Advances in Bioinformatics and Computational Biology

Metagenomics is an emerging field in which the power of genome analysis is applied to entire communities of microbes. A large variety of classifiers has been developed for gene prediction though there is lack of an empirical evaluation regarding the core machine learning techniques implemented in these tools. In this work we present an empirical performance evaluation of classification strategies for metagenomic gene prediction. This comparison takes into account distinct supervised learning strategies: one lazy learner, two eager-learners and one ensemble learner. Though the performance of the four base classifiers was good, the ensemble-based strategy with Random Forest has achieved the overall best result.

Microorganisms abound everywhere. Though we know they play key roles in several ecosystems, too little is known about how these complex communities work. To act as a community they must interact with each other in order to achieve such community stability in which proper functions allows the microbial community to adapt in complex environment conditions. Thus, to effectively understand microbial genetic networks one needs to explore them by means of a systems biology approach. The proposed approach extends the metagenomic gene-centric view by taking into account the set of genes present in a metagenome and also the complex links of interactions among these genes and by treating the microbiome as a single biological system. In this paper, we present the FUNN-MG computational framework to explore functional modules in microbial genetic networks.

The advent of rapid evolution on sequencing capacity of new genomes has evidenced the need for data analysis automation aiming at speeding up the genomic annotation process and reducing its cost. Given that one important step for functional genomic annotation is the promoter identification, several studies have been taken in order to propose computational approaches to predict promoters. Different classifiers and characteristics of the promoter sequences have been used to deal with this prediction problem. However, several works in literature have addressed the promoter prediction problem using datasets containing sequences of 250 nucleotides or more. As the sequence length defines the amount of dataset attributes, even considering a limited number of properties to characterize the sequences, datasets with a high number of attributes are generated for training classifiers. Once high-dimensional datasets can degrade the classifiers predictive performance or even require an infesible processing time, predicting promoters by training classifiers from datasets with a reduced number of attributes, it is essential to obtain good predictive performance with low computational cost. To the best of our knowledge, there is no work in literature that verified in a sistematic way the relation between the sequences length and the predictive performance of classifiers. Thus, in this work, sixteen datasets composed of different sized sequences are built and evaluated using the SVM and k-NN classifiers. The experimental results show that several datasets composed of shorter sequences acheived better predictive performance when compared with datasets composed of longer sequences and consumed a significantly shorter processing time.

The catalogs of molecular biology databases does not provide a full description of databases, so the user should select databases using limited information available. Taking into account this fact, in the context of an initiative called BioDBCore, a group of experts proposes core metadata definitions to describe the molecular biology databases. However, how to use these metadata to infer the quality of a database is a clear open issue. In the present work, we propose an ontology-based approach aiming to guide the database selection process from molecular biology database catalogs using these metadata.