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

Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

11.8K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
11.8K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

7.2K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
7.2K
Genome Copying Errors02:46

Genome Copying Errors

5.4K
DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their  survival. Therefore, the copying errors are checked and repaired at three levels.
5.4K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

2.1K
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...
2.1K
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
Errors in Taping01:18

Errors in Taping

418
Errors in taping arise from multiple factors that can significantly impact measurement accuracy in surveying. Misalignment of the tape, often due to human error, is one primary source. A skilled rear tapeman, using a telescope, can help correct alignment by guiding the head tapeman; however, human limitations still lead to small inaccuracies. These errors may include misplacement of pins or inaccurate tape readings due to common visual confusions, such as mistaking a six for a nine. Such...
418

You might also read

Related Articles

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

Sort by
Same author

EV-Planner: a machine learning approach to electric vehicle charging infrastructure planning.

Frontiers in artificial intelligence·2026
Same author

Learning to take it personally: Precision drug repurposing through patient-specific loss on knowledge graphs using Biobank data.

Journal of biomedical informatics·2026
Same author

scProfiterole: Clustering of Single-Cell Proteomic Data Using Graph Contrastive Learning via Spectral Filters.

bioRxiv : the preprint server for biology·2026
Same author

What Does Next-Generation Mass Spectrometry Offer for Proteomics? A Comprehensive Platform Comparison.

Journal of proteome research·2026
Same author

Interpretable Machine Learning to Identify Risk Factors for Recidivism in Intimate Partner Violence.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same author

Mutational signatures in hematological malignancies.

Einstein (Sao Paulo, Brazil)·2026

Related Experiment Video

Updated: Mar 18, 2026

Proofreading and DNA Repair Assay Using Single Nucleotide Extension and MALDI-TOF Mass Spectrometry Analysis
11:08

Proofreading and DNA Repair Assay Using Single Nucleotide Extension and MALDI-TOF Mass Spectrometry Analysis

Published on: June 19, 2018

10.3K

Pluribus-Exploring the Limits of Error Correction Using a Suffix Tree.

Daniel Savel, Thomas LaFramboise, Ananth Grama

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |July 1, 2016
    PubMed
    Summary
    This summary is machine-generated.

    Pluribus, a new method using generalized suffix tries, effectively corrects next-generation sequencing errors. It minimizes false positives and improves genome assembly quality, outperforming existing techniques.

    More Related Videos

    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
    Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms
    09:30

    Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

    Published on: September 13, 2018

    10.0K

    Related Experiment Videos

    Last Updated: Mar 18, 2026

    Proofreading and DNA Repair Assay Using Single Nucleotide Extension and MALDI-TOF Mass Spectrometry Analysis
    11:08

    Proofreading and DNA Repair Assay Using Single Nucleotide Extension and MALDI-TOF Mass Spectrometry Analysis

    Published on: June 19, 2018

    10.3K
    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
    Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms
    09:30

    Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

    Published on: September 13, 2018

    10.0K

    Area of Science:

    • Genomics
    • Bioinformatics

    Background:

    • Next-generation sequencing (NGS) technologies offer efficient genome sequencing but introduce errors.
    • Sequencing errors complicate de novo assembly and reduce sequence quality.
    • Existing error correction methods often rely on substring frequencies.

    Purpose of the Study:

    • To introduce Pluribus, a novel method for correcting sequencing errors.
    • To evaluate Pluribus's performance against existing error correction techniques.
    • To demonstrate Pluribus's contribution to improved genome assembly quality.

    Main Methods:

    • Development of Pluribus, a novel error correction method.
    • Utilizing a generalized suffix trie to identify and correct sequencing errors.
    • Leveraging multiple error manifestations within the trie for accurate correction.

    Main Results:

    • Pluribus demonstrated the lowest number of false positives across diverse real sequencing datasets.
    • Pluribus achieved higher accuracy when used in conjunction with other error correction methods.
    • Improvements in error correction accuracy directly translated to enhanced contig quality in genome assembly.

    Conclusions:

    • Pluribus is an effective tool for correcting sequencing errors in NGS data.
    • The method offers superior performance in minimizing false positives compared to existing approaches.
    • Pluribus enhances genome assembly quality and can be combined with other tools for synergistic improvements.