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Decreasing the number of false positives in sequence classification

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 seque...

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Autores principales: Machado-Lima, Ariane, Kashiwabara, André Yoshiaki, Durham, Alan Mitchell
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3045793/
https://www.ncbi.nlm.nih.gov/pubmed/21210966
http://dx.doi.org/10.1186/1471-2164-11-S5-S10
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author Machado-Lima, Ariane
Kashiwabara, André Yoshiaki
Durham, Alan Mitchell
author_facet Machado-Lima, Ariane
Kashiwabara, André Yoshiaki
Durham, Alan Mitchell
author_sort Machado-Lima, Ariane
collection PubMed
description 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.
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spelling pubmed-30457932011-03-01 Decreasing the number of false positives in sequence classification Machado-Lima, Ariane Kashiwabara, André Yoshiaki Durham, Alan Mitchell BMC Genomics Proceedings 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. BioMed Central 2010-12-22 /pmc/articles/PMC3045793/ /pubmed/21210966 http://dx.doi.org/10.1186/1471-2164-11-S5-S10 Text en Copyright ©2010 Durham et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Proceedings
Machado-Lima, Ariane
Kashiwabara, André Yoshiaki
Durham, Alan Mitchell
Decreasing the number of false positives in sequence classification
title Decreasing the number of false positives in sequence classification
title_full Decreasing the number of false positives in sequence classification
title_fullStr Decreasing the number of false positives in sequence classification
title_full_unstemmed Decreasing the number of false positives in sequence classification
title_short Decreasing the number of false positives in sequence classification
title_sort decreasing the number of false positives in sequence classification
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3045793/
https://www.ncbi.nlm.nih.gov/pubmed/21210966
http://dx.doi.org/10.1186/1471-2164-11-S5-S10
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