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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.
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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...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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Related Experiment Video

Updated: May 31, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
05:07

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

Published on: November 7, 2025

Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling.

Paweł P Łabaj1, Germán G Leparc, Bryan E Linggi

  • 1Boku University Vienna, 1190 Muthgasse 18, Vienna, Austria.

Bioinformatics (Oxford, England)
|June 21, 2011
PubMed
Summary
This summary is machine-generated.

This study analyzes RNA-Seq precision, finding that higher sequencing depth offers diminishing returns for gene expression profiling. A new computational approach improves transcript quantification accuracy, enabling reliable measurement of more transcripts.

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Related Experiment Videos

Last Updated: May 31, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
05:07

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Published on: November 7, 2025

AQRNA-seq for Quantifying Small RNAs
05:12

AQRNA-seq for Quantifying Small RNAs

Published on: February 2, 2024

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms
09:30

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Published on: September 13, 2018

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Measurement precision is crucial for identifying significant biological signals in RNA sequencing (RNA-Seq) data.
  • Large-scale RNA-Seq datasets with technical replicates enable systematic analysis of expression estimation precision.
  • Understanding precision limitations is key to improving computational and experimental strategies.

Purpose of the Study:

  • To systematically analyze the precision of expression level estimates from RNA-Seq.
  • To investigate the dependence of measurement precision on factors like transcript expression levels and read depth.
  • To develop and evaluate improved computational methods for gene expression profiling.

Main Methods:

  • Comprehensive analysis of target identification and measurement precision using large-scale RNA-Seq data.
  • Evaluation of transcript expression levels, read depth, and read length impacts on quantification.
  • Development of a novel approach for mapping and analyzing sequencing reads for gene expression profiling.

Main Results:

  • High recall (84%) achieved with 331 million 50 bp reads, with limited gains from longer reads or increased depth.
  • Measurement power is concentrated on highly expressed transcripts (7% of transcriptome), making lowly expressed ones harder to quantify.
  • <30% of transcripts reliably quantified with <20% relative error using established tools.
  • The new approach increased reliably quantified transcripts to over 40%.

Conclusions:

  • RNA-Seq measurement precision is influenced by transcript abundance and sequencing depth, with diminishing returns.
  • A novel computational method significantly enhances gene expression profiling accuracy and the number of reliably quantified transcripts.
  • Further improvements in quantification precision require efficient complementary experimental and computational strategies.