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RNA-seq

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 Profiling

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-PCR

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 1, 2026

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

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

Published on: November 7, 2025

Computational methods for transcriptome annotation and quantification using RNA-seq.

Manuel Garber¹, Manfred G Grabherr, Mitchell Guttman

¹Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA. mgarber@broadinstitute.org

|May 31, 2011

Summary

This summary is machine-generated.

High-throughput RNA sequencing (RNA-seq) offers a complete view of the transcriptome. This summary explains computational challenges in read mapping, transcriptome reconstruction, and expression quantification for RNA-seq data analysis.

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Related Experiment Videos

Last Updated: Jun 1, 2026

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

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

Published on: November 7, 2025

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

High-throughput RNA sequencing (RNA-seq) provides a comprehensive view of the transcriptome.
Accurate gene and isoform annotation and quantification are crucial for biological insights.
Advancements in RNA-seq necessitate sophisticated computational approaches.

Purpose of the Study:

To outline the primary computational challenges in RNA-seq data analysis.
To categorize these challenges into read mapping, transcriptome reconstruction, and expression quantification.
To discuss existing solutions and their interdependencies for various biological applications.

Main Methods:

Review of computational methodologies for RNA-seq data processing.
Categorization of challenges in read mapping, transcriptome reconstruction, and expression quantification.
Analysis of the relationships between different computational steps.

Main Results:

Identification of key conceptual and practical challenges in each category.
Overview of general classes of computational solutions for RNA-seq analysis.
Emphasis on the interconnectedness of mapping, reconstruction, and quantification.

Conclusions:

Addressing computational challenges is vital for realizing the full potential of RNA-seq.
Understanding the interdependence of computational steps enhances data analysis.
Effective RNA-seq analysis supports diverse biological research applications.