Sequencing

11th Annual Sequencing, Finishing, and Analysis in the Future Meeting
QUALITY ASSESSMENT AND VALIDATION CRITERIA
– TOWARDS THE DEFINITION OF TABLE 1.
Wednesday, 1st June 20:00 La Fonda NM Room (1st floor) Poster (PS‐1b.09)
Dominika Borek, Maciej Puzio, Zbyszek Otwinowski
UT Southwestern Medical Center
Although next‐generation sequencing provides the means to study properties of nucleic acids on unprecedented
scales, concise measures for assessing the confidence of results from NGS experiments are
lacking. There are tens to hundreds of statistical indicators available right now which separately provide
information about: (1) the quality of the sequencing library and the material that was used to generate it,
(2) the performance of the equipment, (3) potential biases in the results, and other important sequencingrelated
features. However, the average consumer of NGS technology is rarely in a position to efficiently
integrate all of this information. This leads to decisions regarding whether an experiment was successful
and whether the results are trustworthy frequently being arbi‐ trary. Comparative and meta‐analyses are
the area most affected by this; differences are attributed to biological phenomena when they frequently
originated from differences in the experimental ap‐ proach. The lack of transparent validation criteria leads
not only to the incorrect or sub‐optimal interpretation of results but also to expensive over‐sequencing.
We have developed alignment‐free metrics that provide transparent and comprehensive validation
of NGS experiment results and define the so‐called Table 1, which concisely summarizes the quality
of an experiment and data analysis so that NGS users and reviewers of publications and grant applications
can quickly and yet with high certainty asses the quality of a particular NGS experiment.
Our approach is based on data mining of sequencing reads, which includes analysis of overdispersion
properties. This is followed by the analysis of residuals to detect whether our models of the
experiment are sufficiently complete.
This approach provides partitioning of uncertainty into components related to error sources and
estimates the magnitude of each error source. Together, these directly assess the quality of NGS
experiments and contribute to the validation of NGS results.
75