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Related Concept Videos

RNA-seq03:21

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 Profiling02:24

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...
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the addition of a...
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...

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Related Experiment Video

Updated: Jul 4, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
12:54

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Data-driven RNA phenotyping captures genetically regulated dimensions of the transcriptome.

Daniel Munro1, Alexander Gusev2, Abraham A Palmer3

  • 1Department of Psychiatry, UC San Diego, La Jolla, CA, USA; Center for Immunity and Immunotherapies, Seattle Children's Research Institute, Seattle, WA, USA.

American Journal of Human Genetics
|July 2, 2026
PubMed
Summary

LaDDR, a novel framework, enhances the discovery of genetic associations with complex traits by analyzing RNA regulation without needing complete gene annotations. It identifies significantly more trait-associated genes than previous methods, improving our understanding of genetic regulation.

Keywords:
RNA-seqgene regulationmolecular quantitative trait locistatistical geneticstranscriptome-wide association studytranscriptomics

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Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
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Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Related Experiment Videos

Last Updated: Jul 4, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
12:54

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
06:24

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Transcriptomic diversity is key to understanding gene regulation and interpreting genome-wide association study (GWAS) signals.
  • Existing multimodal frameworks like Pantry integrate various RNA expression data but require complete gene annotations and struggle with correlated data.
  • Limitations in current tools restrict the discovery of molecular quantitative trait loci (xQTLs) and the full scope of regulatory variation.

Purpose of the Study:

  • To introduce LaDDR (latent data-driven RNA phenotyping), a mechanism-agnostic framework for enhanced xQTL discovery and GWAS integration.
  • To overcome the reliance on complete gene annotations and the statistical complexity of analyzing correlated transcriptomic modalities.
  • To broaden the detectable landscape of trait-relevant transcriptomic regulation by efficiently recovering missed regulatory variation.

Main Methods:

  • LaDDR generates orthogonal, latent coverage features per gene, enabling analysis without complete gene annotations.
  • The framework was applied to the Genotype-Tissue Expression (GTEx) Project data.
  • LaDDR phenotypes were integrated with knowledge-driven phenotypes and used in transcriptome-wide association studies (TWAS).

Main Results:

  • LaDDR identified an average of 95% more independent xQTLs per tissue compared to Pantry's knowledge-driven modes.
  • Combining LaDDR with knowledge-driven phenotypes increased discovery by an additional 41% per tissue on average.
  • In TWAS of 114 complex traits, LaDDR uncovered an average of 11,796 unique gene-trait pairs per tissue, versus 8,630 from knowledge-driven phenotypes.

Conclusions:

  • LaDDR significantly expands the discovery of xQTLs and gene-trait associations by capturing regulatory variation missed by current pipelines.
  • The framework provides a powerful, annotation-independent approach for genetic analysis of transcriptomic data.
  • LaDDR enhances the interpretation of complex traits by revealing novel functional and colocalization qualities of genetic signals.