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Abstract Background Phylogenetic reconstruction methods based on gene content often place all the parasitic and endosymbiotic eubacteria (parasites for short) together in a clan. Many other lines of evidence point to this parasites clan being an artefact. This artefact could be a consequence of the methods used to construct ortholog databases (due to some unknown bias), the methods used to estimate the phylogeny, or both. We test the idea that the parasites clan is an ortholog identification artefact by analyzing three different ortholog databases (COG, TRIBES, and OFAM), which were constructed using different methods, and are thus unlikely to share the same biases. In each case, we estimate a phylogeny using an improved version of the conditioned logdet distance method. If the parasites clan appears in trees from all three databases, it is unlikely to be an ortholog identification artefact. Accelerated loss of a subset of gene families in parasites (a form of heterotachy) may contribute to the difficulty of estimating a phylogeny from gene content data. We test the idea that heterotachy is the underlying reason for the estimation of an artefactual parasites clan by applying two different mixture models (phylogenetic and non-phylogenetic), in combination with conditioned logdet. In these models, there are two categories of gene families, one of which has accelerated loss in parasites. Distances are estimated separately from each category by conditioned logdet. This should reduce the tendency for tree estimation methods to group the parasites together, if heterotachy is the underlying reason for estimation of the parasites clan. Results The parasites clan appears in conditioned logdet trees estimated from all three databases. This makes it less likely to be an artefact of database construction. The non-phylogenetic mixture model gives trees without a parasites clan. However, the phylogenetic mixture model still results in a tree with a parasites clan. Thus, it is not entirely clear whether heterotachy is the underlying reason for the estimation of a parasites clan. Simulation studies suggest that the phylogenetic mixture model approach may be unsuccessful because the model of gene family gain and loss it uses does not adequately describe the real data. Conclusions The most successful methods for estimating a reliable phylogenetic tree for parasitic and endosymbiotic eubacteria from gene content data are still ad-hoc approaches such as the SHOT distance method. however, the improved conditioned logdet method we developed here may be useful for non-parasites and can be accessed at http://www.liv.ac.uk/~cgrbios/cond_logdet.html

Innovations in -omics technologies have driven advances in biomedical research. However, integrating and analysing the large volumes of data generated from different high-throughput -omics technologies remain a significant challenge to basic and clinical scientists without bioinformatics skills or access to bioinformatics support. To address this demand, we have significantly updated our previous O-miner analytical suite, to incorporate several new features and data types to provide an efficient and easy-to-use Web tool for the automated analysis of data from '-omics' technologies. Created from a biologist's perspective, this tool allows for the automated analysis of large and complex transcriptomic, genomic and methylomic data sets, together with biological/clinical information, to identify significantly altered pathways and prioritize novel biomarkers/targets for biological validation. Our resource can be used to analyse both in-house data and the huge amount of publicly available information from array and sequencing platforms. Multiple data sets can be easily combined, allowing for meta-analyses. Here, we describe the analytical pipelines currently available in O-miner and present examples of use to demonstrate its utility and relevance in maximizing research output. O-miner Web server is free to use and is available at http://www.o-miner.org. [ABSTRACT FROM AUTHOR]

Currently, the majority of patients diagnosed with pancreatic ductal adenocarcinoma (PDAC) present with locally invasive and/or metastatic disease, resulting in five-year survival of less than 5%. The development of an early diagnostic test is, therefore, expected to significantly impact the patient's prognosis. In this study, we successfully evaluated the feasibility of identifying diagnostic cell free microRNAs (miRNAs) for early stage PDAC, through the analysis of urine samples. Using Affymetrix microarrays, we established a global miRNA profile of 13 PDAC, six chronic pancreatitis (CP), and seven healthy (H) urine specimens. Selected differentially expressed miRNAs were subsequently investigated using an independent technique (RT-PCR) on 101 urine samples including 46 PDAC, 29 CP and 26 H. Receiver operating characteristic (ROC) and logistic regression analyses were applied to determine the discriminatory potential of the candidate miRNA biomarkers. Three miRNAs (miR-143, miR-223, and miR-30e) were significantly over-expressed in patients with Stage I cancer when compared with age-matched healthy individuals (P=0.022, 0.035 and 0.04, respectively); miR-143, miR-223 and miR-204 were also shown to be expressed at higher levels in Stage I compared to Stages II-IV PDAC (P=0.025, 0.013 and 0.008, respectively). Furthermore, miR-223 and miR-204 were able to distinguish patients with early stage cancer from patients with CP (P=0.037 and 0.036). Among the three biomarkers, miR-143 was best able to differentiate Stage I (n=6) from healthy (n=26) with area under the curve (AUC) of 0.862 (95% CI 0.695-1.000), with sensitivity (SN) of 83.3% (95% CI 50.0-100.0), and specificity (SP) of 88.5% (95% CI 73.1-100.0). The combination of miR-143 with miR-30e was significantly better at discriminating between these two groups, achieving an AUC of 0.923 (95% CI 0.793-1.000), with SN of 83.3% (95% CI 50.0-100.0) and SP of 96.2% (95% CI 88.5-100.0). In this feasibility study, we demonstrate for the first time the utility of miRNA biomarkers for non-invasive, early detection of PDAC in urine specimens.