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This article is part of the supplement: Proceedings of the 5th International Conference of the Brazilian Association for Bioinformatics and Computational Biology (X-meeting 2009)

Open Access Proceedings

Decreasing the number of false positives in sequence classification

Ariane Machado-Lima12, André Yoshiaki Kashiwabara3 and Alan Mitchell Durham3*

Author affiliations

1 Escola de Artes, Ciências e Humanidades, Universidade de São Paulo, Rua Arlindo Béttio, 1000, 03828-000, São Paulo, SP, Brazil

2 Instituto de Psiquiatria, Universidade de São Paulo, R. Dr. OvÍdio Pires de Campos, 785, 01060-970, São Paulo, SP, Brazil

3 Instituto de Matemática e EstatÍstica, Universidade de São Paulo, Rua do Matão, 1010, 05508-090, São Paulo, SP, Brazil

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Citation and License

BMC Genomics 2010, 11(Suppl 5):S10  doi:10.1186/1471-2164-11-S5-S10

Published: 22 December 2010

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 of a null model in the final result of classifications. In particular, we are interested in minimizing the number of false predictions in a classification. This is a crucial issue to reduce costs of biological validation.

Results

For all the tests, the target null model presented the lowest number of false positives, when using random sequences as a test. The study was performed in DNA sequences using GC content as the measure of content bias, but the results should be valid also for protein sequences. To broaden the application of the results, the study was performed using randomly generated sequences. Previous studies were performed on aminoacid sequences, using only one probabilistic model (HMM) and on a specific benchmark, and lack more general conclusions about the performance of null models. Finally, a benchmark test with P. falciparum confirmed these results.

Conclusions

Of the evaluated models the best suited for classification are the uniform model and the target model. However, the use of the uniform model presents a GC bias that can cause more false positives for candidate sequences with extreme compositional bias, a characteristic not described in previous studies. In these cases the target model is more dependable for biological validation due to its higher specificity.