Limits...
Measuring global credibility with application to local sequence alignment.

Webb-Robertson BJ, McCue LA, Lawrence CE - PLoS Comput. Biol. (2008)

Bottom Line: Because sequence alignment is arguably the most extensively used procedure in computational biology, we employ it here to make these general concepts more concrete.The maximum similarity estimator (i.e., the alignment that maximizes the likelihood) and the centroid estimator (i.e., the alignment that minimizes the mean Hamming distance from the posterior weighted ensemble of alignments) are used to demonstrate the application of Bayesian credibility limits to alignment estimators.Application of Bayesian credibility limits to the alignment of 20 human/rodent orthologous sequence pairs and 125 orthologous sequence pairs from six Shewanella species shows that credibility limits of the alignments of promoter sequences of these species vary widely, and that centroid alignments dependably have tighter credibility limits than traditional maximum similarity alignments.

View Article: PubMed Central - PubMed

Affiliation: Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, Washington, United States of America. bj@pnl.gov

ABSTRACT
Computational biology is replete with high-dimensional (high-D) discrete prediction and inference problems, including sequence alignment, RNA structure prediction, phylogenetic inference, motif finding, prediction of pathways, and model selection problems in statistical genetics. Even though prediction and inference in these settings are uncertain, little attention has been focused on the development of global measures of uncertainty. Regardless of the procedure employed to produce a prediction, when a procedure delivers a single answer, that answer is a point estimate selected from the solution ensemble, the set of all possible solutions. For high-D discrete space, these ensembles are immense, and thus there is considerable uncertainty. We recommend the use of Bayesian credibility limits to describe this uncertainty, where a (1-alpha)%, 0< or =alpha< or =1, credibility limit is the minimum Hamming distance radius of a hyper-sphere containing (1-alpha)% of the posterior distribution. Because sequence alignment is arguably the most extensively used procedure in computational biology, we employ it here to make these general concepts more concrete. The maximum similarity estimator (i.e., the alignment that maximizes the likelihood) and the centroid estimator (i.e., the alignment that minimizes the mean Hamming distance from the posterior weighted ensemble of alignments) are used to demonstrate the application of Bayesian credibility limits to alignment estimators. Application of Bayesian credibility limits to the alignment of 20 human/rodent orthologous sequence pairs and 125 orthologous sequence pairs from six Shewanella species shows that credibility limits of the alignments of promoter sequences of these species vary widely, and that centroid alignments dependably have tighter credibility limits than traditional maximum similarity alignments.

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Related in: MedlinePlus

Histograms of the distances of the sampled alignments from the EC and MS for the intergenic regions upstream of the gene SMR4_0576.SMR4_0576 alignment distribution with its orthologous sequence from (A) SONE, (B) CN32, (C) SPV4, and (D) DENI.
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pcbi-1000077-g004: Histograms of the distances of the sampled alignments from the EC and MS for the intergenic regions upstream of the gene SMR4_0576.SMR4_0576 alignment distribution with its orthologous sequence from (A) SONE, (B) CN32, (C) SPV4, and (D) DENI.

Mentions: The four example alignment ND distributions displayed in Figure 4 are indicated by a letter next to the corresponding symbol.


Measuring global credibility with application to local sequence alignment.

Webb-Robertson BJ, McCue LA, Lawrence CE - PLoS Comput. Biol. (2008)

Histograms of the distances of the sampled alignments from the EC and MS for the intergenic regions upstream of the gene SMR4_0576.SMR4_0576 alignment distribution with its orthologous sequence from (A) SONE, (B) CN32, (C) SPV4, and (D) DENI.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2367447&req=5

pcbi-1000077-g004: Histograms of the distances of the sampled alignments from the EC and MS for the intergenic regions upstream of the gene SMR4_0576.SMR4_0576 alignment distribution with its orthologous sequence from (A) SONE, (B) CN32, (C) SPV4, and (D) DENI.
Mentions: The four example alignment ND distributions displayed in Figure 4 are indicated by a letter next to the corresponding symbol.

Bottom Line: Because sequence alignment is arguably the most extensively used procedure in computational biology, we employ it here to make these general concepts more concrete.The maximum similarity estimator (i.e., the alignment that maximizes the likelihood) and the centroid estimator (i.e., the alignment that minimizes the mean Hamming distance from the posterior weighted ensemble of alignments) are used to demonstrate the application of Bayesian credibility limits to alignment estimators.Application of Bayesian credibility limits to the alignment of 20 human/rodent orthologous sequence pairs and 125 orthologous sequence pairs from six Shewanella species shows that credibility limits of the alignments of promoter sequences of these species vary widely, and that centroid alignments dependably have tighter credibility limits than traditional maximum similarity alignments.

View Article: PubMed Central - PubMed

Affiliation: Computational Biology and Bioinformatics, Pacific Northwest National Laboratory, Richland, Washington, United States of America. bj@pnl.gov

ABSTRACT
Computational biology is replete with high-dimensional (high-D) discrete prediction and inference problems, including sequence alignment, RNA structure prediction, phylogenetic inference, motif finding, prediction of pathways, and model selection problems in statistical genetics. Even though prediction and inference in these settings are uncertain, little attention has been focused on the development of global measures of uncertainty. Regardless of the procedure employed to produce a prediction, when a procedure delivers a single answer, that answer is a point estimate selected from the solution ensemble, the set of all possible solutions. For high-D discrete space, these ensembles are immense, and thus there is considerable uncertainty. We recommend the use of Bayesian credibility limits to describe this uncertainty, where a (1-alpha)%, 0< or =alpha< or =1, credibility limit is the minimum Hamming distance radius of a hyper-sphere containing (1-alpha)% of the posterior distribution. Because sequence alignment is arguably the most extensively used procedure in computational biology, we employ it here to make these general concepts more concrete. The maximum similarity estimator (i.e., the alignment that maximizes the likelihood) and the centroid estimator (i.e., the alignment that minimizes the mean Hamming distance from the posterior weighted ensemble of alignments) are used to demonstrate the application of Bayesian credibility limits to alignment estimators. Application of Bayesian credibility limits to the alignment of 20 human/rodent orthologous sequence pairs and 125 orthologous sequence pairs from six Shewanella species shows that credibility limits of the alignments of promoter sequences of these species vary widely, and that centroid alignments dependably have tighter credibility limits than traditional maximum similarity alignments.

Show MeSH
Related in: MedlinePlus