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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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
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RNA Stability01:53

RNA Stability

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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 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.
Different Types of RNA Have the Same Basic Structure
There are three main types of ribonucleic acid (RNA): messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three RNA types consist of a...
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Alternative RNA Splicing02:18

Alternative RNA Splicing

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Alternative RNA splicing is the regulated splicing of exons and introns to produce different mature mRNAs from a single pre-mRNA. Unlike in constitutive splicing where a single gene produces a single type of mRNA, alternative splicing allows an organism to produce multiple proteins from a single gene and plays an important role in protein diversity.
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RNA Interference01:23

RNA Interference

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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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Updated: Jan 23, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

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Single-Cell RNA-seq: Introduction to Bioinformatics Analysis.

Fei Ji1,2, Ruslan I Sadreyev1,3

  • 1Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts.

Current Protocols in Molecular Biology
|June 26, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a bioinformatics workflow for analyzing single-cell RNA sequencing (RNA-seq) data. It helps interpret cellular heterogeneity and identify differentially expressed genes between cell clusters.

Keywords:
bioinformaticssingle-cell RNA-seq

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Quantitative analysis of single-cell RNA sequencing (RNA-seq) is essential for understanding cellular heterogeneity.
  • Identifying molecular mechanisms within individual cells requires robust analytical methods.

Purpose of the Study:

  • To present a bioinformatics workflow for analyzing single-cell RNA-seq data.
  • To facilitate the interpretation of cellular heterogeneity and gene expression patterns.

Main Methods:

  • Utilized publicly available computational tools for single-cell RNA-seq data analysis.
  • Developed a workflow focused on transcriptomic interpretation and differential gene expression analysis.

Main Results:

  • The workflow enables the identification of distinct cell clusters.
  • It allows for the detection of genes differentially expressed between these identified clusters.

Conclusions:

  • The presented workflow provides a framework for analyzing single-cell RNA-seq data.
  • This approach aids in understanding cellular heterogeneity and gene expression differences.