With the emergence of Next Generation Sequencing (NGS) technologies, a large volume of sequence data in particular de novo sequencing was rapidly produced at relatively low costs. In this context, computational tools are increasingly important to assist in the identification of relevant information to understand the functioning of organisms. This work introduces BASiNET, an alignment-free tool for classifying biological sequences based on the feature extraction from complex network measurements. The method initially transform the sequences and represents them as complex networks. Then it extracts topological measures and constructs a feature vector that is used to classify the sequences. The method was evaluated in the classification of coding and non-coding RNAs of 13 species and compared to the CNCI, PLEK and CPC2 methods. BASiNET outperformed all compared methods in all adopted organisms and datasets. BASiNET have classified sequences in all organisms with high accuracy and low standard deviation, showing that the method is robust and non-biased by the organism. The proposed methodology is implemented in open source in R language and freely available for download at https://cran.r-project.org/package=BASiNET.
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Background A current challenge in gene annotation is to define the gene function in the context of the network of relationships instead of using single genes. The inference of gene networks (GNs) has emerged as an approach to better understand the biology of the system and to study how several components of this network interact with each other and keep their functions stable. However, in general there is no sufficient data to accurately recover the GNs from their expression levels leading to the curse of dimensionality, in which the number of variables is higher than samples. One way to mitigate this problem is to integrate biological data instead of using only the expression profiles in the inference process. Nowadays, the use of several biological information in inference methods had a significant increase in order to better recover the connections between genes and reduce the false positives. What makes this strategy so interesting is the possibility of confirming the known connections through the included biological data, and the possibility of discovering new relationships between genes when observed the expression data. Although several works in data integration have increased the performance of the network inference methods, the real contribution of adding each type of biological information in the obtained improvement is not clear. Methods We propose a methodology to include biological information into an inference algorithm in order to assess its prediction gain by using biological information and expression profile together. We also evaluated and compared the gain of adding four types of biological information: (a) protein-protein interaction, (b) Rosetta stone fusion proteins, (c) KEGG and (d) KEGG+GO. Results and conclusions This work presents a first comparison of the gain in the use of prior biological information in the inference of GNs by considering the eukaryote ( P. falciparum ) organism. Our results indicates that information based on direct interaction can produce a higher improvement in the gain than data about a less specific relationship as GO or KEGG. Also, as expected, the results show that the use of biological information is a very important approach for the improvement of the inference. We also compared the gain in the inference of the global network and only the hubs. The results indicates that the use of biological information can improve the identification of the most connected proteins.
A current challenge in gene annotation is to define the gene function in the context of the network of relationships instead of using gene or their products alone. In this way the gene networks (GNs) inference has emerged as an approach to better understand the biology of the system. In recent years there has been a growing of use of other biological information than expression data to better recover the gene networks. These approaches are called data integration. Although several works in data integration have increased the performance of network inference, the precise gain of adding each type of biological information is still unclear. In this work we propose a methodology to include biological information into an inference algorithm, in order to assess its prediction gain by using biological information and expression profile together. Our results shows, as expected, that by adding biological information is a very important approach for the improvement of inference. The sensitivity measure presented approximately 90% of correct recovering, by setting equal weights for biological and expression profile. The PPV measure indicates that is a very difficult task due to the complexity of the biological machinery and the indirect relationship between transcripts and proteins. In addition, it could be observed a logarithmic behavior of the sensitivity measure. This work presents a first step towards assessing the gain in adding prior biological information in the inference of gene networks by considering an eukaryote (P. falciparum) organism.
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