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Related Concept Videos

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Real-World Application of Classical Conditioning01:15

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Classical conditioning not only includes the initial pairing of stimuli but also extends to more complex forms, such as higher-order conditioning. Higher-order conditioning involves creating associations beyond the primary conditioned stimulus, resulting in a chain of conditioned responses.
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RNA Interference01:23

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RNA interference (RNAi) is a process in which a small non-coding RNA molecule blocks the post-transcriptional expression of a gene by binding to its messenger RNA (mRNA) and preventing the protein from being translated.
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RNA Structure01:23

RNA Structure

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Overview
The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
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RNA Stability01:53

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Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
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RNA Splicing01:32

RNA Splicing

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Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...
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Related Experiment Video

Updated: Feb 7, 2026

Identification of Footprints of RNA:Protein Complexes via RNA Immunoprecipitation in Tandem Followed by Sequencing RIPiT-Seq
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Benchmarking RNA-seq Tools for Real-World Diagnostic Applications.

Sarah Silverstein1,2,3, Kaushik R Ganapathy4, Sandra Donkervoort1

  • 1Neuromuscular and Neurogenetics Disorders of Childhood Section, NINDS, NIH Bethesda MD.

Medrxiv : the Preprint Server for Health Sciences
|February 6, 2026
PubMed
Summary

RNA-sequencing (RNA-seq) tools aid in diagnosing pediatric neuromuscular diseases by analyzing RNA data for aberrant splicing and expression. While helpful, these computational tools complement, rather than replace, manual genetic analysis for definitive diagnoses.

Keywords:
RNA-seqcomputational toolsdiagnosticspediatric neuromuscular diseaserare diseasetranscriptomicsvariant interpretation

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Area of Science:

  • Genetics
  • Bioinformatics
  • Molecular Biology

Background:

  • Pediatric neuromuscular diseases present significant genetic and clinical heterogeneity.
  • Many cases lack a definitive genetic diagnosis despite advanced molecular testing.
  • RNA-sequencing (RNA-seq) offers potential to analyze functional impacts of genetic variants, but its systematic application requires established best practices.

Purpose of the Study:

  • To evaluate the performance of open-source computational tools for analyzing RNA-seq data in diagnosing pediatric neuromuscular diseases.
  • To establish best use practices for RNA-seq analysis tools in a clinical diagnostic setting.
  • To identify novel genetic diagnoses in undiagnosed pediatric neuromuscular disease cases using RNA-seq analysis.

Main Methods:

  • A truth set was created using RNA-seq data from 97 diagnosed samples to benchmark tool performance.
  • Eight common RNA-seq analysis tools for splicing, expression, and allelic imbalance were assessed.
  • The optimal analysis strategy was applied to 74 undiagnosed RNA-seq samples.

Main Results:

  • Computational tools identified diagnoses in 28 out of 68 diagnosed probands with aberrant RNA events.
  • Splicing analysis tools were most frequent, but allelic imbalance tools provided unique insights.
  • False positive rates varied, with splicing tools having the highest and expression analysis the lowest.
  • Candidate variants were identified for 9 out of 74 undiagnosed patients.

Conclusions:

  • RNA-seq analysis tools can accelerate variant prioritization and interpretation in genetic diagnostics.
  • These tools serve as a valuable complement to traditional DNA sequencing and manual analysis.
  • Further refinement of RNA-seq analysis strategies is needed for broader clinical utility.