Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal OmegaMultiple sequence alignments are fundamental to many sequence analysis methods. The new program Clustal Omega can align virtually any number of protein sequences quickly and has powerful features for adding sequences to existing precomputed alignments.

Supplementary data are available at Bioinformatics online.

The PhyloFacts ‘Fast Approximate Tree Classification’ (FAT-CAT) web server provides a novel approach to ortholog identification using subtree hidden Markov model-based placement of protein sequences to phylogenomic orthology groups in the PhyloFacts database. Results on a data set of microbial, plant and animal proteins demonstrate FAT-CAT’s high precision at separating orthologs and paralogs and robustness to promiscuous domains. We also present results documenting the precision of ortholog identification based on subtree hidden Markov model scoring. The FAT-CAT phylogenetic placement is used to derive a functional annotation for the query, including confidence scores and drill-down capabilities. PhyloFacts’ broad taxonomic and functional coverage, with >7.3 M proteins from across the Tree of Life, enables FAT-CAT to predict orthologs and assign function for most sequence inputs. Four pipeline parameter presets are provided to handle different sequence types, including partial sequences and proteins containing promiscuous domains; users can also modify individual parameters. PhyloFacts trees matching the query can be viewed interactively online using the PhyloScope Javascript tree viewer and are hyperlinked to various external databases. The FAT-CAT web server is available at http://phylogenomics.berkeley.edu/phylofacts/fatcat/.

The accurate computational prediction of transcription start sites (TSS) in vertebrate genomes is a difficult problem. The physicochemical properties of DNA can be computed in various ways and a many combinations of DNA features have been tested in the past for use as predictors of transcription. We looked in detail at melting temperature, which measures the temperature, at which two strands of DNA separate, considering the cooperative nature of this process. We find that peaks in melting temperature correspond closely to experimentally determined transcription start sites in human and mouse chromosomes. Using melting temperature alone, and with simple thresholding, we can predict TSS with accuracy that is competitive with the most accurate state-of-the-art TSS prediction methods. Accuracy is measured using both experimentally and manually determined TSS. The method works especially well with CpG island containing promoters, but also works when CpG islands are absent. This result is clear evidence of the important role of the physical properties of DNA in the process of transcription. It also points to the importance for TSS prediction methods to include melting temperature as prior information.

BackgroundThe computational prediction of transcription start sites is an important unsolved problem. Some recent progress has been made, but many promoters, particularly those not associated with CpG islands, are still difficult to locate using current methods. These methods use different features and training sets, along with a variety of machine learning techniques and result in different prediction sets.ResultsWe demonstrate the heterogeneity of current prediction sets, and take advantage of this heterogeneity to construct a two-level classifier ('Profisi Ensemble') using predictions from 7 programs, along with 2 other data sources. Support vector machines using 'full' and 'reduced' data sets are combined in an either/or approach. We achieve a 14% increase in performance over the current state-of-the-art, as benchmarked by a third-party tool.ConclusionsSupervised learning methods are a useful way to combine predictions from diverse sources.

Background: Nurses play a key role in providing discharge education. With the increased demand for orthopaedic surgery and subsequent fast-track surgical programmes resulting in reduction in hospital length of stay, obtaining patient feedback about discharge is important to inform nursing practice of discharge.Aim: To explore patients' experiences of discharge from hospital following orthopaedic surgery.Methods: A descriptive qualitative study was undertaken with a sample of 34 patients discharged following orthopaedic surgery at a private acute Australian hospital. Individual semistructured telephone interviews were conducted and analysed using inductive thematic analysis.Findings: From the analysis, patient experiences have been described in three themes: (1) experiences of hospital discharge, (2) perceptions of discharge information, and (3) limitations of discharge information. Although participants reported being informed when discharged from hospital, more information about medication management, constipation, and wound care would have better supported their recovery to assist in their self-care.Discussion: Discharge experiences and perceptions varied between participants, highlighting the importance of nurses and other health professionals, in providing discharge information to meet individual patient needs. This included improved communication, information about the discharge process, management of medication, wound, and prevention of constipation as part of recovery. Conclusion:Patient feedback has highlighted that nurses need to provide more tailored discharge information for orthopaedic patients to support recovery to prevent postdischarge problems and hospital readmission.

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