Efficient and accurate P-value computation for Position Weight Matrices. Touzet H, Varré JS - Algorithms Mol Biol (2007) Bottom Line: For many examples of PWMs, they fail to give accurate results in a reasonable amount of time.The main idea is to use a series of discretized score distributions that improves the final result step by step until some convergence criterion is met.Experimental results show that it achieves better performance in terms of computational time and precision than existing tools. View Article: PubMed Central - HTML - PubMed Affiliation: LIFL, UMR CNRS 8022, Université des Sciences et Technologies de Lille, 59655 Villeneuve d'Ascq, France. helene.touzet@lifl.fr ABSTRACTBackground: Position Weight Matrices (PWMs) are probabilistic representations of signals in sequences. They are widely used to model approximate patterns in DNA or in protein sequences. The usage of PWMs needs as a prerequisite to knowing the statistical significance of a word according to its score. This is done by defining the P-value of a score, which is the probability that the background model can achieve a score larger than or equal to the observed value. This gives rise to the following problem: Given a P-value, find the corresponding score threshold. Existing methods rely on dynamic programming or probability generating functions. For many examples of PWMs, they fail to give accurate results in a reasonable amount of time.Results: The contribution of this paper is two fold. First, we study the theoretical complexity of the problem, and we prove that it is NP-hard. Then, we describe a novel algorithm that solves the P-value problem efficiently. The main idea is to use a series of discretized score distributions that improves the final result step by step until some convergence criterion is met. Moreover, the algorithm is capable of calculating the exact P-value without any error, even for matrices with non-integer coefficient values. The same approach is also used to devise an accurate algorithm for the reverse problem: finding the P-value for a given score. Both methods are implemented in a software called TFM-PVALUE, that is freely available.Conclusion: We have tested TFM-PVALUE on a large set of PWMs representing transcription factor binding sites. Experimental results show that it achieves better performance in terms of computational time and precision than existing tools. No MeSH data available. Related in: MedlinePlus © Copyright Policy - open-access Related In: Results  -  Collection License getmorefigures.php?uid=PMC2238751&req=5 .flowplayer { width: px; height: px; } Figure 8: Time efficiency for granularity 10-6. We compare the computation time for the score associated to a P-value of 10-3 and 10-6 onto the Jaspar matrices when the granularity is set to 10-6 for TFM-PVALUE and LAZYDISTRIBUTION. The histogram has the same meaning as in Figure 3. Mentions: We then repeated the same procedure as above with a smaller granularity, 10-6 instead of 10-3. Results are reported in Figure 8. When the granularity decreases, the computation time of LAZYDISTRIBUTION dramatically increases. With the P-value set to 10-3, LAZYDISTRIBUTION needs a running time greater than one minute for 89 percent of the matrices (109 out of 122). TFM-Pvalue needs less than 0.1 second for 85 percent of the matrices (104 out of 122). With the P-value set to P-value = 10-6, LAZYDISTRIBUTION needs a computation time greater than 1 minute for 62 percent of matrices (47 out of 75). TFM-PVALUE needs less than 0.1 second for 89 percent of matrices (67 out of 75). Moreover, if we compare the histogram for TFM-PVALUE in Figure 8 with the histogram for LAZYDISTRIBUTION in Figure 7, it appears that TFM-PVALUE is still more efficient, whereas the granularity is a thousand fold larger. This demonstrates that we are able to provide more accurate results within the same amount of time. The same conclusion holds for the amount of memory needed to achieve the computation (data not shown).

Efficient and accurate P-value computation for Position Weight Matrices.

Touzet H, Varré JS - Algorithms Mol Biol (2007)

Related In: Results  -  Collection

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Figure 8: Time efficiency for granularity 10-6. We compare the computation time for the score associated to a P-value of 10-3 and 10-6 onto the Jaspar matrices when the granularity is set to 10-6 for TFM-PVALUE and LAZYDISTRIBUTION. The histogram has the same meaning as in Figure 3.
Mentions: We then repeated the same procedure as above with a smaller granularity, 10-6 instead of 10-3. Results are reported in Figure 8. When the granularity decreases, the computation time of LAZYDISTRIBUTION dramatically increases. With the P-value set to 10-3, LAZYDISTRIBUTION needs a running time greater than one minute for 89 percent of the matrices (109 out of 122). TFM-Pvalue needs less than 0.1 second for 85 percent of the matrices (104 out of 122). With the P-value set to P-value = 10-6, LAZYDISTRIBUTION needs a computation time greater than 1 minute for 62 percent of matrices (47 out of 75). TFM-PVALUE needs less than 0.1 second for 89 percent of matrices (67 out of 75). Moreover, if we compare the histogram for TFM-PVALUE in Figure 8 with the histogram for LAZYDISTRIBUTION in Figure 7, it appears that TFM-PVALUE is still more efficient, whereas the granularity is a thousand fold larger. This demonstrates that we are able to provide more accurate results within the same amount of time. The same conclusion holds for the amount of memory needed to achieve the computation (data not shown).

Bottom Line: For many examples of PWMs, they fail to give accurate results in a reasonable amount of time.The main idea is to use a series of discretized score distributions that improves the final result step by step until some convergence criterion is met.Experimental results show that it achieves better performance in terms of computational time and precision than existing tools.

View Article: PubMed Central - HTML - PubMed

Affiliation: LIFL, UMR CNRS 8022, Université des Sciences et Technologies de Lille, 59655 Villeneuve d'Ascq, France. helene.touzet@lifl.fr

ABSTRACT

Background: Position Weight Matrices (PWMs) are probabilistic representations of signals in sequences. They are widely used to model approximate patterns in DNA or in protein sequences. The usage of PWMs needs as a prerequisite to knowing the statistical significance of a word according to its score. This is done by defining the P-value of a score, which is the probability that the background model can achieve a score larger than or equal to the observed value. This gives rise to the following problem: Given a P-value, find the corresponding score threshold. Existing methods rely on dynamic programming or probability generating functions. For many examples of PWMs, they fail to give accurate results in a reasonable amount of time.

Results: The contribution of this paper is two fold. First, we study the theoretical complexity of the problem, and we prove that it is NP-hard. Then, we describe a novel algorithm that solves the P-value problem efficiently. The main idea is to use a series of discretized score distributions that improves the final result step by step until some convergence criterion is met. Moreover, the algorithm is capable of calculating the exact P-value without any error, even for matrices with non-integer coefficient values. The same approach is also used to devise an accurate algorithm for the reverse problem: finding the P-value for a given score. Both methods are implemented in a software called TFM-PVALUE, that is freely available.

Conclusion: We have tested TFM-PVALUE on a large set of PWMs representing transcription factor binding sites. Experimental results show that it achieves better performance in terms of computational time and precision than existing tools.

No MeSH data available.

Related in: MedlinePlus