Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Extraction: Advanced Methods00:56

Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is formed in...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

RNY1 Is Heterogeneously Partitioned in Inflamed Airway Fluid and Modulates Pro-Inflammatory Macrophage Transcriptional Programming.

Journal of extracellular biology·2026
Same author

Tissue-aware elastic net decomposition reveals shared and lineage-specific drug response biomarkers.

bioRxiv : the preprint server for biology·2026
Same author

AVITI sequencing of a four-generation CEPH/Utah pedigree confirms low mutation rates at homopolymer loci despite their low sequence complexity.

Genome biology·2026
Same author

Colibactin-associated mutations in the human colon appear to reflect anatomy and early exposure, not oncogenesis.

medRxiv : the preprint server for health sciences·2026
Same author

Metabolite and nutrient regulation of macrophages in obesity and metabolic disease.

Nature reviews. Immunology·2026
Same author

A shape-constrained regression and wild bootstrap framework for reproducible drug synergy testing.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: May 10, 2026

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut
08:32

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Published on: June 15, 2020

13.3K

DeepRNA-Reg: a deep-learning based approach for comparative analysis of CLIP experiments.

Harshaan Sekhon1, Robin Kageyama1, Neil T Sprenkle2

  • 1Department of Microbiology & Immunology and Sandler Asthma Basic Research Center, University of California San Francisco, San Francisco, CA, USA.

RNA Biology
|October 7, 2025
PubMed
Summary
This summary is machine-generated.

DeepRNA-Reg, a deep learning tool, enhances analysis of RNA sequencing data (HITS-CLIP) for microRNA research. It improves prediction accuracy and identifies novel regulatory mechanisms in T-Helper 2 cells.

Keywords:
Differential HITS-CLIPRNA-binding-protein (RBP)deep learning

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K
Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

600

Related Experiment Videos

Last Updated: May 10, 2026

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut
08:32

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Published on: June 15, 2020

13.3K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K
Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

600

Area of Science:

  • Computational Biology
  • Genomics
  • Molecular Biology

Background:

  • High-throughput sequencing of RNA isolated by crosslinking immunoprecipitation (HITS-CLIP) is crucial for studying RNA-protein interactions.
  • Analyzing differential HITS-CLIP data, especially when microRNA (miRNA) activity is modulated, presents analytical challenges.
  • Understanding miRNA-mediated RNA targeting requires accurate prediction of RNA structural motifs and regulatory networks.

Purpose of the Study:

  • To introduce DeepRNA-Reg, a novel deep learning framework for high-fidelity comparative analysis of paired HITS-CLIP datasets.
  • To evaluate DeepRNA-Reg's performance against existing methods for differential HITS-CLIP analysis.
  • To identify novel mediators of miRNA-mediated regulation in biological systems using DeepRNA-Reg.

Main Methods:

  • Development of DeepRNA-Reg, a deep learning model leveraging advances in AI for HITS-CLIP data analysis.
  • Application of DeepRNA-Reg to paired HITS-CLIP datasets with perturbed miRNA activity (e.g., gene knockout of miRNA clusters).
  • Comparative analysis of DeepRNA-Reg's predictions against established differential HITS-CLIP analysis methods and ground-truth RNA structural data.

Main Results:

  • DeepRNA-Reg demonstrated superior prediction accuracy compared to current leading methods for differential HITS-CLIP analysis.
  • Predictions generated by DeepRNA-Reg showed better adherence to known RNA primary and secondary structural motifs involved in miRNA targeting.
  • The tool successfully uncovered novel mediators in the mechanism of miRNA-mediated restraint of type-2 immunity in T-Helper 2 cells.
  • DeepRNA-Reg predictions exhibited enhanced translatability across different biological contexts.

Conclusions:

  • DeepRNA-Reg offers a robust and accurate deep learning approach for analyzing HITS-CLIP data, particularly in comparative studies.
  • The framework improves the understanding of miRNA-mediated RNA regulation by accurately predicting structural motifs and identifying novel regulatory elements.
  • DeepRNA-Reg provides a versatile tool with broad applicability for researchers investigating RNA biology and gene regulation across diverse biological systems.