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Related Concept Videos

RNA-seq03:21

RNA-seq

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 microarray-based...
Ribosome Profiling02:24

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...
Real Time RT-PCR02:57

Real Time RT-PCR

Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...

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Related Experiment Video

Updated: Jun 6, 2026

AQRNA-seq for Quantifying Small RNAs
05:12

AQRNA-seq for Quantifying Small RNAs

Published on: February 2, 2024

Accurate quantification of transcriptome from RNA-Seq data by effective length normalization.

Soohyun Lee1, Chae Hwa Seo, Byungho Lim

  • 1Korean Bioinformation Center (KOBIC), Korea Research Institute of Bioscience and Biotechnology (KRIBB), Yuseong-gu, Daejeon, Korea.

Nucleic Acids Research
|November 10, 2010
PubMed
Summary
This summary is machine-generated.

We developed NEUMA, a new method for accurately estimating mRNA levels from RNA-Seq data. This approach normalizes gene expression by considering uniquely mappable areas, improving quantification accuracy.

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AQRNA-seq for Quantifying Small RNAs
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Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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Area of Science:

  • Genomics
  • Transcriptomics
  • Bioinformatics

Background:

  • Accurate quantification of mRNA abundance is crucial for understanding gene expression.
  • Existing RNA-Seq quantification methods face challenges with normalization and accuracy.

Purpose of the Study:

  • To introduce NEUMA (Normalization by Expected Uniquely Mappable Area), a novel and efficient method for estimating mRNA abundances from RNA-Seq data.
  • To demonstrate the superior accuracy and broad applicability of NEUMA compared to existing methods.

Main Methods:

  • NEUMA utilizes effective length normalization based on uniquely mappable areas of gene and mRNA isoform models.
  • It pre-computes informative read counts using transcriptome sequence models (e.g., RefSeq).
  • The method incorporates experimental fragment size distributions for accurate length estimation.

Main Results:

  • NEUMA demonstrated superior accuracy in estimating mRNA abundances compared to other methods, validated by quantitative RT-PCR and simulations.
  • The method provides a consistency coefficient to compare gene-level and isoform-level expression.
  • NEUMA is applicable to both paired-end and single-end RNA-Seq data and covers a large proportion of genes and isoforms.

Conclusions:

  • NEUMA offers an efficient, intuitive, and accurate approach for quantifying gene transcript levels from RNA-Seq data.
  • Its robustness and comprehensive features suggest potential for standardization in transcript quantification.