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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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Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
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Proteins that regulate transcription can do so either via direct contact with RNA Polymerase or through indirect interactions facilitated by adaptors, mediators, histone-modifying proteins, and nucleosome remodelers. Direct interactions to activate transcription is seen in bacteria as well as in some eukaryotic genes. In these cases, upstream activation sequences are adjacent to the promoters, and the activator proteins interact directly with the transcriptional machinery. For example, in...
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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 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.
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Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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scRNABatchQC: multi-samples quality control for single cell RNA-seq data.

Qi Liu1,2, Quanhu Sheng1,2, Jie Ping1,2

  • 1Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN, USA.

Bioinformatics (Oxford, England)
|August 3, 2019
PubMed
Summary
This summary is machine-generated.

scRNABatchQC is a new R package for analyzing single-cell RNA sequencing data. It helps distinguish technical noise from biological variation by comparing multiple datasets to detect biases and outliers.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Single-cell RNA sequencing (scRNA-seq) reveals cellular heterogeneity but is prone to technical noise and biological variation.
  • Disentangling these sources of variability is crucial for accurate interpretation of scRNA-seq data.
  • Existing tools primarily focus on single-dataset quality control, lacking methods for multi-dataset comparison and bias detection.

Purpose of the Study:

  • To introduce scRNABatchQC, an R package designed for the comparative analysis of multiple scRNA-seq datasets.
  • To provide a tool for identifying and characterizing systematic biases, batch effects, and outliers across different sample sets.
  • To aid researchers in distinguishing technical artifacts from genuine biological variations in scRNA-seq data.

Main Methods:

  • Development of the scRNABatchQC R package.
  • Implementation of functions to compare multiple scRNA-seq sample sets simultaneously.
  • Analysis of technical and biological features for variability assessment.

Main Results:

  • scRNABatchQC enables simultaneous comparison of multiple scRNA-seq datasets.
  • The package facilitates the identification of technical artifacts, batch effects, and outliers.
  • Visual detection of biases and inconsistencies across datasets is supported.

Conclusions:

  • scRNABatchQC offers a valuable approach for quality control and batch effect assessment in multi-dataset scRNA-seq studies.
  • The package aids in distinguishing technical noise from biological signals, improving data reliability.
  • Systematic characterization of variability sources is achieved through cross-dataset comparisons.