A Bayesian nonparametric method for prediction in EST analysis

Background: 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. Results: 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; ...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Antonio Lijoi, Ramsés H. Mena, Igor Prünster
• Keywords: Bayesian Nonparametric Approach, BMCBI 2007, EST Sample, Future Samples