Design, construction, and technical implementation of a web-based interdisciplinary symptom evaluation (WISE) - a heuristic proposal for orofacial pain and temporomandibular disorders.

BACKGROUND: Medical symptoms independent of body location burden individuals to varying degrees and may require care by more than one expert. Various paper and computer-based tools exist that aim to comprehensively capture data for optimal clinical management and research. METHODS: A web-based interdisciplinary symptom evaluation (WISE) was newly designed, constructed, and technically implemented. For worldwide applicability and to avoid copyright infringements, open source software tools and free validated questionnaires available in multiple languages were used. Highly secure data storage limits access strictly to those who use the tool for collecting, storing, and evaluating their data. Concept and implementation is illustrated by a WISE sample tailored for the requirements of a single center in Switzerland providing interdisciplinary care to orofacial pain and temporomandibular disorder patients. RESULTS: By combining a symptom- burden checklist with in-depth questionnaires serving as case-finding instruments, an algorithm was developed that assists in clarifying case complexity and need for targeted expert evaluation. This novel modular approach provides a personalized, response-tailored instrument for the time- and cost-effective collection of symptom-burden focused quantitative data. The tool includes body drawing options and instructional videos. It is applicable for biopsychosocial evaluation in a variety of clinical settings and offers direct feedback by a case report summary. CONCLUSIONS: In clinical practice, the new instrument assists in clarifying case complexity and referral need, based on symptom burden and response -tailored case finding. It provides single-case summary reports from a biopsychosocial perspective and includes graphical symptom maps. Secure, centrally stored data collection of anonymous data is possible. The tool enables personalized medicine, facilitates interprofessional education and collaboration, and allows for multicenter patient-reported outcomes research.

SparkSeq: fast, scalable and cloud-ready tool for the interactive genomic data analysis with nucleotide precision.

UNLABELLED: Many time-consuming analyses of next -: generation sequencing data can be addressed with modern cloud computing. The Apache Hadoop-based solutions have become popular in genomics BECAUSE OF: their scalability in a cloud infrastructure. So far, most of these tools have been used for batch data processing rather than interactive data querying. The SparkSeq software has been created to take advantage of a new MapReduce framework, Apache Spark, for next-generation sequencing data. SparkSeq is a general-purpose, flexible and easily extendable library for genomic cloud computing. It can be used to build genomic analysis pipelines in Scala and run them in an interactive way. SparkSeq opens up the possibility of customized ad hoc secondary analyses and iterative machine learning algorithms. This article demonstrates its scalability and overall fast performance by running the analyses of sequencing datasets. Tests of SparkSeq also prove that the use of cache and HDFS block size can be tuned for the optimal performance on multiple worker nodes. AVAILABILITY AND IMPLEMENTATION: Available under open source Apache 2.0 license: https://bitbucket.org/mwiewiorka/sparkseq/.

gcodeml: a Grid-enabled tool for detecting positive selection in biological evolution.

One of the important questions in biological evolution is to know if certain changes along protein coding genes have contributed to the adaptation of species. This problem is known to be biologically complex and computationally very expensive. It, therefore, requires efficient Grid or cluster solutions to overcome the computational challenge. We have developed a Grid-enabled tool (gcodeml) that relies on the PAML (codeml) package to help analyse large phylogenetic datasets on both Grids and computational clusters. Although we report on results for gcodeml, our approach is applicable and customisable to related problems in biology or other scientific domains.

SwissPIT: An workflow-based platform for analyzing tandem-MS spectra using the Grid.

The identification and characterization of peptides from MS/MS data represents a critical aspect of proteomics. It has been the subject of extensive research in bioinformatics resulting in the generation of a fair number of identification software tools. Most often, only one program with a specific and unvarying set of parameters is selected for identifying proteins. Hence, a significant proportion of the experimental spectra do not match the peptide sequences in the screened database due to inappropriate parameters or scoring schemes. The Swiss protein identification toolbox (swissPIT) project provides the scientific community with an expandable multitool platform for automated in-depth analysis of MS data also able to handle data from high-throughput experiments. With swissPIT many problems have been solved: The missing standards for input and output formats (A), creation of analysis workflows (B), unified result visualization (C), and simplicity of the user interface (D). Currently, swissPIT supports four different programs implementing two different search strategies to identify MS/MS spectra. Conceived to handle the calculation-intensive needs of each of the programs, swissPIT uses the distributed resources of a Swiss-wide computer Grid (http://www.swing-grid.ch).

swissPIT: a novel approach for pipelined analysis of mass spectrometry data.

The identification and characterization of peptides from tandem mass spectrometry (MS/MS) data represents a critical aspect of proteomics. Today, tandem MS analysis is often performed by only using a single identification program achieving identification rates between 10-50% (Elias and Gygi, 2007). Beside the development of new analysis tools, recent publications describe also the pipelining of different search programs to increase the identification rate (Hartler et al., 2007; Keller et al., 2005). The Swiss Protein Identification Toolbox (swissPIT) follows this approach, but goes a step further by providing the user an expandable multi-tool platform capable of executing workflows to analyze tandem MS-based data. One of the major problems in proteomics is the absent of standardized workflows to analyze the produced data. This includes the pre-processing part as well as the final identification of peptides and proteins. The main idea of swissPIT is not only the usage of different identification tool in parallel, but also the meaningful concatenation of different identification strategies at the same time. The swissPIT is open source software but we also provide a user-friendly web platform, which demonstrates the capabilities of our software and which is available at http://swisspit.cscs.ch upon request for account.