AIMC Topic: RNA Processing, Post-Transcriptional

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Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications.

Nature communications
Recent studies suggest that epi-transcriptome regulation via post-transcriptional RNA modifications is vital for all RNA types. Precise identification of RNA modification sites is essential for understanding the functions and regulatory mechanisms of...

iMethyl-Deep: N6 Methyladenosine Identification of Yeast Genome with Automatic Feature Extraction Technique by Using Deep Learning Algorithm.

Genes
One of the most common and well studied post-transcription modifications in RNAs is N6-methyladenosine (m6A) which has been involved with a wide range of biological processes. Over the past decades, N6-methyladenosine produced some positive consequen...

Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.

Nature genetics
We address the challenge of detecting the contribution of noncoding mutations to disease with a deep-learning-based framework that predicts the specific regulatory effects and the deleterious impact of genetic variants. Applying this framework to 1,7...

SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions.

PLoS computational biology
LncRNA-protein interactions play important roles in post-transcriptional gene regulation, poly-adenylation, splicing and translation. Identification of lncRNA-protein interactions helps to understand lncRNA-related activities. Existing computational ...

Detecting N-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines.

Scientific reports
As one of the most abundant RNA post-transcriptional modifications, N-methyladenosine (mA) involves in a broad spectrum of biological and physiological processes ranging from mRNA splicing and stability to cell differentiation and reprogramming. Howe...

FASTK post-transcriptional regulators - a 'FAST-tracK' in mitochondrial gene expression.

Biochemical Society transactions
Fas-activated serine/threonine kinase (FASTK) proteins comprise one of the largest families of mitochondrial post-transcriptional regulators. Members are classified based on their conserved C-terminus, which shows homology with the PD-(D/E)XK superfa...

TransCNN: A novel architecture combining transformer and TextCNN for detecting N4-acetylcytidine sites in human mRNA.

Analytical biochemistry
N4-acetylcytidine (ac4C), a pivotal post-transcriptional RNA modification, is central to understanding transcriptional regulation and diverse biological processes. As a key determinant of RNA structural stability and functional regulation, ac4C has b...

ModiDeC: a multi-RNA modification classifier for direct nanopore sequencing.

Nucleic acids research
RNA modifications play a crucial role in various cellular functions. Here, we present ModiDeC, a deep-learning-based classifier able to identify and distinguish multiple RNA modifications (N6-methyladenosine, inosine, pseudouridine, 2'-O-methylguanos...

Nmix: a hybrid deep learning model for precise prediction of 2'-O-methylation sites based on multi-feature fusion and ensemble learning.

Briefings in bioinformatics
RNA 2'-O-methylation (Nm) is a crucial post-transcriptional modification with significant biological implications. However, experimental identification of Nm sites is challenging and resource-intensive. While multiple computational tools have been de...

Deep and accurate detection of m6A RNA modifications using miCLIP2 and m6Aboost machine learning.

Nucleic acids research
N6-methyladenosine (m6A) is the most abundant internal RNA modification in eukaryotic mRNAs and influences many aspects of RNA processing. miCLIP (m6A individual-nucleotide resolution UV crosslinking and immunoprecipitation) is an antibody-based appr...