Measuring global credibility with application to local sequence alignment.

Area of Science:

• Computational biology
• Bioinformatics
• Statistical genetics

Background:

• High-dimensional discrete prediction and inference are common in computational biology, yet global measures of uncertainty are underdeveloped.
• Current methods often provide point estimates, overlooking the vast solution ensembles and inherent uncertainty in high-dimensional spaces.

Purpose of the Study:

• To introduce and demonstrate the application of Bayesian credibility limits for quantifying uncertainty in high-dimensional discrete prediction problems within computational biology.
• To compare the effectiveness of traditional maximum similarity estimators with centroid estimators using Bayesian credibility limits.

Main Methods:

• Developed Bayesian credibility limits, defined as the minimum Hamming distance radius containing a specified percentage of the posterior distribution.
• Applied these limits to sequence alignment, utilizing both maximum similarity and centroid estimators.
• Tested the method on orthologous sequence pairs from human/rodent and Shewanella species, focusing on promoter sequences.

Main Results:

• Bayesian credibility limits effectively quantify uncertainty in sequence alignment.
• Credibility limits for promoter sequence alignments varied widely across species.
• Centroid alignments consistently exhibited tighter credibility limits compared to maximum similarity alignments.

Conclusions:

• Bayesian credibility limits offer a robust framework for assessing uncertainty in computational biology predictions.
• Centroid estimators, when combined with Bayesian credibility limits, provide more reliable and precise alignments than traditional methods.
• This approach enhances the interpretability and trustworthiness of predictions in high-dimensional biological data analysis.