Exploiting the Functional and Taxonomic Structure of Genomic Data by Probabilistic T

[laiju]

Approach based on homology and composition-based approach to further study the functional nucleus (ie, the microbial nucleus and nucleus of the gene, correspondingly). In the proposed method, the identification of the main groups of functionality is achieved by generative theme modelling, which is able to extract useful information from unlabelled data. We first demonstrate that generative topic model can be used to model the taxon abundance information obtained by homology-based approach and study the microbial nucleus. The model considers each sample as a "document", which has a mixture of functional groups, while each functional group (also known as "latent theme") is a mixture of species weight. Therefore, the estimation of the generative theme model for taxon abundance data will reveal the distribution over latent functions (latent theme) in each sample. Second, we show that, generative topic model can also be used to study genome-level composition of "N-Mer" characteristics (DNA subreads obtained by composition-based approaches). The model considers each genome as a mixture Of latten genetic patterns (latent themes), while each functional pattern is a weighted mixture of "N-mer" characteristics, so the existence of nucleus genomes can be indicated by a set of common N-mer characteristics. After studying the mutual information between the latent themes and the genetic regions, we offer an explanation of the functional roles of the latent genetic patterns discovered. The experimental results demonstrate the efficacy of the proposed method.

[pradeeba]

Abstract

In this paper, we present a method that enable both homology-based approach and composition-based approach to further study the functional core (i.e., microbial core and gene core, correspondingly). In the proposed method, the identification of major functionality groups is achieved by generative topic modeling, which is able to extract useful information from unlabeled data. We first show that generative topic model can be used to model the taxon abundance information obtained by homology-based approach and study the microbial core. The model considers each sample as a document, which has a mixture of functional groups, while each functional group (also known as a latent topic ) is a weight mixture of species. Therefore, estimating the generative topic model fortaxon abundance data will uncover the distribution over latent functions (latent topic) in each sample. Second, we show that, generative topic model can also be used to study the genome-level composition of N-mer features (DNA subreads obtained by composition-based approaches). The model consider each genome as a mixture of latten genetic patterns (latent topics), while each functional pattern is a weighted mixture of the N-mer features, thus the existence of core genomes can be indicated by a set of common N-mer features. After studying the mutual information between latent topics and gene regions, we provide an explanation of the functional roles of uncovered latten genetic patterns. The experimental results demonstrate the effectiveness of proposed method