Performance database: capturing data for optimizing distributed streaming workflows.

The performance database (PDB) stores performance-related data gathered during workflow enactment. We argue that, by carefully understanding and manipulating these data, we can improve efficiency when enacting workflows. This paper describes the rationale behind the PDB, and proposes a systematic way to implement it. The prototype is built as part of the Advanced Data Mining and Integration Research for Europe project. We use workflows from real-world experiments to demonstrate the usage of PDB.

Validation and mismatch repair of workflows through typed data streams.

The type system of a language guarantees that all of the operations on a set of data comply with the rules and conditions set by the language. While language typing is a fundamental requirement for any programming language, the typing of data that flow between processing elements within a workflow is currently being treated as optional. In this paper, we introduce a three-level type system for typing workflow data streams. These types are parts of the Data Intensive System Process Engineering Language programming language, which empowers users with the ability to validate the connections inside a workflow composition, and apply appropriate data type conversions when necessary. Furthermore, this system enables the enactment engine in carrying out type-directed workflow optimizations.

A user-friendly web portal for T-Coffee on supercomputers.

BACKGROUND: Parallel T-Coffee (PTC) was the first parallel implementation of the T-Coffee multiple sequence alignment tool. It is based on MPI and RMA mechanisms. Its purpose is to reduce the execution time of the large-scale sequence alignments. It can be run on distributed memory clusters allowing users to align data sets consisting of hundreds of proteins within a reasonable time. However, most of the potential users of this tool are not familiar with the use of grids or supercomputers. RESULTS: In this paper we show how PTC can be easily deployed and controlled on a super computer architecture using a web portal developed using Rapid. Rapid is a tool for efficiently generating standardized portlets for a wide range of applications and the approach described here is generic enough to be applied to other applications, or to deploy PTC on different HPC environments. CONCLUSIONS: The PTC portal allows users to upload a large number of sequences to be aligned by the parallel version of TC that cannot be aligned by a single machine due to memory and execution time constraints. The web portal provides a user-friendly solution.

Automatically identifying and annotating mouse embryo gene expression patterns.

MOTIVATION: Deciphering the regulatory and developmental mechanisms for multicellular organisms requires detailed knowledge of gene interactions and gene expressions. The availability of large datasets with both spatial and ontological annotation of the spatio-temporal patterns of gene expression in mouse embryo provides a powerful resource to discover the biological function of embryo organization. Ontological annotation of gene expressions consists of labelling images with terms from the anatomy ontology for mouse development. If the spatial genes of an anatomical component are expressed in an image, the image is then tagged with a term of that anatomical component. The current annotation is done manually by domain experts, which is both time consuming and costly. In addition, the level of detail is variable, and inevitably errors arise from the tedious nature of the task. In this article, we present a new method to automatically identify and annotate gene expression patterns in the mouse embryo with anatomical terms. RESULTS: The method takes images from in situ hybridization studies and the ontology for the developing mouse embryo, it then combines machine learning and image processing techniques to produce classifiers that automatically identify and annotate gene expression patterns in these images. We evaluate our method on image data from the EURExpress study, where we use it to automatically classify nine anatomical terms: humerus, handplate, fibula, tibia, femur, ribs, petrous part, scapula and head mesenchyme. The accuracy of our method lies between 70% and 80% with few exceptions. We show that other known methods have lower classification performance than ours. We have investigated the images misclassified by our method and found several cases where the original annotation was not correct. This shows our method is robust against this kind of noise. AVAILABILITY: The annotation result and the experimental dataset in the article can be freely accessed at http://www2.docm.mmu.ac.uk/STAFF/L.Han/geneannotation/. CONTACT: l.han@mmu.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An open source toolkit for medical imaging de-identification.

OBJECTIVE: Medical imaging acquired for clinical purposes can have several legitimate secondary uses in research projects and teaching libraries. No commonly accepted solution for anonymising these images exists because the amount of personal data that should be preserved varies case by case. Our objective is to provide a flexible mechanism for anonymising Digital Imaging and Communications in Medicine (DICOM) data that meets the requirements for deployment in multicentre trials. METHODS: We reviewed our current de-identification practices and defined the relevant use cases to extract the requirements for the de-identification process. We then used these requirements in the design and implementation of the toolkit. Finally, we tested the toolkit taking as a reference those requirements, including a multicentre deployment. RESULTS: The toolkit successfully anonymised DICOM data from various sources. Furthermore, it was shown that it could forward anonymous data to remote destinations, remove burned-in annotations, and add tracking information to the header. The toolkit also implements the DICOM standard confidentiality mechanism. CONCLUSION: A DICOM de-identification toolkit that facilitates the enforcement of privacy policies was developed. It is highly extensible, provides the necessary flexibility to account for different de-identification requirements and has a low adoption barrier for new users.

