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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. 
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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.
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Related Experiment Video

Updated: Aug 21, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
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The seeker R package: simplified fetching and processing of transcriptome data.

Joshua L Schoenbachler1, Jacob J Hughey1,2

  • 1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States.

Peerj
|November 17, 2022
PubMed
Summary
This summary is machine-generated.

We created seeker, an R package to simplify RNA-seq and microarray data processing. This tool streamlines data fetching and analysis, making complex bioinformatics workflows more accessible for researchers.

Keywords:
AutomationCommand-line interfaceGenomicsMicroarrayRNA-seqTranscriptome data

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Transcriptome data analysis, especially RNA-seq, involves complex command-line tools.
  • The steep learning curve of command-line interfaces (CLIs) hinders automation and parallelization.
  • Familiarity with R in the biological community presents an opportunity for streamlined workflows.

Purpose of the Study:

  • To develop an R package named seeker for simplified fetching and processing of RNA-seq and microarray data.
  • To provide a unified interface for various bioinformatics tools, reducing the need for shell scripting.
  • To enhance reproducibility and accessibility of transcriptome data analysis.

Main Methods:

  • Developed 'seeker' as an R package wrapping existing bioinformatics tools.
  • Implemented a standard interface for data fetching and processing.
  • Integrated simple parallelization and detailed logging capabilities.
  • Ensured compatibility with Entrez and Ensembl Gene IDs for direct downstream analysis.

Main Results:

  • Seeker provides a simplified, standardized interface for transcriptome data preparation.
  • The package facilitates direct integration of processed data into differential expression analysis.
  • Reproducibility is enhanced through a standalone R package and a Docker image.

Conclusions:

  • Seeker significantly lowers the barrier to entry for analyzing RNA-seq and microarray data.
  • The R package promotes reproducible and efficient transcriptome data processing.
  • Seeker empowers researchers by simplifying complex bioinformatics pipelines.