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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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Comparing Copy Number Variations and SNPs02:26

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Quality control method for RNA-seq using single nucleotide polymorphism allele frequency.

Takaho A Endo1

  • 1RIKEN Center for Integrative Medical Science (IMS-RIKEN), 1-7-22 Suehiro-Cho, Tsurumi-Ku, Yokohama, Kanagawa, 230-0045, Japan.

Genes to Cells : Devoted to Molecular & Cellular Mechanisms
|September 23, 2014
PubMed
Summary
This summary is machine-generated.

Analyzing RNA sequencing (RNA-seq) data with single nucleotide polymorphism (SNP) information can reveal sample quality. Allele frequencies from RNA-seq data can identify potential chromosomal abnormalities and mixed cell populations in experimental datasets.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • RNA sequencing (RNA-seq) offers insights into gene expression and host cell genomic sequences.
  • Integrating transcriptome data with whole-genome single nucleotide polymorphism (SNP) information allows for the assessment of genetic composition and chromosomal aberrations.
  • Retracted studies, such as those on stimulus-triggered acquisition of pluripotency (STAP) cells, highlight the need for robust experimental quality control.

Purpose of the Study:

  • To demonstrate the utility of single nucleotide polymorphisms (SNPs) in messenger RNAs (mRNAs) for evaluating RNA sequencing (RNA-seq) experiments.
  • To re-evaluate RNA-seq data from a retracted study on STAP cells using SNP allele frequency analysis.
  • To establish allele frequency observation as a method for assessing sample quality in both prospective and retrospective analyses.

Main Methods:

  • Analysis of RNA sequencing (RNA-seq) data, focusing on single nucleotide polymorphism (SNP) variants within mRNA sequences.
  • Comparison of allele frequencies derived from transcriptome data against known genetic information.
  • Application of these methods to RNA-seq datasets from a study on stimulus-triggered acquisition of pluripotency (STAP) cells.

Main Results:

  • The re-evaluation of STAP cell RNA-seq data using SNP allele frequencies indicated the potential presence of diverse cell types and chromosomal abnormalities within the dataset.
  • Observed allele frequency patterns suggested experimental artifacts or sample heterogeneity that may have contributed to the original study's conclusions.
  • The findings underscore the sensitivity of allele frequency analysis in detecting sample quality issues.

Conclusions:

  • Single nucleotide polymorphism (SNP) analysis in RNA sequencing (RNA-seq) data provides a valuable tool for assessing sample quality and experimental integrity.
  • Allele frequency profiling can retrospectively identify potential issues such as sample contamination or chromosomal aberrations that might compromise study results.
  • This approach enhances the reliability of RNA-seq-based research by enabling rigorous quality control of experimental data.