptional regulatory mechanism; yet, its regulation and impact on human diseases remain understudied. Existing bulk RNA sequencing (RNA-seq)-based APA methods predominantly rely on predefined anno...
Deep learning of human polyadenylation sites at nucleotide resolution reveals molecular determinants of site usage and relevance in disease.
Nature communications
Nov 15, 2023
The genomic distribution of cleavage and polyadenylation (polyA) sites should be co-evolutionally optimized with the local gene structure. Otherwise, spurious polyadenylation can cause premature transcription termination and generate aberrant protein...
SCAPTURE: a deep learning-embedded pipeline that captures polyadenylation information from 3' tag-based RNA-seq of single cells.
Genome biology
Aug 10, 2021
Single-cell RNA-seq (scRNA-seq) profiles gene expression with high resolution. Here, we develop a stepwise computational method-called SCAPTURE to identify, evaluate, and quantify cleavage and polyadenylation sites (PASs) from 3' tag-based scRNA-seq....
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...
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 ...
Hybrid model for efficient prediction of poly(A) signals in human genomic DNA.
Methods (San Diego, Calif.)
Apr 13, 2019
Polyadenylation signals (PAS) are found in most protein-coding and some non-coding genes in eukaryotes. Their accurate recognition improves understanding gene regulation mechanisms and recognition of the 3'-end of transcribed gene regions where prema...
An improved poly(A) motifs recognition method based on decision level fusion.
Computational biology and chemistry
Dec 30, 2014
Polyadenylation is the process of addition of poly(A) tail to mRNA 3' ends. Identification of motifs controlling polyadenylation plays an essential role in improving genome annotation accuracy and better understanding of the mechanisms governing gene...
The Advances in Deep Learning Modeling of Polyadenylation Codes.
Wiley interdisciplinary reviews. RNA
Jan 1, 2025
3'-end cleavage and polyadenylation is an essential step of eukaryotic mRNA and lncRNA expression. The formation of a polyadenylation (polyA) site is determined by combinatory effects of multiple tandem motifs (~6 motifs in humans), each of which is ...
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