Measuring Quality of DNA Sequence Data via Degradation

TL;DR

This paper introduces a new method to assess genome data quality by measuring how it degrades under intentional damage, helping identify outliers and potential data issues.

Contribution

It presents a novel paradigm for genome data quality characterization based on degradation effects, applicable for outlier detection and data validation.

Findings

Degradation measures reveal data fragility related to initial quality.

Method detects outliers and anomalies in genome datasets.

Degradation patterns can indicate potential data subversion or errors.

Abstract

We propose and apply a novel paradigm for characterization of genome data quality, which quantifies the effects of intentional degradation of quality. The rationale is that the higher the initial quality, the more fragile the genome and the greater the effects of degradation. We demonstrate that this phenomenon is ubiquitous, and that quantified measures of degradation can be used for multiple purposes. We focus on identifying outliers that may be problematic with respect to data quality, but might also be true anomalies or even attempts to subvert the database.