Correcting for intra-experiment variation in Illumina BeadChip data is necessary to generate robust gene-expression profiles.

BACKGROUND: Microarray technology is a popular means of producing whole genome transcriptional profiles, however high cost and scarcity of mRNA has led many studies to be conducted based on the analysis of single samples. We exploit the design of the Illumina platform, specifically multiple arrays on each chip, to evaluate intra-experiment technical variation using repeated hybridisations of universal human reference RNA (UHRR) and duplicate hybridisations of primary breast tumour samples from a clinical study. RESULTS: A clear batch-specific bias was detected in the measured expressions of both the UHRR and clinical samples. This bias was found to persist following standard microarray normalisation techniques. However, when mean-centering or empirical Bayes batch-correction methods (ComBat) were applied to the data, inter-batch variation in the UHRR and clinical samples were greatly reduced. Correlation between replicate UHRR samples improved by two orders of magnitude following batch-correction using ComBat (ranging from 0.9833-0.9991 to 0.9997-0.9999) and increased the consistency of the gene-lists from the duplicate clinical samples, from 11.6% in quantile normalised data to 66.4% in batch-corrected data. The use of UHRR as an inter-batch calibrator provided a small additional benefit when used in conjunction with ComBat, further increasing the agreement between the two gene-lists, up to 74.1%. CONCLUSION: In the interests of practicalities and cost, these results suggest that single samples can generate reliable data, but only after careful compensation for technical bias in the experiment. We recommend that investigators appreciate the propensity for such variation in the design stages of a microarray experiment and that the use of suitable correction methods become routine during the statistical analysis of the data.

Towards a virtual fly brain.

Models of the brain that simulate sensory input, behavioural output and information processing in a biologically plausible manner pose significant challenges to both computer science and biology. Here we investigated strategies that could be used to create a model of the insect brain, specifically that of Drosophila melanogaster that is very widely used in laboratory research. The scale of the problem is an order of magnitude above the most complex of the current simulation projects, and it is further constrained by the relative sparsity of available electrophysiological recordings from the fly nervous system. However, fly brain research at the anatomical and behavioural levels offers some interesting opportunities that could be exploited to create a functional simulation. We propose to exploit these strengths of Drosophila central nervous system research to focus on a functional model that maps biologically plausible network architecture onto phenotypic data from neuronal inhibition and stimulation studies, leaving aside biophysical modelling of individual neuronal activity for future models until more data are available.

Evolving combinatorial problem instances that are difficult to solve.

This paper demonstrates how evolutionary computation can be used to acquire difficult to solve combinatorial problem instances. As a result of this technique, the corresponding algorithms used to solve these instances are stress-tested. The technique is applied in three important domains of combinatorial optimisation, binary constraint satisfaction, Boolean satisfiability, and the travelling salesman problem. The problem instances acquired through this technique are more difficult than the ones found in popular benchmarks. In this paper, these evolved instances are analysed with the aim to explain their difficulty in terms of structural properties, thereby exposing the weaknesses of corresponding algorithms.

Determination of dissociation constants of polyprotic acids from chromatographic data.

The separation and purification of drugs and biological compounds, which are typically weak polyprotic acids, by HPLC is the subject of numerous literature reports. The development of separation methodologies depends on the acid dissociation constants, and the HPLC offers a valuable method for determining these constants, especially when the compounds are poorly soluble in water. This review presents general basic equations and shows how they are used for determining the pKa's. It also discusses the parameters affecting the pKa's and the methods of their measurement as presented in a representative number of research papers published in the last 20 years.

General equation for calculating the dissociation constants of polyprotic acids and bases from measured retention factors in high-performance liquid chromatography.

A general equation relating the observed retention factor to the pH of the mobile phase, the dissociation constants, and the retention factors of the different ionic species has been derived. This equation is applicable to polyprotic weak acid and base dissociation events, that is, the secondary equilibria existing in the HPLC mobile phase. It is written as: [formula: see text] where the kr values are the retention factors of the dissociated species, and Ka(r), the product of the first r-dissociation constants, [formula: see text] is related to the pH of the mobile phase: x = 2.303.pH. The derived equation was used to calculate three dissociation constants of leukotriene E4. Also, a formula is established for calculating the range of pH values where an ionic species is most likely to be predominant in the mobile phase.