A Bayesian nonparametric method for prediction in EST analysis

• Published in: BMC bioinformatics Vol. 8; no. 1; p. 339
• Main Authors: Lijoi, Antonio, Mena, Ramsés H, Prünster, Igor
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 14-09-2007; BioMed Central; BMC
• Subjects: Algorithms; Amoeba - genetics; Animals; Bayes Theorem; Chromosome Mapping; Decision Theory; Expressed Sequence Tags; Gene banks; Gene Expression Profiling - methods; Gene Library; Genes; Identification and classification; Methodology; Methods; Models, Genetic; Models, Statistical; Nucleic acids; Predictive Value of Tests; Sample Size; Separation; Sequence Analysis, DNA - methods; Statistics, Nonparametric; Italy
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-8-339

Expressed sequence tags (ESTs) analyses are a fundamental tool for gene identification in organisms. Given a preliminary EST sample from a certain library, several statistical prediction problems arise. In particular, it is of interest to estimate how many new genes can be detected in a future EST sample of given size and also to determine the gene discovery rate: these estimates represent the basis for deciding whether to proceed sequencing the library and, in case of a positive decision, a guideline for selecting the size of the new sample. Such information is also useful for establishing sequencing efficiency in experimental design and for measuring the degree of redundancy of an EST library.
In this work we propose a Bayesian nonparametric approach for tackling statistical problems related to EST surveys. In particular, we provide estimates for: a) the coverage, defined as the proportion of unique genes in the library represented in the given sample of reads; b) the number of new unique genes to be observed in a future sample; c) the discovery rate of new genes as a function of the future sample size. The Bayesian nonparametric model we adopt conveys, in a statistically rigorous way, the available information into prediction. Our proposal has appealing properties over frequentist nonparametric methods, which become unstable when prediction is required for large future samples. EST libraries, previously studied with frequentist methods, are analyzed in detail.
The Bayesian nonparametric approach we undertake yields valuable tools for gene capture and prediction in EST libraries. The estimators we obtain do not feature the kind of drawbacks associated with frequentist estimators and are reliable for any size of the additional sample.