Search research articles

Related Concept Videos

Comparing Copy Number Variations and SNPs

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

Genealogy-based trait association with LOCATER boosts power at loci with allelic heterogeneity.

Genome research·2026

Same author

Comparison of variant callers using 60 532 multi-ancestry whole genome sequences.

Briefings in bioinformatics·2026

Same author

Genealogy based trait association with LOCATER boosts power at loci with allelic heterogeneity.

medRxiv : the preprint server for health sciences·2025

Same author

Single cell variant to enhancer to gene map for coronary artery disease.

medRxiv : the preprint server for health sciences·2024

Same author

Whole-genome sequencing uncovers two loci for coronary artery calcification and identifies ARSE as a regulator of vascular calcification.

Nature cardiovascular research·2024

Same author

A draft human pangenome reference.

Nature·2023

Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026

Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026

Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026

Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026

Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026

Same journal

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

Bioinformatics (Oxford, England)·2026

See all related articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Apr 30, 2026

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

SAMBLASTER: fast duplicate marking and structural variant read extraction.

Gregory G Faust¹, Ira M Hall²

¹Department of Biochemistry and Molecular Genetics and Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA.

Bioinformatics (Oxford, England)

|May 10, 2014

Summary

This summary is machine-generated.

SAMBLASTER is a new tool that optimizes DNA sequencing analysis by efficiently marking duplicate reads in large BAM files. This significantly reduces computational time and complexity in bioinformatic pipelines.

More Related Videos

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Related Experiment Videos

Last Updated: Apr 30, 2026

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Area of Science:

Genomics
Bioinformatics

Background:

Illumina DNA sequencing generates vast amounts of genomic data, overwhelming current bioinformatic pipelines.
Processing large BAM files repeatedly is a major bottleneck, increasing analysis time and resource consumption.

Purpose of the Study:

To introduce SAMBLASTER, a novel tool designed to streamline DNA sequencing data analysis.
To reduce the computational overhead associated with handling large BAM files in genomic pipelines.

Main Methods:

SAMBLASTER functions as a post-processing step for DNA aligner output, marking duplicates in read-sorted SAM files before BAM compression.
It can concurrently extract discordant read-pairs and split-read mappings for structural variant detection.
Implemented in open-source C++.

Main Results:

SAMBLASTER significantly reduces the number of costly file operations (read, write, sort, compress) in BAM file processing.
Its runtime overhead as an alignment post-pass is negligible, improving overall pipeline efficiency.
Outperforms existing tools like PICARD and SAMBAMBA in speed and memory usage for duplicate marking, with comparable accuracy.

Conclusions:

SAMBLASTER offers a substantial improvement in the efficiency and speed of genomic data analysis pipelines.
The tool simplifies pipeline complexity and reduces overall runtime, addressing a critical bottleneck in bioinformatics.
It provides a faster and more memory-efficient alternative for duplicate marking in large-scale sequencing data analysis.