AIMC Topic: Sequence Analysis, RNA

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Salivary microRNAs identified by small RNA sequencing and machine learning as potential biomarkers of alcohol dependence.

Epigenomics
Salivary miRNA can be easily accessible biomarkers of alcohol dependence (AD). The miRNA transcriptome in the saliva of 56 African-Americans (AAs; 28 AD patients/28 controls) and 64 European-Americans (EAs; 32 AD patients/32 controls) was profiled ...

Exploratory Gene Ontology Analysis with Interactive Visualization.

Scientific reports
The Gene Ontology (GO) is a central resource for functional-genomics research. Scientists rely on the functional annotations in the GO for hypothesis generation and couple it with high-throughput biological data to enhance interpretation of results. ...

MLSeq: Machine learning interface for RNA-sequencing data.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In the last decade, RNA-sequencing technology has become method-of-choice and prefered to microarray technology for gene expression based classification and differential expression analysis since it produces less noisy data....

Novel taxonomy-independent deep learning microbiome approach allows for accurate classification of different forensically relevant human epithelial materials.

Forensic science international. Genetics
Correct identification of different human epithelial materials such as from skin, saliva and vaginal origin is relevant in forensic casework as it provides crucial information for crime reconstruction. However, the overlap in human cell type composit...

Deep-learning augmented RNA-seq analysis of transcript splicing.

Nature methods
A major limitation of RNA sequencing (RNA-seq) analysis of alternative splicing is its reliance on high sequencing coverage. We report DARTS (https://github.com/Xinglab/DARTS), a computational framework that integrates deep-learning-based predictions...

RNA sequencing and swarm intelligence-enhanced classification algorithm development for blood-based disease diagnostics using spliced blood platelet RNA.

Nature protocols
Blood-based diagnostics tests, using individual or panels of biomarkers, may revolutionize disease diagnostics and enable minimally invasive therapy monitoring. However, selection of the most relevant biomarkers from liquid biosources remains an imme...

RPITER: A Hierarchical Deep Learning Framework for ncRNA⁻Protein Interaction Prediction.

International journal of molecular sciences
Non-coding RNAs (ncRNAs) play crucial roles in multiple fundamental biological processes, such as post-transcriptional gene regulation, and are implicated in many complex human diseases. Mostly ncRNAs function by interacting with corresponding RNA-bi...

DeepM6ASeq: prediction and characterization of m6A-containing sequences using deep learning.

BMC bioinformatics
BACKGROUND: N6-methyladensine (m6A) is a common and abundant RNA methylation modification found in various species. As a type of post-transcriptional methylation, m6A plays an important role in diverse RNA activities such as alternative splicing, an ...

Discerning novel splice junctions derived from RNA-seq alignment: a deep learning approach.

BMC genomics
BACKGROUND: Exon splicing is a regulated cellular process in the transcription of protein-coding genes. Technological advancements and cost reductions in RNA sequencing have made quantitative and qualitative assessments of the transcriptome both poss...