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Related Concept Videos

Bias01:22

Bias

Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
RNA-seq03:21

RNA-seq

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 microarray-based...
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Related Experiment Video

Updated: May 11, 2026

Strand-Specific Analysis of Proteins at Replicating DNA Strands by Enrichment and Sequencing of Protein-Associated Nascent DNA Method
08:53

Strand-Specific Analysis of Proteins at Replicating DNA Strands by Enrichment and Sequencing of Protein-Associated Nascent DNA Method

Published on: May 2, 2025

Characterizing and measuring bias in sequence data.

Michael G Ross, Carsten Russ, Maura Costello

    Genome Biology
    |May 31, 2013
    PubMed
    Summary
    This summary is machine-generated.

    Computational methods were developed to measure DNA sequencing bias across platforms like Illumina and Pacific Biosciences. These tools help improve genome assemblies and coverage, especially in challenging GC-rich regions.

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    Area of Science:

    • Genomics
    • Bioinformatics
    • Computational Biology

    Background:

    • DNA sequencing technologies exhibit non-uniform read distribution, known as bias.
    • This bias negatively impacts scientific and medical applications.
    • Computational methods are crucial for addressing these limitations.

    Purpose of the Study:

    • To develop and apply computational methods for detecting, characterizing, and quantifying DNA sequencing bias.
    • To evaluate bias across major sequencing platforms.
    • To identify strategies for mitigating sequencing bias.

    Main Methods:

    • Development of computational assays to measure bias.
    • Application of methods to Illumina, Ion Torrent, Pacific Biosciences, and Complete Genomics platforms.
    • Analysis of human and microbial DNA sequencing data with varying base compositions.

    Main Results:

    • Pacific Biosciences and Illumina platforms showed lower bias compared to others.
    • All technologies exhibited error-rate biases in GC-rich regions and homopolymer runs.
    • Combining complementary technologies can reduce coverage bias.
    • Unexplained poor coverage was identified in a small fraction of the human genome.

    Conclusions:

    • The developed assays offer a comprehensive assessment of sequencing bias.
    • These methods can guide laboratory improvements and monitor production.
    • Application of these assays will lead to enhanced genome assemblies and better coverage of critical genomic regions.