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Genome Annotation and Assembly

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.

Genome Copying Errors

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 Errors

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.
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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Related Experiment Video

Updated: Mar 29, 2026

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Published on: September 13, 2018

Identification of sample annotation errors in gene expression datasets.

Miriam Lohr¹, Birte Hellwig¹, Karolina Edlund²

¹Department of Statistics, TU Dortmund University, Vogelpothsweg 87, 44227, Dortmund, Germany.

Archives of Toxicology

|November 27, 2015

Summary

This summary is machine-generated.

Gene expression data analysis can identify mislabeled human tissue samples in public databases. New methods detect sample mix-ups, improving the reliability of transcriptomic studies in cancer research and beyond.

Keywords:

Gene expression Male–female classifier Microarray Misannotation Quality control

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Area of Science:

Transcriptomics
Bioinformatics
Oncology

Background:

Transcriptomic analysis of human tissues is crucial in oncology, immunology, and toxicology.
Gene expression profiling aids in disease subclassification and predicting therapy response.
Publicly available transcriptomic data offers opportunities for meta-analysis but requires reliable annotations.

Purpose of the Study:

To develop and validate gene expression-based methods for detecting sample misannotations in public transcriptomic datasets.
To assess the prevalence of erroneous sample labeling in large-scale transcriptomic studies.
To enhance the reliability and reproducibility of clinical gene expression data analysis.

Main Methods:

Development of a classifier to distinguish between male and female patient samples based on gene expression.
Utilizing correlation analysis to identify multiple measurements from the same sample.
Application of these methods to 45 public transcriptomic datasets comprising 4913 patients.

Main Results:

Erroneous sample annotations were identified in 40% of the analyzed datasets.
The proposed methods successfully detected sample mix-ups and redundant measurements.
The prevalence of mislabeled samples is potentially higher than previously recognized.

Conclusions:

Gene expression-based quality control methods can effectively identify sample misannotations in public transcriptomic data.
Addressing erroneous sample labeling is critical for accurate statistical evaluation and interpretation of results.
Routine implementation of these methods can improve the integrity of clinical gene expression data analysis.