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

12.7K
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...
12.7K
Next-generation Sequencing03:00

Next-generation Sequencing

102.3K
The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
102.3K

You might also read

Related Articles

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

Sort by
Same author

Transcriptomic insights into the co-occurring psychological symptoms and cardiovascular risks among military service members and veterans with mild traumatic brain injury: A LIMBIC-CENC study.

Brain, behavior, & immunity - health·2026
Same author

Human pain transcriptomics: lessons learned so far.

Pain reports·2026
Same author

Association of Marshall CT Scores with GFAP, UCH-L1, Tau, NfL, and p-Tau231 After Traumatic Brain Injury.

International journal of molecular sciences·2025
Same author

Editorial: Integrated diagnostics and biomarker discovery in endocrinology and biomedical sciences, volume II.

Frontiers in endocrinology·2025
Same author

NEMF-mediated CAT tailing facilitates translocation-associated quality control.

The Journal of cell biology·2025
Same author

Transcriptome analysis of rheumatoid arthritis uncovers genes linked to inflammation-induced pain.

Scientific reports·2024
Same journal

Model-based quantification of protein-protein interaction aberrations for exploring dysregulated signalling pathways through pathway maps and gene expression levels.

BMC bioinformatics·2026
Same journal

Research on multi-trait genome association study method based on Shannon information entropy.

BMC bioinformatics·2026
Same journal

A multi-view feature fusion framework with interpretable graph convolution for predicting microbe-drug associations.

BMC bioinformatics·2026
Same journal

Covariance decomposition for distance based species tree estimation.

BMC bioinformatics·2026
Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026
Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Apr 19, 2026

Targeted DNA Methylation Analysis by Next-generation Sequencing
08:38

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

38.3K

Masking as an effective quality control method for next-generation sequencing data analysis.

Sajung Yun, Sijung Yun1

  • 1Laboratory of Molecular Biology, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA. yuns@mail.nih.gov.

BMC Bioinformatics
|December 16, 2014
PubMed
Summary
This summary is machine-generated.

Masking is a superior preprocessing method for next-generation sequencing data, effectively reducing false positives in single nucleotide polymorphism (SNP) identification without impacting accuracy. This method is recommended over trimming for variant calling.

More Related Videos

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
07:30

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

13.0K
Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
07:21

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Published on: August 25, 2018

13.6K

Related Experiment Videos

Last Updated: Apr 19, 2026

Targeted DNA Methylation Analysis by Next-generation Sequencing
08:38

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

38.3K
Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
07:30

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

13.0K
Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
07:21

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Published on: August 25, 2018

13.6K

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) generates base calls with low quality scores, potentially impacting the accuracy of simple nucleotide variation (SNV) detection.
  • SNVs include single nucleotide polymorphisms (SNPs) and small insertions/deletions (indels).
  • Data preprocessing methods like masking and trimming are employed to address low-quality base calls in NGS data.

Purpose of the Study:

  • To compare the effectiveness of masking versus trimming for preprocessing whole-genome sequence data.
  • To evaluate the accuracy of simple nucleotide variation calls after applying these preprocessing methods.
  • To determine the optimal preprocessing strategy for improving variant calling accuracy in NGS data.

Main Methods:

  • Whole-genome sequence data from Caenorhabditis elegans was analyzed.
  • Two preprocessing methods were compared: masking (replacing low-quality bases with 'N') and trimming (removing low-quality bases).
  • The accuracy of single nucleotide polymorphism (SNP) and small indel calls was assessed by comparing false-positive and false-negative rates.

Main Results:

  • Masking demonstrated superior performance over trimming in reducing the false-positive rate for SNP calling.
  • Neither masking nor trimming significantly affected the false-negative rate for SNP calling compared to no preprocessing.
  • No significant differences in false-positive or false-negative rates for small indels were observed between masking and trimming.

Conclusions:

  • Masking is recommended as a more effective preprocessing method for NGS data analysis than trimming.
  • Masking reduces the false-positive rate in SNP calling without compromising the false-negative rate.
  • A perl script for masking is available, and the sequencing data is publicly accessible.