phangorn: phylogenetic analysis in R

SUMMARY: phangorn is a package for phylogenetic reconstruction and analysis in the R language. Previously it was only possible to estimate phylogenetic trees with distance methods in R. phangorn, now offers the possibility of reconstructing phylogenies with distance based methods, maximum parsimony or maximum likelihood (ML) and performing Hadamard conjugation. Extending the general ML framework, this package provides the possibility of estimating mixture and partition models. Furthermore, phangorn offers several functions for comparing trees, phylogenetic models or splits, simulating character data and performing congruence analyses. AVAILABILITY: phangorn can be obtained through the CRAN homepage http://cran.r-project.org/web/packages/phangorn/index.html. phangorn is licensed under GPL 2.     

Partially-supervised protein subclass discovery with simultaneous annotation of functional residues

Background  

The study of functional subfamilies of protein domain families and the identification of the residues which determine substrate specificity is an important question in the analysis of protein domains. One way to address this question is the use of clustering methods for protein sequence data and approaches to predict functional residues based on such clusterings. The locations of putative functional residues in known protein structures provide insights into how different substrate specificities are reflected on the protein structure level.     

Laboratory diagnosis of melioidosis: Past,present and future

Melioidosis is an emerging, potentially fatal disease caused by Burkholderia pseudomallei, which requires prolonged antibiotic treatment to prevent disease relapse. However, difficulties in laboratory diagnosis of melioidosis may delay treatment and affect disease outcomes. Isolation of B. pseudomallei from clinical specimens has been improved with the use of selective media. However, even with positive cultures, identification of B. pseudomallei can be difficult in clinical microbiology laboratories, especially in non-endemic areas where clinical suspicion is low. Commercial identification systems may fail to distinguish between B. pseudomallei and closely related species such as Burkholderia thailandensis. Genotypic identification of suspected isolates can be achieved by sequencing of gene targets such as groEL which offer higher discriminative power than 16S rRNA. Specific PCR-based identification of B. pseudomallei has also been developed using B. pseudomallei-specific gene targets such as Type III secretion system and Tat-domain protein. Matrix-assisted laser desorption ionization time-of-flight mass spectrometry, a revolutionary technique for pathogen identification, has been shown to be potentially useful for rapid identification of B. pseudomallei, although existing databases require optimization by adding reference spectra for B. pseudomallei. Despite these advances in bacterial identification, diagnostic problems encountered in culture-negative cases remain largely unresolved. Although various serological tests have been developed, they are generally unstandardized “in house” assays and have low sensitivities and specificities. Although specific PCR assays have been applied to direct clinical and environmental specimens, the sensitivities for diagnosis remain to be evaluated. Metabolomics is an uprising tool for studying infectious diseases and may offer a novel approach for exploring potential diagnostic biomarkers. The metabolomics profiles of B. pseudomallei culture supernatants can be potentially distinguished from those of related bacterial species including B. thailandensis. Further studies using bacterial cultures and direct patient samples are required to evaluate the potential of metabolomics for improving diagnosis of melioidosis.     

Harvesting evolutionary signals in a forest of prokaryotic gene trees

Phylogenomic studies produce increasingly large phylogenetic forests of trees with patchy taxonomical sampling. Typically, prokaryotic data generate thousands of gene trees of all sizes that are difficult, if not impossible, to root. Their topologies do not match the genealogy of lineages, as they are influenced not only by duplication, losses, and vertical descent but also by lateral gene transfer (LGT) and recombination. Because this complexity in part reflects the diversity of evolutionary processes, the study of phylogenetic forests is thus a great opportunity to improve our understanding of prokaryotic evolution. Here, we show how the rich evolutionary content of such novel phylogenetic objects can be exploited through the development of new approaches designed specifically for extracting the multiple evolutionary signals present in the forest of life, that is, by slicing up trees into remarkable bits and pieces: clans, slices, and clips. We harvested a forest of 6,901 unrooted gene trees comprising up to 100 prokaryotic genomes (41 archaea and 59 bacteria) to search for evolutionary events that a species tree would not account for. We identified 1) trees and partitions of trees that reflected the lifestyle of organisms rather than their taxonomy, 2) candidate lifestyle-specific genetic modules, used by distinct unrelated organisms to adapt to the same environment, 3) gene families, nonrandomly distributed in the functional space, that were frequently exchanged between archaea and bacteria, sometimes without major changes in their sequences. Finally, 4) we reconstructed polarized networks of genetic partnerships between archaea and bacteria to describe some of the rules affecting LGT between these two Domains.     

Clustering cancer gene expression data: a comparative study

Background  

The use of clustering methods for the discovery of cancer subtypes has drawn a great deal of attention in the scientific community. While bioinformaticians have proposed new clustering methods that take advantage of characteristics of the gene expression data, the medical community has a preference for using "classic" clustering methods. There have been no studies thus far performing a large-scale evaluation of different clustering methods in this context.