Measuring Quality of DNA Sequence Data via Degradation
This addresses data quality issues in genomics, potentially aiding researchers and database managers in identifying problematic sequences, though it appears incremental as it builds on existing quality assessment concepts.
The paper tackles the problem of assessing DNA sequence data quality by introducing a novel paradigm that quantifies the effects of intentional degradation, demonstrating that higher initial quality leads to greater fragility and using these measures to identify outliers for quality control or anomaly detection.
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.