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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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Published on: August 3, 2018

Detecting, Categorizing, and Correcting Coverage Anomalies of RNA-Seq Quantification.

Cong Ma¹, Carl Kingsford¹

¹Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213, USA.

|December 2, 2019

Summary

This study introduces a method to find expression anomalies in RNA sequencing data, improving accuracy by identifying and correcting errors caused by incomplete transcriptomes or flawed algorithms. This helps in detecting novel isoforms.

Keywords:

RNA-seq anomaly detection expression quantification unannotated isoform

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

RNA sequencing (RNA-seq) analysis can inaccurately model gene expression due to incomplete reference transcriptomes or sequencing bias.
Existing algorithms may fail to fully explain all input reads, leading to potential errors in expression quantification.

Purpose of the Study:

To develop a computational method for detecting expression anomalies in RNA-seq data.
To differentiate anomalies caused by reference transcriptome incompleteness from algorithmic errors.
To improve the accuracy of differential gene expression analysis.

Main Methods:

Identified regions with significant deviations between expected and observed read coverage.
Developed a method to classify the cause of detected anomalies.
Applied the anomaly detection and correction methods to GEUVADIS and Human Body Map datasets.

Main Results:

Detected expression anomalies in multiple human RNA-seq datasets.
Correcting anomalies reduced false positives in differential expression predictions.
Identified 88 common anomalies potentially indicating unannotated isoforms, often with reduced 3' end coverage.

Conclusions:

The developed method effectively detects and characterizes expression anomalies in RNA-seq data.
Addressing these anomalies enhances the reliability of gene expression quantification and differential expression analysis.
Uncorrected anomalies suggest the presence of novel, unannotated isoforms in the reference transcriptome.