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Local Renyi entropic profiles of DNA sequences.

Vinga S, Almeida JS - BMC Bioinformatics (2007)

Bottom Line: Subsequent work proposed a fractal pdf kernel as a more exact solution for the iterated map representation.The new methodology enables two results.On the other hand, by spanning the parameters of the kernel function it is possible to extract important information about the scale of each conserved DNA region.

View Article: PubMed Central - HTML - PubMed

Affiliation: Instituto de Engenharia de Sistemas e Computadores: Investigação e Desenvolvimento (INESC-ID), R, Alves Redol 9, 1000-029 Lisboa, Portugal. svinga@kdbio.inesc-id.pt

ABSTRACT

Background: In a recent report the authors presented a new measure of continuous entropy for DNA sequences, which allows the estimation of their randomness level. The definition therein explored was based on the Rényi entropy of probability density estimation (pdf) using the Parzen's window method and applied to Chaos Game Representation/Universal Sequence Maps (CGR/USM). Subsequent work proposed a fractal pdf kernel as a more exact solution for the iterated map representation. This report extends the concepts of continuous entropy by defining DNA sequence entropic profiles using the new pdf estimations to refine the density estimation of motifs.

Results: The new methodology enables two results. On the one hand it shows that the entropic profiles are directly related with the statistical significance of motifs, allowing the study of under and over-representation of segments. On the other hand, by spanning the parameters of the kernel function it is possible to extract important information about the scale of each conserved DNA region. The computational applications, developed in Matlab m-code, the corresponding binary executables and additional material and examples are made publicly available at http://kdbio.inesc-id.pt/~svinga/ep/.

Conclusion: The ability to detect local conservation from a scale-independent representation of symbolic sequences is particularly relevant for biological applications where conserved motifs occur in multiple, overlapping scales, with significant future applications in the recognition of foreign genomic material and inference of motif structures.

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Related in: MedlinePlus

Conserved motif detection and extraction. By searching the parameter space (L, φ) for a specific position i and finding the values  it is possible to extract the most significant suffix in) the entropic profile context, illustrated here for the first four sequences. Each of the panels corresponds to a different sequence and position where the motif was correctly recovered just by using these maxima: a) m3, b) m4, c) m5 and d) Es (see also Table 1). The profiles for the Lmax and φmax are also shown: apparently one can obtain a non-decreasing function of the positions, which means that previous suffixes are embedded in the implanted motifs.
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Figure 7: Conserved motif detection and extraction. By searching the parameter space (L, φ) for a specific position i and finding the values it is possible to extract the most significant suffix in) the entropic profile context, illustrated here for the first four sequences. Each of the panels corresponds to a different sequence and position where the motif was correctly recovered just by using these maxima: a) m3, b) m4, c) m5 and d) Es (see also Table 1). The profiles for the Lmax and φmax are also shown: apparently one can obtain a non-decreasing function of the positions, which means that previous suffixes are embedded in the implanted motifs.

Mentions: Using this methodology one obtains precisely the implanted motifs of the previous datasets. As an example, the "TATA"-box referred to before is correctly inferred and also the above mentioned examples with the artificial sequences (Figure 7).


Local Renyi entropic profiles of DNA sequences.

Vinga S, Almeida JS - BMC Bioinformatics (2007)

Conserved motif detection and extraction. By searching the parameter space (L, φ) for a specific position i and finding the values  it is possible to extract the most significant suffix in) the entropic profile context, illustrated here for the first four sequences. Each of the panels corresponds to a different sequence and position where the motif was correctly recovered just by using these maxima: a) m3, b) m4, c) m5 and d) Es (see also Table 1). The profiles for the Lmax and φmax are also shown: apparently one can obtain a non-decreasing function of the positions, which means that previous suffixes are embedded in the implanted motifs.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2238722&req=5

Figure 7: Conserved motif detection and extraction. By searching the parameter space (L, φ) for a specific position i and finding the values it is possible to extract the most significant suffix in) the entropic profile context, illustrated here for the first four sequences. Each of the panels corresponds to a different sequence and position where the motif was correctly recovered just by using these maxima: a) m3, b) m4, c) m5 and d) Es (see also Table 1). The profiles for the Lmax and φmax are also shown: apparently one can obtain a non-decreasing function of the positions, which means that previous suffixes are embedded in the implanted motifs.
Mentions: Using this methodology one obtains precisely the implanted motifs of the previous datasets. As an example, the "TATA"-box referred to before is correctly inferred and also the above mentioned examples with the artificial sequences (Figure 7).

Bottom Line: Subsequent work proposed a fractal pdf kernel as a more exact solution for the iterated map representation.The new methodology enables two results.On the other hand, by spanning the parameters of the kernel function it is possible to extract important information about the scale of each conserved DNA region.

View Article: PubMed Central - HTML - PubMed

Affiliation: Instituto de Engenharia de Sistemas e Computadores: Investigação e Desenvolvimento (INESC-ID), R, Alves Redol 9, 1000-029 Lisboa, Portugal. svinga@kdbio.inesc-id.pt

ABSTRACT

Background: In a recent report the authors presented a new measure of continuous entropy for DNA sequences, which allows the estimation of their randomness level. The definition therein explored was based on the Rényi entropy of probability density estimation (pdf) using the Parzen's window method and applied to Chaos Game Representation/Universal Sequence Maps (CGR/USM). Subsequent work proposed a fractal pdf kernel as a more exact solution for the iterated map representation. This report extends the concepts of continuous entropy by defining DNA sequence entropic profiles using the new pdf estimations to refine the density estimation of motifs.

Results: The new methodology enables two results. On the one hand it shows that the entropic profiles are directly related with the statistical significance of motifs, allowing the study of under and over-representation of segments. On the other hand, by spanning the parameters of the kernel function it is possible to extract important information about the scale of each conserved DNA region. The computational applications, developed in Matlab m-code, the corresponding binary executables and additional material and examples are made publicly available at http://kdbio.inesc-id.pt/~svinga/ep/.

Conclusion: The ability to detect local conservation from a scale-independent representation of symbolic sequences is particularly relevant for biological applications where conserved motifs occur in multiple, overlapping scales, with significant future applications in the recognition of foreign genomic material and inference of motif structures.

Show MeSH
Related in: MedlinePlus