A general strategy to determine the congruence between a hierarchical and a non-hierarchical classification

Analyses of nuclearly encoded mitochondrial genes suggest gene duplication as a mechanism for resolving intralocus sexually antagonistic conflict in Drosophila

Gene duplication is probably the most important mechanism for generating new gene functions. However, gene duplication has been overlooked as a potentially effective way to resolve genetic conflicts. Here, we analyze the entire set of Drosophila melanogaster nuclearly encoded mitochondrial duplicate genes and show that both RNA- and DNA-mediated mitochondrial gene duplications exhibit an unexpectedly high rate of relocation (change in location between parental and duplicated gene) as well as an extreme tendency to avoid the X chromosome. These trends are likely related to our observation that relocated genes tend to have testis-specific expression. We also infer that these trends hold across the entire Drosophila genus. Importantly, analyses of gene ontology and functional interaction networks show that there is an overrepresentation of energy production-related functions in these mitochondrial duplicates. We discuss different hypotheses to explain our results and conclude that our findings substantiate the hypothesis that gene duplication for male germline function is likely a mechanism to resolve intralocus sexually antagonistic conflicts that we propose are common in testis. In the case of nuclearly encoded mitochondrial duplicates, our hypothesis is that past sexually antagonistic conflict related to mitochondrial energy function in Drosophila was resolved by gene duplication.

Jerarca: efficient analysis of complex networks using hierarchical clustering

Background

How to extract useful information from complex biological networks is a major goal in many fields, especially in genomics and proteomics. We have shown in several works that iterative hierarchical clustering, as implemented in the UVCluster program, is a powerful tool to analyze many of those networks. However, the amount of computation time required to perform UVCluster analyses imposed significant limitations to its use.

Methodology/Principal Findings

We describe the suite Jerarca, designed to efficiently convert networks of interacting units into dendrograms by means of iterative hierarchical clustering. Jerarca is divided into three main sections. First, weighted distances among units are computed using up to three different approaches: a more efficient version of UVCluster and two new, related algorithms called RCluster and SCluster. Second, Jerarca builds dendrograms based on those distances, using well-known phylogenetic algorithms, such as UPGMA or Neighbor-Joining. Finally, Jerarca provides optimal partitions of the trees using statistical criteria based on the distribution of intra- and intercluster connections. Outputs compatible with the phylogenetic software MEGA and the Cytoscape package are generated, allowing the results to be easily visualized.

Conclusions/Significance

The four main advantages of Jerarca in respect to UVCluster are: 1) Improved speed of a novel UVCluster algorithm; 2) Additional, alternative strategies to perform iterative hierarchical clustering; 3) Automatic evaluation of the hierarchical trees to obtain optimal partitions; and, 4) Outputs compatible with popular software such as MEGA and Cytoscape.

A reference guide for tree analysis and visualization

The quantities of data obtained by the new high-throughput technologies, such as microarrays or ChIP-Chip arrays, and the large-scale OMICS-approaches, such as genomics, proteomics and transcriptomics, are becoming vast. Sequencing technologies become cheaper and easier to use and, thus, large-scale evolutionary studies towards the origins of life for all species and their evolution becomes more and more challenging. Databases holding information about how data are related and how they are hierarchically organized expand rapidly. Clustering analysis is becoming more and more difficult to be applied on very large amounts of data since the results of these algorithms cannot be efficiently visualized. Most of the available visualization tools that are able to represent such hierarchies, project data in 2D and are lacking often the necessary user friendliness and interactivity. For example, the current phylogenetic tree visualization tools are not able to display easy to understand large scale trees with more than a few thousand nodes. In this study, we review tools that are currently available for the visualization of biological trees and analysis, mainly developed during the last decade. We describe the uniform and standard computer readable formats to represent tree hierarchies and we comment on the functionality and the limitations of these tools. We also discuss on how these tools can be developed further and should become integrated with various data sources. Here we focus on freely available software that offers to the users various tree-representation methodologies for biological data analysis.

Interactome and Gene Ontology provide congruent yet subtly different views of a eukaryotic cell

BACKGROUND

The characterization of the global functional structure of a cell is a major goal in bioinformatics and systems biology. Gene Ontology (GO) and the protein-protein interaction network offer alternative views of that structure.

RESULTS

This study presents a comparison of the global structures of the Gene Ontology and the interactome of Saccharomyces cerevisiae. Sensitive, unsupervised methods of clustering applied to a large fraction of the proteome led to establish a GO-interactome correlation value of +0.47 for a general dataset that contains both high and low-confidence interactions and +0.58 for a smaller, high-confidence dataset.

CONCLUSION

The structures of the yeast cell deduced from GO and interactome are substantially congruent. However, some significant differences were also detected, which may contribute to a better understanding of cell function and also to a refinement of the current ontologies.

Interspliced transcription chimeras: neglected pathological mechanism infiltrating gene accession queries?

Over half of the DNA of mammalian genomes is transcribed, and one of the emerging enigmas in the field of RNA research is intergenic splicing or transcription induced chimerism. We argue that fused low-copy-number transcripts constitute neglected pathological mechanism akin to copy number variation, due to loss of stoichiometric subunit ratios in protein complexes. An obstacle for transcriptomics meta-analysis of published microarrays is the traditional nomenclature of merged transcript neighbors under same accession codes. Tandem transcripts cover 4-20% of genomes but are only loosely overlapping in population. They were most enriched in systems medicine annotations concerning neurology, thalassemia and genital disorders in the GeneGo Inc. MetaCore-MetaDrug(TM) knowledgebase, evaluated with external randomizations here. Clinical transcriptomics is good news since new disease etiologies offer new remedies. We identified homeotic HOX-transfactors centered around BMI-1, the Grb2 adaptor network, the kallikrein system, and thalassemia RNA surveillance as vulnerable hotspot chimeras. As a cure, RNA interference would require verification of chimerism from symptomatic tissue contra healthy control tissue from the same patient.