A new method for evaluating the impacts of semantic similarity measures on the annotation of gene sets.

MOTIVATION: The recent revolution in new sequencing technologies, as a part of the continuous process of adopting new innovative protocols has strongly impacted the interpretation of relations between phenotype and genotype. Thus, understanding the resulting gene sets has become a bottleneck that needs to be addressed. Automatic methods have been proposed to facilitate the interpretation of gene sets. While statistical functional enrichment analyses are currently well known, they tend to focus on well-known genes and to ignore new information from less-studied genes. To address such issues, applying semantic similarity measures is logical if the knowledge source used to annotate the gene sets is hierarchically structured. In this work, we propose a new method for analyzing the impact of different semantic similarity measures on gene set annotations. RESULTS: We evaluated the impact of each measure by taking into consideration the two following features that correspond to relevant criteria for a "good" synthetic gene set annotation: (i) the number of annotation terms has to be drastically reduced and the representative terms must be retained while annotating the gene set, and (ii) the number of genes described by the selected terms should be as large as possible. Thus, we analyzed nine semantic similarity measures to identify the best possible compromise between both features while maintaining a sufficient level of details. Using Gene Ontology to annotate the gene sets, we obtained better results with node-based measures that use the terms' characteristics than with measures based on edges that link the terms. The annotation of the gene sets achieved with the node-based measures did not exhibit major differences regardless of the characteristics of terms used.

Chaining sequence/structure seeds for computing RNA similarity.

We describe a new method to compare a query RNA with a static set of target RNAs. Our method is based on (i) a static indexing of the sequence/structure seeds of the target RNAs; (ii) searching the target RNAs by detecting seeds of the query present in the target, chaining these seeds in promising candidate homologs; and then (iii) completing the alignment using an anchor-based exact alignment algorithm. We apply our method on the benchmark Bralibase2.1 and compare its accuracy and efficiency with the exact method LocARNA and its recent seeds-based speed-up ExpLoc-P. Our pipeline RNA-unchained greatly improves computation time of LocARNA and is comparable to the one of ExpLoc-P, while improving the overall accuracy of the final alignments.

BRASERO: A Resource for Benchmarking RNA Secondary Structure Comparison Algorithms.

The pairwise comparison of RNA secondary structures is a fundamental problem, with direct application in mining databases for annotating putative noncoding RNA candidates in newly sequenced genomes. An increasing number of software tools are available for comparing RNA secondary structures, based on different models (such as ordered trees or forests, arc annotated sequences, and multilevel trees) and computational principles (edit distance, alignment). We describe here the website BRASERO that offers tools for evaluating such software tools on real and synthetic datasets.

A new distance for high level RNA secondary structure comparison.

We describe an algorithm for comparing two RNA secondary structures coded in the form of trees that introduces two new operations, called node fusion and edge fusion, besides the tree edit operations of deletion, insertion, and relabeling classically used in the literature. This allows us to address some serious limitations of the more traditional tree edit operations when the trees represent RNAs and what is searched for is a common structural core of two RNAs. Although the algorithm complexity has an exponential term, this term depends only on the number of successive fusions that may be applied to a same node, not on the total number of fusions. The algorithm remains therefore efficient in practice and is used for illustrative purposes on ribosomal as well as on other types of RNAs.