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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...

Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

Proteomics

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...

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Related Experiment Video

Updated: May 10, 2026

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Published on: April 11, 2019

Proteogenomic database construction driven from large scale RNA-seq data.

Sunghee Woo¹, Seong Won Cha, Gennifer Merrihew

¹Department of Electrical and Computing Engineering, ¶Department of Bioinformatics and Systems Biology, and §Department of Computer Science, University of California, San Diego , La Jolla, California 92093, United States.

Journal of Proteome Research

|June 28, 2013

Summary

This summary is machine-generated.

Researchers developed a novel method to compress RNA-sequencing data, significantly reducing file sizes by 1000x. This approach enables efficient integration of RNA and proteomic data for improved genome annotation.

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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

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Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Published on: April 11, 2019

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Area of Science:

Genomics
Proteomics
Bioinformatics

Background:

RNA-sequencing (RNA-seq) and mass spectrometry (MS) offer powerful insights into genomic annotation and cellular conditions.
Integrating large, redundant RNA-seq and MS datasets presents a significant computational challenge.

Purpose of the Study:

To develop a method for compressing RNA-seq data to facilitate integration with proteomic data.
To improve genome annotation accuracy through combined transcriptomic and proteomic analysis.

Main Methods:

Construction of a compact splice graph database from RNA-seq reads.
Application of the method to C. elegans data, achieving substantial data size reduction.
Utilizing a fully automated pipeline for proteogenomic analysis.

Main Results:

Reduced 496.2 GB of RNA-seq data to 410 MB (1000x compression) without loss of sensitivity.
Identified 4044 novel genomic events, including 215 novel genes and 808 novel exons.
Detected various other novelties such as alternative splicing, gene/exon boundary corrections, frame shifts, and translated UTRs.

Conclusions:

The developed method enables highly efficient data compression for RNA-seq.
Transcriptomic and proteomic data integration significantly enhances genome annotation quality.
This approach is crucial for advancing genomic research and understanding cellular mechanisms.