N7-methylguanosine (m7G) is an essential, ubiquitous, and positively charged modification at the 5′ cap of eukaryotic mRNA, modulating its export, translation, and splicing processes. Although several machine learning (ML)-based computational predictors for m7G have been developed, all utilized specific computational ...
Read More »deCS – A tool for systematic cell type annotations of single-cell RNA sequencing data among human tissues
Single-cell RNA sequencing (scRNA-seq) is revolutionizing the study of complex and dynamic cellular mechanisms. However, cell-type annotation remains a main challenge as it largely relies on a...
Read More »RNA-Seq data reanalysis IDs 1,000+ mislabeled, overlooked gene fragments in plants
Researchers have overlooked especially minuscule gene fragments that are critical to the assembly of cellular machinery and could help better trace the evolutionary history of plants, says a new study led by the University of Nebraska–Lincoln...
Read More »RiboDetector – rapid and accurate identification of ribosomal RNA sequences via deep learning
Advances in transcriptomic and translatomic techniques enable in-depth studies of RNA activity profiles and RNA-based regulatory mechanisms. Ribosomal RNA (rRNA) sequences are highly abundant among cellular RNA, but if the target sequences...
Read More »scAnnotate – an automated cell type annotation tool for single-cell RNA-sequencing data
Single-cell RNA-sequencing (scRNA-seq) technology enables researchers to investigate a genome at the cellular level with unprecedented resolution. An organism consists of a heterogeneous collection of cell types, each of which plays a distinct role...
Read More »sRNARFTarget – a fast machine-learning-based approach for transcriptome-wide sRNA target prediction
Bacterial small regulatory RNAs (sRNAs) are key regulators of gene expression in many processes related to adaptive responses. A multitude of sRNAs have been identified in many bacterial species...
Read More »CLEARER – a machine learning approach for predicting essential genes across eukaryotes
Identifying essential genes on a genome scale is resource intensive and has been performed for only a few eukaryotes. For less studied organisms...
Read More »Automated annotation of rare-cell types from single-cell RNA-sequencing data through synthetic oversampling
The research landscape of single-cell and single-nuclei RNA-sequencing is evolving rapidly. In particular, the area for the detection of rare cells was highly facilitated by this technology. However...
Read More »CoCoA-diff – counterfactual inference for single-cell gene expression analysis
Finding a causal gene is a fundamental problem in genomic medicine. Researchers from the University of British Columbia and MIT have developed a...
Read More »WIND (Workflow for pIRNAs aNd beyonD): a strategy for in-depth analysis of small RNA-seq data
Current bioinformatics workflows for PIWI-interacting RNA (piRNA) analysis focus primarily on germline-derived piRNAs and piRNA-clusters...
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