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

Ribosome Profiling02:24

Ribosome Profiling

3.2K
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...
3.2K
Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

833
Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
833
Applications of Molecular Taxonomy01:20

Applications of Molecular Taxonomy

705
Molecular taxonomy has revolutionized the understanding and classification of bacteria, providing precise insights into their diversity, evolutionary relationships, and ecological roles. By utilizing molecular techniques such as DNA sequencing and fingerprinting, researchers have made significant strides in various fields related to bacterial studies.Resolving Taxonomic AmbiguitiesMolecular taxonomy has been instrumental in distinguishing closely related bacterial species initially thought to...
705
RNA-seq03:21

RNA-seq

9.3K
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...
9.3K

You might also read

Related Articles

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

Sort by
Same author

Substrate scope of ancestral versus modern family-1 glycosidases.

Protein science : a publication of the Protein Society·2026
Same author

Chronic lymphocytic leukemia with <i>IGH</i>::<i>BCL3</i>-translocation is characterized by a homogeneous and distinct genetic and epigenetic landscape.

HemaSphere·2026
Same author

Toward Robust Machine Learning Models for MALDI-TOF MS: Novel Approaches for <i>Mycobacterium abscessus</i> Subspecies Identification.

Journal of proteome research·2026
Same author

<i>In silico</i> prediction of the impact of genomic variations in the small conductance calcium activated potassium channel SK3 structure and function.

Frontiers in neuroscience·2025
Same author

Unravelling the human taste receptor interactome: machine learning and molecular modelling insights into protein-protein interactions.

NPJ science of food·2025
Same author

Subtyping Burkitt Lymphoma by DNA Methylation.

Genes, chromosomes & cancer·2025

Related Experiment Video

Updated: Apr 27, 2026

MS2-Affinity Purification Coupled with RNA Sequencing in Gram-Positive Bacteria
08:34

MS2-Affinity Purification Coupled with RNA Sequencing in Gram-Positive Bacteria

Published on: February 23, 2021

6.6K

A multiobjective method for robust identification of bacterial small non-coding RNAs.

Javier Arnedo1, Rocío Romero-Zaliz2, Igor Zwir3

  • 1Department of Computer Science and Artificial Intelligence, Universidad de Granada, Granada 18071, Spain, Instituto de Investigación Biosanitaria ibs.GRANADA. Hospitales Universitarios de Granada/Universidad de Granada, Granada, Spain and Department of Psychiatry at Washington University, St. Louis, MO 63130, USA.

Bioinformatics (Oxford, England)
|June 25, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a new computational method to identify bacterial small non-coding RNAs (sRNAs). The approach optimally combines existing prediction tools, improving accuracy across diverse bacterial genomes.

More Related Videos

Identification of Rare Bacterial Pathogens by 16S rRNA Gene Sequencing and MALDI-TOF MS
06:34

Identification of Rare Bacterial Pathogens by 16S rRNA Gene Sequencing and MALDI-TOF MS

Published on: July 11, 2016

17.1K
Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs
08:49

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Published on: September 16, 2019

7.3K

Related Experiment Videos

Last Updated: Apr 27, 2026

MS2-Affinity Purification Coupled with RNA Sequencing in Gram-Positive Bacteria
08:34

MS2-Affinity Purification Coupled with RNA Sequencing in Gram-Positive Bacteria

Published on: February 23, 2021

6.6K
Identification of Rare Bacterial Pathogens by 16S rRNA Gene Sequencing and MALDI-TOF MS
06:34

Identification of Rare Bacterial Pathogens by 16S rRNA Gene Sequencing and MALDI-TOF MS

Published on: July 11, 2016

17.1K
Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs
08:49

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

Published on: September 16, 2019

7.3K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Small non-coding RNAs (sRNAs) are crucial for post-transcriptional regulation in prokaryotes.
  • Current experimental validation methods for sRNAs are limited in scope and species coverage.
  • Developing robust computational algorithms is essential for predicting sRNAs.

Purpose of the Study:

  • To develop a novel computational methodology for identifying bacterial sRNAs.
  • To enhance sRNA prediction accuracy by optimally aggregating existing prediction methods.
  • To create a robust prediction system applicable across diverse bacterial genomes.

Main Methods:

  • Incorporation and optimal aggregation of knowledge from different sRNA prediction methods.
  • Utilizing multiobjective optimization techniques to find trade-off solutions between specificity and sensitivity.
  • Development of a multiclassifier system for robust sRNA detection.

Main Results:

  • The novel methodology successfully predicts sRNAs in different bacterial species, including Sinorhizobium meliloti.
  • The approach demonstrates robustness even in genomes with varying nucleotide compositions.
  • The method shows promise for predicting sRNAs in poorly annotated species.

Conclusions:

  • The proposed methodology offers a robust approach for bacterial sRNA identification.
  • Optimal aggregation of prediction tools enhances individual method merits.
  • This meta-analysis-like approach can form a foundation for broad predictive capabilities across genomic variability.