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

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...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

You might also read

Related Articles

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

Sort by
Same author

RNU4ATAC-opathy: Clinical, molecular and transcriptomic insights from a large cohort.

Genetics in medicine : official journal of the American College of Medical Genetics·2026
Same author

Phenotype-Specific Recalibration of MAVE Data Enables Repurposing of <i>BAP1</i> Functional Assays for Küry-Isidor Syndrome.

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

Building an Interoperable Rare Disease Multi-omic Resource: The GREGoR Data Model and Dataset.

bioRxiv : the preprint server for biology·2026
Same author

Genome-wide detection and clinical prioritization of tandem repeat outliers using long-read sequencing.

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

Telomeric amplicons of <i>SUL1</i> and Y' in yeast are generated by microhomology-mediated break induced replication occurring <i>in cis</i>.

bioRxiv : the preprint server for biology·2026
Same author

A comprehensive assessment of tandem repeat genotyping methods for Nanopore long-read genomes.

bioRxiv : the preprint server for biology·2026
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026
Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026
Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026
Same journal

Primer Design through Submodular Function Estimation.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Jun 19, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

needLR: Long-read structural variant annotation with population-scale frequency estimation.

Jonas A Gustafson1,2, Jiadong Lin3, Miranda P G Zalusky2

  • 1Department of Molecular and Cellular Biology, University of Washington, Seattle, WA 98195, United States.

Bioinformatics (Oxford, England)
|June 17, 2026
PubMed
Summary
This summary is machine-generated.

needLR is a new tool that filters and prioritizes structural variants (SVs) from long-read sequencing data. It effectively reduces candidate SVs while preserving known pathogenic variants, aiding genetic research.

More Related Videos

Ultra-long Read Sequencing for Whole Genomic DNA Analysis
10:34

Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Published on: March 15, 2019

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Related Experiment Videos

Last Updated: Jun 19, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Ultra-long Read Sequencing for Whole Genomic DNA Analysis
10:34

Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Published on: March 15, 2019

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Structural variants (SVs) are crucial in genetic variation and disease.
  • Analyzing SVs from long-read sequencing data presents challenges in filtering and prioritization.

Purpose of the Study:

  • To introduce needLR, a novel tool for annotating and prioritizing structural variants (SVs).
  • To enhance the filtering of candidate pathogenic SVs identified through long-read sequencing.

Main Methods:

  • needLR utilizes population allele frequencies, genomic context annotations, and gene-phenotype associations.
  • Implementation in bash with dependencies on Truvari, BEDTools, and BCFtools.

Main Results:

  • needLR assigned allele frequencies to over 97.5% of detected SVs using population data from 500 individuals.
  • The tool reduced the average number of novel genic SVs to 121 per case.
  • All known pathogenic variants were successfully retained in test cases.

Conclusions:

  • needLR is an effective tool for filtering and prioritizing candidate pathogenic SVs from long-read sequencing data.
  • The tool aids in reducing the complexity of SV analysis, improving diagnostic yield.