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

Next-generation Sequencing03:00

Next-generation Sequencing

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.

You might also read

Related Articles

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

Sort by
Same author

Neighborhood-level socioeconomic disadvantage is associated with gut microbial composition and diversity across many chronic disease states.

Frontiers in public health·2026
Same author

Urobiome composition varies with vesicoureteral reflux grade and history of febrile urinary tract infection.

Scientific reports·2026
Same author

<i>Special Issue:</i> 13th International Conference on Computational Advances in Bio and Medical Sciences.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same author

Loss of nsp14-exonuclease activity impairs the replication, proofreading, fitness, and pathogenesis of SARS-CoV-2.

mBio·2026
Same author

Erratum for Yuan et al., "Case-control and genomic epidemiology characterization of SARS-CoV-2 breakthrough infections during Delta-to-Omicron transition".

mBio·2026
Same author

Factors associated with protection from MASLD in type 2 diabetes: A prospective study integrating longitudinal MRI/MRE and stable isotope tracing.

JHEP reports : innovation in hepatology·2026

Related Experiment Video

Updated: Jun 10, 2026

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens
09:14

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

ANDES: Statistical tools for the ANalyses of DEep Sequencing.

Kelvin Li1, Eli Venter, Shibu Yooseph

  • 1The J, Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. kli@jcvi.org.

BMC Research Notes
|July 17, 2010
PubMed
Summary

ANDES is a new software tool for analyzing deep sequencing data. It provides statistical methods and visualizations for comparing multiple samples, enabling more efficient analysis of large genomic datasets.

More Related Videos

Introductory Analysis and Validation of CUT&#38;RUN Sequencing Data
04:58

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Published on: December 13, 2024

Related Experiment Videos

Last Updated: Jun 10, 2026

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens
09:14

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

Introductory Analysis and Validation of CUT&#38;RUN Sequencing Data
04:58

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Published on: December 13, 2024

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Advancements in DNA sequencing enable deep sequencing of organism samples.
  • Detecting subtle variations within and between species is now possible.
  • Traditional multiple sequence alignment (MSA) methods become cumbersome with deep sequencing data.

Purpose of the Study:

  • To develop a software library and applications for the statistical analysis of deep sequencing data.
  • To provide tools for efficient comparison and analysis of large, homogeneous sequence datasets.

Main Methods:

  • Developed ANDES (ANalyses of DEep Sequencing), a Perl and R software package.
  • Utilizes a position profile data structure representing nucleotide distributions from MSA.
  • Includes tools for RMSD plots, base conversion frequencies, Shannon entropy, clustering, and polymorphism detection.

Main Results:

  • ANDES facilitates visual comparison of multiple samples using RMSD plots.
  • Enables computation of variation, clustering, and consensus sequence generation.
  • Provides empirically determined sequencing quality values.

Conclusions:

  • ANDES offers a solution for the increasing need for inter- and intra-sample comparisons in deep sequencing.
  • The software package is demonstrated on various datasets and available for download.
  • Facilitates efficient analysis as deep sequencing becomes more cost-effective.