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Related Concept Videos

Genome Copying Errors02:46

Genome Copying Errors

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.
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
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A survey team is tasked with determining the elevation difference between points Point A and Point B, separated by uneven terrain. They use a leveling instrument and a leveling rod.Common MistakesMisreading the Rod: During a backsight reading at Point A, the instrumentman observes the rod partially obscured by tall grass. Instead of reading 1.135 m, they mistakenly record 1.735 m due to the misalignment of the crosshair with the wrong graduation. This error adds 0.600 m to all subsequent...
Random and Systematic Errors01:20

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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Random and Systematic Errors01:20

Random and Systematic Errors

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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Updated: May 11, 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

DRISEE overestimates errors in metagenomic sequencing data.

A Murat Eren, Hilary G Morrison, Susan M Huse

    Briefings in Bioinformatics
    |May 24, 2013
    PubMed
    Summary
    This summary is machine-generated.

    High error rates in metagenomic sequencing data may be overestimated. Conserved artificial sequences and natural motifs, not actual errors, likely cause inflated results from tools like DRISEE, especially for Illumina data.

    Keywords:
    PCRadapter ligationnext-generation sequencingquality scoresequencing error

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    Published on: August 25, 2018

    Area of Science:

    • Bioinformatics
    • Genomics
    • Computational Biology

    Background:

    • Metagenomic sequencing generates vast datasets.
    • Accurate error detection is crucial for reliable analysis.
    • Previous tools reported high error rates in next-generation sequencing data.

    Purpose of the Study:

    • To re-examine high error rates reported by Keegan et al. for metagenomic sequencing data.
    • To identify the cause of inflated error rates.
    • To evaluate the reliability of error detection tools.

    Main Methods:

    • Analysis of next-generation sequencing datasets.
    • Investigation of conserved artificial sequences (e.g., Illumina adapters).
    • Examination of naturally occurring sequence motifs.

    Main Results:

    • Inflated sequencing error rates reported by DRISEE are primarily due to conserved artificial sequences and natural sequence motifs.
    • Illumina sequencing data showed particularly high levels of reported errors.
    • The presence of adapter sequences significantly impacts error rate calculations.

    Conclusions:

    • The DRISEE tool overestimates sequencing errors, especially for Illumina data.
    • Tools for evaluating large sequencing datasets require rigorous validation.
    • Careful consideration of sequence artifacts is necessary for accurate metagenomic analysis.