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Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

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Alignment-free sequence comparison (I): statistics and power.

Gesine Reinert¹, David Chew, Fengzhu Sun

¹Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|December 17, 2009

Summary

New D(2) statistics improve biological sequence comparison by reducing noise. D(2)(S) offers normal distribution, while D(2)(*) shows higher power for detecting sequence relatedness.

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Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Comparing biological sequences is crucial in computational biology.
The D(2) statistic, based on k-tuple content, is a fast but flawed method.
Existing D(2) methods are susceptible to single-sequence noise, hindering accuracy.

Purpose of the Study:

To address the limitations of the D(2) statistic in biological sequence comparison.
To introduce novel, noise-resistant variants of the D(2) statistic.
To enhance the detection of relatedness between biological sequences.

Main Methods:

Development of two new D(2) statistic variants: D(2)(S) and D(2)(*).
D(2)(S) is a self-standardized statistic analyzed for asymptotic normality.
D(2)(*) is evaluated for its power in detecting sequence relationships.

Main Results:

The D(2)(S) statistic is asymptotically normally distributed for infinite sequence lengths, unaffected by individual sequence noise.
The D(2)(*) statistic demonstrates superior power in detecting sequence relatedness compared to D(2)(S).
While D(2)(*) allows for simulation from its asymptotic distribution, a closed-form power calculation is not provided.

Conclusions:

The proposed D(2)(S) and D(2)(*) statistics offer significant improvements over the traditional D(2) statistic.
D(2)(S) provides a statistically robust measure, while D(2)(*) enhances sensitivity in detecting sequence similarity.
These advancements contribute to more accurate and reliable large-scale biological sequence comparisons.