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Related Concept Videos

Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
Improving Translational Accuracy02:07

Improving Translational Accuracy

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...
Improving Translational Accuracy02:07

Improving Translational Accuracy

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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Related Experiment Video

Updated: May 22, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

The practical effect of batch on genomic prediction.

Hilary S Parker1, Jeffrey T Leek

  • 1Johns Hopkins Bloomberg School of Public Health, USA.

Statistical Applications in Genetics and Molecular Biology
|May 23, 2012
PubMed
Summary

Batch effects in high-throughput genomic studies can distort results. Minimizing batch-outcome correlation and removing affected data can improve predictor accuracy, enhancing biological conclusions.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput technologies like microarrays generate large datasets susceptible to non-biological artifacts.
  • Batch effects, arising from experimental conditions or processing times, can obscure true biological signals and alter study conclusions.
  • Understanding and mitigating batch effects is crucial for reliable interpretation of genomic data.

Purpose of the Study:

  • To investigate the impact of batch effects on the performance of predictors built from genomic data.
  • To identify strategies for minimizing the negative influence of batch effects on prediction accuracy.

Main Methods:

  • Utilized publicly available gene expression datasets with known outcomes.
  • Estimated batch effects using the date of measurement.

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Last Updated: May 22, 2026

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

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  • Analyzed the correlation between outcomes and estimated batches in training data.
  • Evaluated the effect of removing batch-affected expression measurements on predictor accuracy.

    • Main Results:

    • The impact of batch effects on prediction accuracy is dependent on the correlation between the outcome and batch in the training data.
    • Removing expression measurements most affected by batch effects prior to predictor construction can enhance prediction accuracy.
    • A high correlation between batch and outcome significantly impacts prediction performance.

    Conclusions:

    • Training datasets should be designed to minimize the correlation between experimental batches and study outcomes.
    • Developing methods to identify and remove batch-affected probes is essential for improving prediction accuracy in high-throughput genomic studies.
    • Proactive management of batch effects is key to robust biological conclusions from genomic data.