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

Uncertainty: Overview00:59

Uncertainty: Overview

1.9K
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
1.9K
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
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

2.2K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
2.2K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.6K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.6K
Sanger Sequencing01:57

Sanger Sequencing

781.1K
DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
781.1K
Next-generation Sequencing03:00

Next-generation Sequencing

102.2K
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....
102.2K

You might also read

Related Articles

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

Sort by
Same author

<i>Letter:</i> Psychosis Following Hormonal Therapy in NAA15-Related Neurodevelopmental Disorder.

Journal of child and adolescent psychopharmacology·2026
Same author

Phenotypic variability in female individuals with the NAA10 missense variants p.(L126R), p.(L126V), or p.(F128L) leading to NAA10-related syndrome.

Molecular and cellular pediatrics·2026
Same author

Pathogenic variants in the cohesin loader subunit MAU2 underlie a distinct Cornelia de Lange Syndrome subtype.

Nature communications·2026
Same author

Functional Data Strengthen Clinical Validation of PhenoScore Phenotype-Guided AI for ANKRD11 Missense Variants.

Clinical genetics·2026
Same author

Generation of a male isogenic pair and a female isogenic pair(R83C) for studying NAA10-related syndrome as part of a large Ogden syndrome biobank.

Stem cell research·2026
Same author

Pathogenic variants in the cohesin loader subunit MAU2 lead to a new Cornelia de Lange Syndrome subtype.

medRxiv : the preprint server for health sciences·2025

Related Experiment Video

Updated: Apr 18, 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

12.7K

Accounting for uncertainty in DNA sequencing data.

Jason A O'Rawe1, Scott Ferson2, Gholson J Lyon3

  • 1Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, NY, USA; Stony Brook University, Stony Brook, NY, USA; Applied Biomathematics, Setauket, NY, USA.

Trends in Genetics : TIG
|January 13, 2015
PubMed
Summary

Uncertainty quantification in DNA sequencing is crucial for reliable biological conclusions. This review addresses errors and proposes methods to propagate uncertainties from high-throughput sequencing data.

Keywords:
DNA sequencingsequence errorsuncertaintyuncertainty accounting

More Related Videos

Detection of Copy Number Alterations Using Single Cell Sequencing
09:45

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

12.3K
Validating Whole Genome Nanopore Sequencing, using Usutu Virus as an Example
05:45

Validating Whole Genome Nanopore Sequencing, using Usutu Virus as an Example

Published on: March 11, 2020

9.5K

Related Experiment Videos

Last Updated: Apr 18, 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

12.7K
Detection of Copy Number Alterations Using Single Cell Sequencing
09:45

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

12.3K
Validating Whole Genome Nanopore Sequencing, using Usutu Virus as an Example
05:45

Validating Whole Genome Nanopore Sequencing, using Usutu Virus as an Example

Published on: March 11, 2020

9.5K

Area of Science:

  • Genomics and Molecular Biology
  • Bioinformatics and Computational Biology
  • Statistical Science

Background:

  • High-throughput DNA sequencing generates vast amounts of data with inherent platform-specific errors and biases.
  • Current statistical studies often measure basic error rates but lack general schemes to project uncertainties.
  • Assessing the reliability of conclusions in genetic and epigenetic research is challenging due to unquantified uncertainties.

Purpose of the Study:

  • To review the current state of uncertainty quantification in DNA sequencing applications.
  • To describe the various sources of errors and biases in DNA sequencing technologies.
  • To propose methods for accounting and propagating uncertainties through subsequent analyses.

Main Methods:

  • Literature review of uncertainty quantification in DNA sequencing.
  • Identification and categorization of error sources across different sequencing platforms.
  • Exploration of statistical methods for error propagation.

Main Results:

  • A comprehensive overview of uncertainty sources in DNA sequencing is presented.
  • Existing methods for error rate measurement are insufficient for comprehensive uncertainty propagation.
  • Proposed methods aim to provide a framework for robust uncertainty assessment.

Conclusions:

  • Accurate quantification and propagation of uncertainties are essential for the reliability of DNA sequencing-based biological conclusions.
  • Standardized methods are needed to address the diverse error profiles of various sequencing technologies.
  • Implementing proposed methods will enhance the trustworthiness of findings in genetic, epigenetic, and broader biological studies.