Background: Large multigene sequence alignments have over recent years been increasingly employed for phylogenomic reconstruction of the eukaryote tree of life. Such supermatrices of sequence data are preferred over single gene alignments as they contain vastly more information about ancient sequence characteristics, and are thus more suitable for resolving deeply diverging relationships. However, as alignments are expanded, increasingly numbers of sites with misleading phylogenetic information are also added. Therefore, a major goal in phylogenomic analyses is to maximize the ratio of information to noise; this can be achieved by the reduction of fast evolving sites. Results: Here we present a batch-oriented web-based program package, named AIR that allows 1) transformation of several single genes to one multigene alignment, 2) identification of evolutionary rates in multigene alignments and 3) removal of fast evolving sites. These three processes can be done with the programs AIR-A ppender, AIR-I dentifier, and AIR-R emover (AIR), which can be used independently or in a semi-automated pipeline. AIR produces user-friendly output files with filtered and non-filtered alignments where residues are colored according to their evolutionary rates. Other bioinformatics applications linked to the AIR package are available at the Bioportal http://www.bioportal.uio.no, University of Oslo; together these greatly improve the flexibility, efficiency and quality of phylogenomic analyses. Conclusion: The AIR program package allows for efficient creation of multigene alignments and better assessment of evolutionary rates in sequence alignments. Removing fast evolving sites with the AIR programs has been employed in several recent phylogenomic analyses resulting in improved phylogenetic resolution and increased statistical support for branching patterns among the early diverging eukaryotes.

Understanding the early evolution and diversification of eukaryotes relies on a fully resolved phylogenetic tree. In recent years, most eukaryotic diversity has been assigned to six putative supergroups, but the evolutionary origin of a few major “orphan” lineages remains elusive. Two ecologically important orphan groups are the heterotrophic Telonemia and Centroheliozoa. Telonemids have been proposed to be related to the photosynthetic cryptomonads or stramenopiles and centrohelids to haptophytes, but molecular phylogenies have failed to provide strong support for any phylogenetic hypothesis. Here, we investigate the origins of Telonema subtilis (a telonemid) and Raphidiophrys contractilis (a centrohelid) by large-scale 454 pyrosequencing of cDNA libraries and including new genomic data from two cryptomonads (Guillardia theta and Plagioselmis nannoplanctica) and a haptophyte (Imantonia rotunda). We demonstrate that 454 sequencing of cDNA libraries is a powerful and fast method of sampling a high proportion of protist genes, which can yield ample information for phylogenomic studies. Our phylogenetic analyses of 127 genes from 72 species indicate that telonemids and centrohelids are members of an emerging major group of eukaryotes also comprising cryptomonads and haptophytes. Furthermore, this group is possibly closely related to the SAR clade comprising stramenopiles (heterokonts), alveolates, and Rhizaria. Our results link two additional heterotrophic lineages to the predominantly photosynthetic chromalveolate supergroup, providing a new framework for interpreting the evolution of eukaryotic cell structures and the diversification of plastids.

Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes. Tumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a “cluster-of-clusters” approach with consensus clustering. Based on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed. The six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer.