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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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Ribosome Profiling02:24

Ribosome Profiling

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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...
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Related Experiment Video

Updated: Mar 28, 2026

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions
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Generating Sample-Specific Databases for Mass Spectrometry-Based Proteomic Analysis by Using RNA Sequencing.

Toni Luge1, Sascha Sauer2

  • 1Otto Warburg Laboratory, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195, Berlin, Germany.

Methods in Molecular Biology (Clifton, N.J.)
|December 25, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new pipeline using RNA sequencing (RNA-Seq) to improve mass spectrometry proteomics. This method enhances protein identification, especially for organisms lacking genome sequences.

Keywords:
Gene expressionMass spectrometryMetaproteomicsProteogenomicsProteomics informed by transcriptomics

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Area of Science:

  • Proteomics
  • Genomics
  • Transcriptomics

Background:

  • Mass spectrometry enables protein analysis but typically requires genomic databases for identification.
  • This reliance on genomic data limits proteomic studies in species without available reference sequences.
  • Existing methods struggle with genetic variations and splicing events affecting protein identification.

Purpose of the Study:

  • To present a generic pipeline for integrating transcriptomic data into proteomics workflows.
  • To enable robust proteomic analysis for organisms lacking genomic sequences.
  • To enhance the accuracy of protein identification in complex biological samples.

Main Methods:

  • Utilizing RNA sequencing (RNA-Seq) for de novo database construction or database refinement.
  • Developing a pipeline to efficiently supply sample-specific RNA-Seq data to proteomics analysis.
  • Applying the pipeline to analyze unsequenced organisms and complex metatranscriptomes/metaproteomes.

Main Results:

  • Demonstrated efficient fueling of proteomic analysis with sample-specific RNA-Seq databases.
  • Successfully applied the approach to proteomic analysis of organisms without prior genome sequences.
  • Showcased the utility for refining existing genomic databases with transcriptomic insights.

Conclusions:

  • The presented pipeline overcomes limitations of traditional proteomics by leveraging transcriptomic data.
  • This approach significantly improves protein identification accuracy and expands the scope of proteomic research.
  • The method is valuable for studying unsequenced organisms, microbial communities, and refining conventional proteomics data quality.