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Updated: Feb 19, 2026

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Identifying structural variants using linked-read sequencing data.

Rebecca Elyanow¹, Hsin-Ta Wu¹, Benjamin J Raphael²

¹Center for Computational Molecular Biology, Brown University, Providence, RI, USA.

Bioinformatics (Oxford, England)

|November 8, 2017

Summary

This summary is machine-generated.

NAIBR is a new algorithm for identifying structural variations in genomes using linked-read sequencing. It outperforms existing methods, aiding in cancer gene discovery.

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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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Published on: June 23, 2012

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Structural variations are common in human and cancer genomes.
Identifying structural variants is challenging due to repetitive genomic regions and short-read limitations.
Linked-read sequencing technology offers long-range genomic information beneficial for variant identification.

Purpose of the Study:

To present NAIBR, a novel algorithm for structural variant identification using linked-read sequencing data.
To evaluate NAIBR's performance against existing methods on simulated and real genomic data.

Main Methods:

Developed NAIBR, a probabilistic algorithm utilizing multiple signals from barcoded reads.
Applied NAIBR to simulated data, NA12878 human genome, and HCC1954 cancer cell line data.
Compared NAIBR's performance with existing structural variant identification methods.

Main Results:

NAIBR demonstrates superior performance in identifying structural variants compared to several existing methods.
The algorithm successfully identified novel structural variants in linked-read sequencing data.
Several novel somatic structural variants identified in the HCC1954 cell line overlap with known cancer genes.

Conclusions:

NAIBR is an effective tool for structural variant identification in linked-read sequencing data.
The algorithm aids in discovering novel structural variants, including those relevant to cancer.
NAIBR advances the reliable identification of complex genomic rearrangements.