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

Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

9.2K
While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
9.2K
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

3.5K
3.5K
RNA Polymerase II Accessory Proteins02:36

RNA Polymerase II Accessory Proteins

11.0K
Proteins that regulate transcription can do so either via direct contact with RNA Polymerase or through indirect interactions facilitated by adaptors, mediators, histone-modifying proteins, and nucleosome remodelers. Direct interactions to activate transcription is seen in bacteria as well as in some eukaryotic genes. In these cases, upstream activation sequences are adjacent to the promoters, and the activator proteins interact directly with the transcriptional machinery. For example, in...
11.0K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

15.7K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
15.7K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

8.2K
The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
8.2K
Viruses with RNA Genomes01:29

Viruses with RNA Genomes

943
RNA viruses are categorized into positive-strand, negative-strand, or double-stranded groups based on their genomic structure and replication mechanisms. This classification dictates how they exploit host cellular machinery for protein synthesis and replication. Some RNA viruses also utilize reverse transcription as part of their life cycle, further diversifying their replication strategies.Positive-Strand RNA VirusesPositive-strand RNA viruses have genomes that function directly as messenger...
943

You might also read

Related Articles

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

Sort by
Same author

ARISE: RNA-Anchored Shared-Edge Topology and Hierarchical Fusion for Spatial Multi-Omics Integration.

Bioinformatics (Oxford, England)·2026
Same author

Interpretable modality-aware mapping of gene regulation in single-cell multiomics with scMAGCA.

Nature communications·2026
Same author

Bridging sequence-structure motifs and genetic variants for genome-wide dynamic RNA-protein interaction profiling.

Nature communications·2026
Same author

Gero-LLM: A Multimodal Large Language Model for Geroprotector Discovery via Cross-Modal Differentiated Mutual Learning.

IEEE journal of biomedical and health informatics·2026
Same author

Accurate and interpretable ADMET prediction: Integrating structural, geometric, and global molecular context representations.

European journal of medicinal chemistry·2026
Same author

Orthogonal disentanglement of single-cell multi-omics reveals private and shared drivers of tissue development and pathogenesis.

Proceedings of the National Academy of Sciences of the United States of America·2026

Related Experiment Video

Updated: Feb 8, 2026

iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution
10:45

iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution

Published on: April 30, 2011

59.4K

Elucidating Genome-Wide Protein-RNA Interactions Using Differential Evolution.

Xiangtao Li, Ka-Chun Wong

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |July 11, 2018
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel differential evolution algorithm to optimize non-negative matrix factorization for RNA binding protein analysis. This method effectively determines the number of components, improving RNA binding pattern discovery from CLIP-seq data.

    More Related Videos

    Genome-wide Mapping of Protein-DNA Interactions with ChEC-seq in Saccharomyces cerevisiae
    10:43

    Genome-wide Mapping of Protein-DNA Interactions with ChEC-seq in Saccharomyces cerevisiae

    Published on: June 3, 2017

    11.7K
    Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions
    10:16

    Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions

    Published on: June 28, 2018

    33.5K

    Related Experiment Videos

    Last Updated: Feb 8, 2026

    iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution
    10:45

    iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution

    Published on: April 30, 2011

    59.4K
    Genome-wide Mapping of Protein-DNA Interactions with ChEC-seq in Saccharomyces cerevisiae
    10:43

    Genome-wide Mapping of Protein-DNA Interactions with ChEC-seq in Saccharomyces cerevisiae

    Published on: June 3, 2017

    11.7K
    Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions
    10:16

    Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions

    Published on: June 28, 2018

    33.5K

    Area of Science:

    • Computational Biology
    • Bioinformatics
    • Molecular Biology

    Background:

    • RNA-binding proteins (RBPs) are crucial for post-transcriptional RNA regulation.
    • Non-negative matrix factorization (NMF) is a powerful tool for identifying RNA binding patterns from multiple data sources.
    • Determining the optimal number of latent dimensions in NMF remains a significant challenge.

    Purpose of the Study:

    • To develop an adaptive model selection method for NMF in RBP analysis.
    • To address the limitations of trial-and-error approaches for selecting NMF components.
    • To improve the accuracy and efficiency of discovering RNA binding patterns.

    Main Methods:

    • Proposed a differential evolution algorithm for model selection in NMF.
    • Applied the algorithm to adaptively decompose protein-RNA data matrices into non-negative components.
    • Validated the method using 31 genome-wide cross-linking immunoprecipitation (CLIP-seq) datasets.

    Main Results:

    • The proposed differential evolution algorithm effectively selects the optimal number of ranks for NMF.
    • Achieved improved factorization quality compared to existing state-of-the-art methods.
    • Demonstrated robustness through comprehensive performance, time complexity, and parameter analyses.

    Conclusions:

    • The differential evolution algorithm offers a robust and efficient solution for NMF model selection in RBP studies.
    • This approach enhances the discovery of class-specific RNA binding patterns.
    • The findings are supported by extensive experimental validation on real-world biological data.