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Methylation

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m5U-SVM: identification of RNA 5-methyluridine modification sites based on multi-view features of physicochemical features and distributed representation.

BMC biology
BACKGROUND: RNA 5-methyluridine (m5U) modifications are obtained by methylation at the C position of uridine catalyzed by pyrimidine methylation transferase, which is related to the development of human diseases. Accurate identification of m5U modifi...

Aptamer facilitated purification of functional proteins.

Journal of chromatography. B, Analytical technologies in the biomedical and life sciences
DNA aptamers are attractive capture probes for affinity chromatography since, in contrast to antibodies, they can be chemically synthesized and, in contrast to tag-specific capture probes (such as Nickel-NTA or Glutathione), they can be used for puri...

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...

DeepPRMS: advanced deep learning model to predict protein arginine methylation sites.

Briefings in functional genomics
Protein methylation is a form of post-translational modifications of protein, which is crucial for various cellular processes, including transcription activity and DNA repair. Correctly predicting protein methylation sites is fundamental for research...

m6ACali: machine learning-powered calibration for accurate m6A detection in MeRIP-Seq.

Nucleic acids research
We present m6ACali, a novel machine-learning framework aimed at enhancing the accuracy of N6-methyladenosine (m6A) epitranscriptome profiling by reducing the impact of non-specific antibody enrichment in MeRIP-Seq. The calibration model serves as a g...

H2Opred: a robust and efficient hybrid deep learning model for predicting 2'-O-methylation sites in human RNA.

Briefings in bioinformatics
2'-O-methylation (2OM) is the most common post-transcriptional modification of RNA. It plays a crucial role in RNA splicing, RNA stability and innate immunity. Despite advances in high-throughput detection, the chemical stability of 2OM makes it diff...

DeepGpgs: a novel deep learning framework for predicting arginine methylation sites combined with Gaussian prior and gated self-attention mechanism.

Briefings in bioinformatics
Protein arginine methylation is an important posttranslational modification (PTM) associated with protein functional diversity and pathological conditions including cancer. Identification of methylation binding sites facilitates a better understandin...

Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation.

Nucleic acids research
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...

Predicting genes associated with RNA methylation pathways using machine learning.

Communications biology
RNA methylation plays an important role in functional regulation of RNAs, and has thus attracted an increasing interest in biology and drug discovery. Here, we collected and collated transcriptomic, proteomic, structural and physical interaction data...

Machine learning algorithm for precise prediction of 2'-O-methylation (Nm) sites from experimental RiboMethSeq datasets.

Methods (San Diego, Calif.)
Analysis of epitranscriptomic RNA modifications by deep sequencing-based approaches brings an essential contribution to the general knowledge on their precise locations and relative stoichiometry in cellular RNAs. To reveal RNA modifications, several...