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Comparison study on k-word statistical measures for protein: from sequence to 'sequence space'.

Dai Q, Wang T - BMC Bioinformatics (2008)

Bottom Line: This paper proposed a scheme to construct protein 'sequence space' which was associated with protein sequences related to the given protein, and the performances of statistical measures were compared when they explored the information on protein 'sequence space' or not.Based on the experiments, several conclusions can be drawn and, from them, novel valuable guidelines for the use of protein 'sequence space' and statistical measures were obtained.The reasonable results of phylogenetic analysis confirm that Gdis.k based on 'sequence space' is a reliable measure for phylogenetic analysis.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Applied Mathematics, Dalian University of Technology, Dalian 116024, PR China. daiailiu2004@yahoo.com.cn

ABSTRACT

Background: Many proposed statistical measures can efficiently compare protein sequence to further infer protein structure, function and evolutionary information. They share the same idea of using k-word frequencies of protein sequences. Given a protein sequence, the information on its related protein sequences hasn't been used for protein sequence comparison until now. This paper proposed a scheme to construct protein 'sequence space' which was associated with protein sequences related to the given protein, and the performances of statistical measures were compared when they explored the information on protein 'sequence space' or not. This paper also presented two statistical measures for protein: gre.k (generalized relative entropy) and gsm.k (gapped similarity measure).

Results: We tested statistical measures based on protein 'sequence space' or not with three data sets. This not only offers the systematic and quantitative experimental assessment of these statistical measures, but also naturally complements the available comparison of statistical measures based on protein sequence. Moreover, we compared our statistical measures with alignment-based measures and the existing statistical measures. The experiments were grouped into two sets. The first one, performed via ROC (Receiver Operating Curve) analysis, aims at assessing the intrinsic ability of the statistical measures to discriminate and classify protein sequences. The second set of the experiments aims at assessing how well our measure does in phylogenetic analysis. Based on the experiments, several conclusions can be drawn and, from them, novel valuable guidelines for the use of protein 'sequence space' and statistical measures were obtained.

Conclusion: Alignment-based measures have a clear advantage when the data is high redundant. The more efficient statistical measure is the novel gsm.k introduced by this article, the cos.k followed. When the data becomes less redundant, gre.k proposed by us achieves a better performance, but all the other measures perform poorly on classification tasks. Almost all the statistical measures achieve improvement by exploring the information on 'sequence space' as word's length increases, especially for less redundant data. The reasonable results of phylogenetic analysis confirm that Gdis.k based on 'sequence space' is a reliable measure for phylogenetic analysis. In summary, our quantitative analysis verifies that exploring the information on 'sequence space' is a promising way to improve the abilities of statistical measures for protein comparison.

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Representation of a star set. a: the diagram of star set, S is similar to A, T and N in BLOSUM62 substitution matrix, and S is the midpoint; b: the star set consists of the midpoint S and vertices A, T and N.
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Figure 9: Representation of a star set. a: the diagram of star set, S is similar to A, T and N in BLOSUM62 substitution matrix, and S is the midpoint; b: the star set consists of the midpoint S and vertices A, T and N.

Mentions: We shall bypass the above similarity classes and consider a new star set which is easily to implement. A star set assumes that the properties are known between vertices and center. We can construct a star set including all the vertices and the center, and specifically write the center as the first element of the set to distinguish one set from the others. For example, S is similar to A, T and N in BLOSUM62 substitution matrix, so S is the center and they can constitute a star set {S, A, T, N} presented in Figure 9. For writing convenience, we write the star set {S, A, T, N} as ℵS = {x / x ℵ S, x ∈ Ω}. With the aid of star set, 20 amino acids can be partitioned into 20 star sets presented in Table 2 based on BLOSUM62 substitution matrix.


Comparison study on k-word statistical measures for protein: from sequence to 'sequence space'.

Dai Q, Wang T - BMC Bioinformatics (2008)

Representation of a star set. a: the diagram of star set, S is similar to A, T and N in BLOSUM62 substitution matrix, and S is the midpoint; b: the star set consists of the midpoint S and vertices A, T and N.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2571980&req=5

Figure 9: Representation of a star set. a: the diagram of star set, S is similar to A, T and N in BLOSUM62 substitution matrix, and S is the midpoint; b: the star set consists of the midpoint S and vertices A, T and N.
Mentions: We shall bypass the above similarity classes and consider a new star set which is easily to implement. A star set assumes that the properties are known between vertices and center. We can construct a star set including all the vertices and the center, and specifically write the center as the first element of the set to distinguish one set from the others. For example, S is similar to A, T and N in BLOSUM62 substitution matrix, so S is the center and they can constitute a star set {S, A, T, N} presented in Figure 9. For writing convenience, we write the star set {S, A, T, N} as ℵS = {x / x ℵ S, x ∈ Ω}. With the aid of star set, 20 amino acids can be partitioned into 20 star sets presented in Table 2 based on BLOSUM62 substitution matrix.

Bottom Line: This paper proposed a scheme to construct protein 'sequence space' which was associated with protein sequences related to the given protein, and the performances of statistical measures were compared when they explored the information on protein 'sequence space' or not.Based on the experiments, several conclusions can be drawn and, from them, novel valuable guidelines for the use of protein 'sequence space' and statistical measures were obtained.The reasonable results of phylogenetic analysis confirm that Gdis.k based on 'sequence space' is a reliable measure for phylogenetic analysis.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Applied Mathematics, Dalian University of Technology, Dalian 116024, PR China. daiailiu2004@yahoo.com.cn

ABSTRACT

Background: Many proposed statistical measures can efficiently compare protein sequence to further infer protein structure, function and evolutionary information. They share the same idea of using k-word frequencies of protein sequences. Given a protein sequence, the information on its related protein sequences hasn't been used for protein sequence comparison until now. This paper proposed a scheme to construct protein 'sequence space' which was associated with protein sequences related to the given protein, and the performances of statistical measures were compared when they explored the information on protein 'sequence space' or not. This paper also presented two statistical measures for protein: gre.k (generalized relative entropy) and gsm.k (gapped similarity measure).

Results: We tested statistical measures based on protein 'sequence space' or not with three data sets. This not only offers the systematic and quantitative experimental assessment of these statistical measures, but also naturally complements the available comparison of statistical measures based on protein sequence. Moreover, we compared our statistical measures with alignment-based measures and the existing statistical measures. The experiments were grouped into two sets. The first one, performed via ROC (Receiver Operating Curve) analysis, aims at assessing the intrinsic ability of the statistical measures to discriminate and classify protein sequences. The second set of the experiments aims at assessing how well our measure does in phylogenetic analysis. Based on the experiments, several conclusions can be drawn and, from them, novel valuable guidelines for the use of protein 'sequence space' and statistical measures were obtained.

Conclusion: Alignment-based measures have a clear advantage when the data is high redundant. The more efficient statistical measure is the novel gsm.k introduced by this article, the cos.k followed. When the data becomes less redundant, gre.k proposed by us achieves a better performance, but all the other measures perform poorly on classification tasks. Almost all the statistical measures achieve improvement by exploring the information on 'sequence space' as word's length increases, especially for less redundant data. The reasonable results of phylogenetic analysis confirm that Gdis.k based on 'sequence space' is a reliable measure for phylogenetic analysis. In summary, our quantitative analysis verifies that exploring the information on 'sequence space' is a promising way to improve the abilities of statistical measures for protein comparison.

Show MeSH