Decreasing the number of false positives in sequence classification.

ABSTRACT:

Background

A large number of probabilistic models used in sequence analysis assign non-zero probability values to most input sequences. To decide when a given probability is sufficient the most common way is bayesian binary classification, where the probability of the model characterizing the sequence family of interest is compared to that of an alternative probability model. We can use as alternative model a null model. This is the scoring technique used by sequence analysis tools such as HMMER, SAM and INFERNAL. The most prevalent null models are position-independent residue distributions that include: the uniform distribution, genomic distribution, family-specific distribution and the target sequence distribution. This paper presents a study to evaluate the impact of the choice

SUBMITTER: Machado-Lima A 

PROVIDER: S-EPMC3045793 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature