AIMC Topic: Sequence Analysis, RNA

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Representation learning of RNA velocity reveals robust cell transitions.

Proceedings of the National Academy of Sciences of the United States of America
RNA velocity is a promising technique for quantifying cellular transitions from single-cell transcriptome experiments and revealing transient cellular dynamics among a heterogeneous cell population. However, the cell transitions estimated from high-d...

Knowledge-based classification of fine-grained immune cell types in single-cell RNA-Seq data.

Briefings in bioinformatics
Single-cell RNA sequencing (scRNA-Seq) is an emerging strategy for characterizing immune cell populations. Compared to flow or mass cytometry, scRNA-Seq could potentially identify cell types and activation states that lack precise cell surface marker...

jSRC: a flexible and accurate joint learning algorithm for clustering of single-cell RNA-sequencing data.

Briefings in bioinformatics
Single-cell RNA-sequencing (scRNA-seq) explores the transcriptome of genes at cell level, which sheds light on revealing the heterogeneity and dynamics of cell populations. Advances in biotechnologies make it possible to generate scRNA-seq profiles f...

A comprehensive survey on computational methods of non-coding RNA and disease association prediction.

Briefings in bioinformatics
The studies on relationships between non-coding RNAs and diseases are widely carried out in recent years. A large number of experimental methods and technologies of producing biological data have also been developed. However, due to their high labor ...

A survey on deep learning in DNA/RNA motif mining.

Briefings in bioinformatics
DNA/RNA motif mining is the foundation of gene function research. The DNA/RNA motif mining plays an extremely important role in identifying the DNA- or RNA-protein binding site, which helps to understand the mechanism of gene regulation and managemen...

Modeling multi-species RNA modification through multi-task curriculum learning.

Nucleic acids research
N6-methyladenosine (m6A) is the most pervasive modification in eukaryotic mRNAs. Numerous biological processes are regulated by this critical post-transcriptional mark, such as gene expression, RNA stability, RNA structure and translation. Recently, ...

Artificial intelligence and metagenomics in intestinal diseases.

Journal of gastroenterology and hepatology
Gut microbiota has been shown to associate with the development of gastrointestinal diseases. In the last decade, development in whole metagenome sequencing and 16S rRNA sequencing technology has dramatically accelerated the gut microbiome's research...

Machine learning and statistical methods for clustering single-cell RNA-sequencing data.

Briefings in bioinformatics
UNLABELLED: Single-cell RNAsequencing (scRNA-seq) technologies have enabled the large-scale whole-transcriptome profiling of each individual single cell in a cell population. A core analysis of the scRNA-seq transcriptome profiles is to cluster the s...

mRNALoc: a novel machine-learning based in-silico tool to predict mRNA subcellular localization.

Nucleic acids research
Recent evidences suggest that the localization of mRNAs near the subcellular compartment of the translated proteins is a more robust cellular tool, which optimizes protein expression, post-transcriptionally. Retention of mRNA in the nucleus can regul...

The neXtProt knowledgebase in 2020: data, tools and usability improvements.

Nucleic acids research
The neXtProt knowledgebase (https://www.nextprot.org) is an integrative resource providing both data on human protein and the tools to explore these. In order to provide comprehensive and up-to-date data, we evaluate and add new data sets. We describ...