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RNA

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Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery.

Briefings in bioinformatics
Computational analysis of RNA sequences constitutes a crucial step in the field of RNA biology. As in other domains of the life sciences, the incorporation of artificial intelligence and machine learning techniques into RNA sequence analysis has gain...

Structural interaction fingerprints and machine learning for predicting and explaining binding of small molecule ligands to RNA.

Briefings in bioinformatics
Ribonucleic acids (RNAs) play crucial roles in living organisms and some of them, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, whereas others, e.g. bacterial riboswitches or viral RNA motifs are consid...

Concepts and methods for transcriptome-wide prediction of chemical messenger RNA modifications with machine learning.

Briefings in bioinformatics
The expanding field of epitranscriptomics might rival the epigenome in the diversity of biological processes impacted. In recent years, the development of new high-throughput experimental and computational techniques has been a key driving force in d...

Machine learning for RNA 2D structure prediction benchmarked on experimental data.

Briefings in bioinformatics
Since the 1980s, dozens of computational methods have addressed the problem of predicting RNA secondary structure. Among them are those that follow standard optimization approaches and, more recently, machine learning (ML) algorithms. The former were...

Identification of metal ion-binding sites in RNA structures using deep learning method.

Briefings in bioinformatics
Metal ion is an indispensable factor for the proper folding, structural stability and functioning of RNA molecules. However, it is very difficult for experimental methods to detect them in RNAs. With the increase of experimentally resolved RNA struct...

Identification of species-specific RNA N6-methyladinosine modification sites from RNA sequences.

Briefings in bioinformatics
N6-methyladinosine (m6A) modification is the most abundant co-transcriptional modification in eukaryotic RNA and plays important roles in cellular regulation. Traditional high-throughput sequencing experiments used to explore functional mechanisms ar...

RNAdegformer: accurate prediction of mRNA degradation at nucleotide resolution with deep learning.

Briefings in bioinformatics
Messenger RNA-based therapeutics have shown tremendous potential, as demonstrated by the rapid development of messenger RNA based vaccines for COVID-19. Nevertheless, distribution of mRNA vaccines worldwide has been hampered by mRNA's inherent therma...

The hitchhikers' guide to RNA sequencing and functional analysis.

Briefings in bioinformatics
DNA and RNA sequencing technologies have revolutionized biology and biomedical sciences, sequencing full genomes and transcriptomes at very high speeds and reasonably low costs. RNA sequencing (RNA-Seq) enables transcript identification and quantific...

RLBind: a deep learning method to predict RNA-ligand binding sites.

Briefings in bioinformatics
Identification of RNA-small molecule binding sites plays an essential role in RNA-targeted drug discovery and development. These small molecules are expected to be leading compounds to guide the development of new types of RNA-targeted therapeutics c...