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Related Concept Videos

Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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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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Genetic Screens02:46

Genetic Screens

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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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.
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The R Chart01:02

The R Chart

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In statistical process control, control charts, particularly R charts, are instrumental in monitoring process variations and identifying non-random patterns that run charts might miss. R charts track the variability within process subgroups, which is crucial when standard deviation use is impractical or unknown process variations exist.
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Interpreting R Charts01:22

Interpreting R Charts

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R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
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Related Experiment Video

Updated: Jun 16, 2026

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

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A ggplot-based single-gene viewer reveals insights into the translatome and other nucleotide-resolution omics data.

Hsin-Yen Larry Wu1, Isaiah D Kaufman1, Polly Yingshan Hsu1

  • 1Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, MI 48824 USA.

Biorxiv : the Preprint Server for Biology
|February 20, 2025
PubMed
Summary

ggRibo is a new R package that visualizes ribosome profiling (Ribo-seq) and other genomic data for individual genes. It helps researchers understand mRNA translation, gene expression, and regulatory mechanisms.

Keywords:
periodicityribosome profilingsingle-nucleotide resolutiontranslationvisualization

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Visualizing periodic Ribo-seq data is crucial for studying mRNA translation but lacks effective tools.
  • Understanding gene expression regulation requires integrated analysis of multiple genome-wide datasets.

Purpose of the Study:

  • Introduce ggRibo, a user-friendly R package for visualizing gene expression data.
  • Enable the study of mRNA translation, novel ORFs, isoform translation, and translational regulation.
  • Facilitate multi-omic data comparisons for comprehensive gene expression analysis.

Main Methods:

  • Developed ggRibo, an R package for visualizing Ribo-seq, RNA-seq, and other omics data.
  • Integrated flexible scaling options and gene structure context (introns, UTRs).
  • Enabled plotting of multiple datasets from different conditions for comparative analysis.

Main Results:

  • ggRibo visualizes the 3-nucleotide periodicity of translating ribosomes within gene structures.
  • The package supports visualization of various omics datasets at single-nucleotide resolution.
  • Demonstrated utility in analyzing upstream/downstream ORFs, isoform translation, and multi-omic comparisons in humans and Arabidopsis.

Conclusions:

  • ggRibo enhances the interpretation of translatome and related genome-wide datasets.
  • Provides a valuable resource for researchers studying gene expression regulation.
  • Facilitates hypothesis generation through intuitive multi-omic data comparison.