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Related Concept Videos

Bias01:22

Bias

4.2K
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...
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Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

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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:  
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Statistical Analysis: Overview01:11

Statistical Analysis: Overview

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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
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Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

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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.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
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Regression Toward the Mean01:52

Regression Toward the Mean

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Blind Procedures02:07

Blind Procedures

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Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...
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Related Experiment Video

Updated: Jun 28, 2025

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

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Measuring, visualizing, and diagnosing reference bias with biastools.

Mao-Jan Lin1, Sheila Iyer2, Nae-Chyun Chen2

  • 1Department of Computer Science, Johns Hopkins University, Baltimore, USA. mlin77@jhu.edu.

Genome Biology
|April 19, 2024
PubMed
Summary
This summary is machine-generated.

Biastools is a new method to measure reference bias in bioinformatics. It reveals that inclusive graph genomes and end-to-end alignment reduce bias, while T2T references improve large-scale bias.

Keywords:
PangenomicsReference biasSequence alignment

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Bioinformatics methods aim to reduce reference bias but lack comprehensive measurement tools.
  • Reference bias can affect the accuracy of genomic analyses.

Purpose of the Study:

  • To introduce Biastools, a novel method for analyzing and categorizing reference bias.
  • To evaluate the impact of different genomic references and alignment strategies on reference bias.

Main Methods:

  • Biastools was developed to analyze reference bias across various scenarios, including simulated and real sequencing reads with known or unknown donor variants.
  • The study utilized Biastools to assess bias in different genomic reference types and alignment methods.

Main Results:

  • More inclusive graph genomes were observed to result in fewer biased sites.
  • End-to-end alignment demonstrated a reduction in bias at indels compared to local aligners.
  • The study characterized improvements in large-scale bias using T2T (Telomere-to-Telomere) references.

Conclusions:

  • Biastools provides a comprehensive approach to measuring and understanding reference bias.
  • The findings highlight the importance of reference genome inclusivity and alignment strategies in mitigating bias.
  • T2T references show promise in addressing large-scale bias in genomic data.