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

piRNA - Piwi-interacting RNAs02:57

piRNA - Piwi-interacting RNAs

6.0K
PIWI-interacting RNAs, or piRNAs, are the most abundant short non-coding RNAs. More than 20,000 genes have been found in humans that code for piRNAs while only 2000 genes have been found for miRNAs. piRNAs can act at the transcriptional and post-transcriptional levels and have a vital role in silencing transposable elements present in germ cells. They are also involved in epigenetic silencing and activation. Previously, they were thought to function only in germ cells but new evidence suggests...
6.0K
RNA Interference01:23

RNA Interference

24.2K
RNA interference (RNAi) is a process in which a small non-coding RNA molecule blocks the post-transcriptional expression of a gene by binding to its messenger RNA (mRNA) and preventing the protein from being translated.
This process occurs naturally in cells, often through the activity of genomically-encoded microRNAs. Researchers can take advantage of this mechanism by introducing synthetic RNAs to deactivate specific genes for research or therapeutic purposes. For example, RNAi could be used...
24.2K
siRNA - Small Interfering RNAs02:30

siRNA - Small Interfering RNAs

13.4K
Small interfering RNAs, or siRNAs, are short regulatory RNA molecules that can silence genes post-transcriptionally, as well as the transcriptional level in some cases. siRNAs are important for protecting cells against viral infections and silencing transposable genetic elements.
In the cytoplasm, siRNA is processed from a double-stranded RNA, which comes from either endogenous DNA transcription or exogenous sources like a virus. This double-stranded RNA is then cleaved by the...
13.4K
Experimental RNAi02:15

Experimental RNAi

6.5K
RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...
6.5K

You might also read

Related Articles

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

Sort by
Same author

scProca: A Cross-Attention-Enhanced Deep Generative Model for Single-Cell Transcriptomics and Proteomics Integration and Imputation.

IEEE journal of biomedical and health informatics·2025
Same author

scVIC: deep generative modeling of heterogeneity for scRNA-seq data.

Bioinformatics advances·2024
Same author

RobustTree: An adaptive, robust PCA algorithm for embedded tree structure recovery from single-cell sequencing data.

Frontiers in genetics·2023
Same author

DeepDetect: Deep Learning of Peptide Detectability Enhanced by Peptide Digestibility and Its Application to DIA Library Reduction.

Analytical chemistry·2023
Same author

A repository for the publication and sharing of heterogeneous materials data.

Scientific data·2022
Same author

BiTSC 2: Bayesian inference of tumor clonal tree by joint analysis of single-cell SNV and CNA data.

Briefings in bioinformatics·2022

Related Experiment Video

Updated: Apr 23, 2026

Identification of Footprints of RNA:Protein Complexes via RNA Immunoprecipitation in Tandem Followed by Sequencing RIPiT-Seq
09:26

Identification of Footprints of RNA:Protein Complexes via RNA Immunoprecipitation in Tandem Followed by Sequencing RIPiT-Seq

Published on: July 10, 2019

10.0K

piRNA identification based on motif discovery.

Xiuqin Liu1, Jun Ding, Fuzhou Gong

  • 1School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China. mathlxq@163.com.

Molecular Biosystems
|September 19, 2014
PubMed
Summary
This summary is machine-generated.

A new computational method, Pibomd, accurately identifies piRNA sequences using motif discovery and machine learning. This tool aids research into piwi-interacting RNA (piRNA) biogenesis and function.

More Related Videos

Enhanced Northern Blot Detection of Small RNA Species in Drosophila Melanogaster
09:39

Enhanced Northern Blot Detection of Small RNA Species in Drosophila Melanogaster

Published on: August 21, 2014

22.0K
Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions
10:52

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Published on: September 28, 2017

7.4K

Related Experiment Videos

Last Updated: Apr 23, 2026

Identification of Footprints of RNA:Protein Complexes via RNA Immunoprecipitation in Tandem Followed by Sequencing RIPiT-Seq
09:26

Identification of Footprints of RNA:Protein Complexes via RNA Immunoprecipitation in Tandem Followed by Sequencing RIPiT-Seq

Published on: July 10, 2019

10.0K
Enhanced Northern Blot Detection of Small RNA Species in Drosophila Melanogaster
09:39

Enhanced Northern Blot Detection of Small RNA Species in Drosophila Melanogaster

Published on: August 21, 2014

22.0K
Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions
10:52

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Published on: September 28, 2017

7.4K

Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Piwi-interacting RNA (piRNA) are small non-coding RNAs crucial for germline development and epigenetic regulation.
  • Identifying piRNA loci is vital for understanding piRNA biogenesis and function.

Purpose of the Study:

  • To develop a computational algorithm for accurate piRNA identification.
  • To leverage motif discovery for piRNA sequence feature extraction.

Main Methods:

  • Applied the Teiresias tool to identify variable-length motifs in mouse piRNA and non-piRNA sequences.
  • Developed the Pibomd algorithm using Support Vector Machine (SVM) with identified sequence motifs.
  • Trained an unbalanced SVM classifier, Asym-Pibomd, for enhanced specificity.

Main Results:

  • Pibomd achieved 91.48% sensitivity and 89.76% specificity on a mouse test dataset.
  • Asym-Pibomd demonstrated higher specificity (96.2%) and improved accuracy (84.44%) compared to the k-mer method.
  • Motif analysis suggested sequence information at the 5' and/or 3' ends of piRNAs is recognized by processing machinery.

Conclusions:

  • The Pibomd algorithm offers a significant improvement over existing methods for piRNA identification.
  • The findings provide insights into the structural features of piRNA precursors.
  • A user-friendly web server for Pibomd is available at http://app.aporc.org/Pibomd/.