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Since the emergence of high-throughput genome sequencing platforms and more recently the next-generation platforms, the genome databases are growing at an astronomical rate. Tremendous efforts have been invested in recent years in understanding intriguing complexities beneath the vast ocean of genomic data. This is apparent in the spurt of computational methods for interpreting these data in the past few years. Genomic data interpretation is notoriously difficult, partly owing to the inherent heterogeneities appearing at different scales. Methods developed to interpret these data often suffer from their inability to adequately measure the underlying heterogeneities and thus lead to confounding results. Here, we present an information entropy-based approach that unravels the distinctive patterns underlying genomic data efficiently and thus is applicable in addressing a variety of biological problems. We show the robustness and consistency of the proposed methodology in addressing three different biological problems of significance--identification of alien DNAs in bacterial genomes, detection of structural variants in cancer cell lines and alignment-free genome comparison.
Develop segmentation technique and apply to variety of problems includine alignment-free genome comparison. Claim much better performance of FFP after segmenting.
Use S-J divergence to segment genomes .Main goal is to determine number of segments.
in a Markov chain model framework, and the generalized Jensen–Shannon divergence measure for a Markov source of order m is defined as (28,29)
or the Jensen–Shannon divergence measure, analytic expression for the probability distribution was derived for a special case when the weight parameters are proportional to sequence lengths (Equations 2 and 5) (21,28). This allowed to assess the statistical significance of the value of Dm. It was shown that asymptotically, for large L, the probability distribution of
Dm : P(Dm <= x) ~ X^2(2L(ln2)X)
to account for short-range nucleotide ordering in the framework of Markov models makes this a powerful tool for mining genomic data (28,29). Briefly, the recursive segmentation procedure proceeds as follows: (i) given as sequence S, compute the difference between sequence segments left and right to each sequence position in S using Jensen–Shannon divergence measure (or its generalization); (ii) find the position of maximum divergence between left and right sequence segments; (iii) if the value of this maximum difference is significantly large, the sequence is segmented at this position; (iv) repeat the aforementioned procedure recursively until none of the resulting sequence segments can be split further. The final output from this procedure is thus a set of sequence segments that are homogeneous within, but heterogeneous between, according to a prespecified criterion
Although other methods require to specify a priori the number of segments or the number of sources or both, the MJSD-based method generates the number of segments and their clusters corresponding to the inherent genomic heterogeneity.
Applied technique to :
Problem 1: Deconstruction of Chimeric Genomes
Problem 2: Detection of Structural Variations in Cancer Genomes
Problem 3: Alignment-free Genome Comparison
In all instances, we observed segmentation clustering approach to be outperforming the FFP method, reiterating that genome composition deconstruction is a critical step in robust inference of organismal relationships.
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