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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. 
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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.
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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.

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Last Updated: Jun 19, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Published on: August 3, 2018

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10:34

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Published on: March 15, 2019

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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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.