Many metabolomics, and other high-content or high-throughput, experiments are set up such that the primary aim is the discovery of biomarker metabolites that can discriminate, with a certain level of certainty, between nominally matched ‘case’ and ‘control’ samples. However, it is unfortunately very easy to find markers that are apparently persuasive but that are in fact entirely spurious, and there are well-known examples in the proteomics literature. The main types of danger are not entirely independent of each other, but include bias, inadequate sample size (especially relative to the number of metabolite variables and to the required statistical power to prove that a biomarker is discriminant), excessive false discovery rate due to multiple hypothesis testing, inappropriate choice of particular numerical methods, and overfitting (generally caused by the failure to perform adequate validation and cross-validation). Many studies fail to take these into account, and thereby fail to discover anything of true significance (despite their claims). We summarise these problems, and provide pointers to a substantial existing literature that should assist in the improved design and evaluation of metabolomics experiments, thereby allowing robust scientific conclusions to be drawn from the available data. We provide a list of some of the simpler checks that might improve one’s confidence that a candidate biomarker is not simply a statistical artefact, and suggest a series of preferred tests and visualisation tools that can assist readers and authors in assessing papers. These tools can be applied to individual metabolites by using multiple univariate tests performed in parallel across all metabolite peaks. They may also be applied to the validation of multivariate models. We stress in particular that classical p-values such as “p < 0.05”, that are often used in biomedicine, are far too optimistic when multiple tests are done simultaneously (as in metabolomics). Ultimately it is desirable that all data and metadata are available electronically, as this allows the entire community to assess conclusions drawn from them. These analyses apply to all high-dimensional ‘omics’ datasets.

/pdf/statistical-strategies-for-avoiding-false-discoveries-in-397pr411pd.pdf

Statistical strategies for avoiding false discoveries in metabolomics and related experiments

Workflow technologies are emerging as the dominant approach to coordinate groups of distributed services. However with a space filled with competing specifications, standards and frameworks from multiple domains, choosing the right tool for the job is not always a straightforward task. Researchers are often unaware of the range of technology that already exists and focus on implementing yet another proprietary workflow system. As an antidote to this common problem, this paper presents a concise survey of existing workflow technology from the business and scientific domain and makes a number of key suggestions towards the future development of scientific workflow systems.

/pdf/scientific-workflow-a-survey-and-research-directions-28s08qa2bw.pdf

Scientific workflow: a survey and research directions

view, showing that multiple molecular pathways must be affected for cancer to develop, but with different specific proteins in each pathway mutated or differentially expressed in a given tumor (The Cancer Genome Atlas Research Network 2008; Parsons et al. 2008). Different studies demonstrated that while widespread mutations exist in cancer, not all mutations drive cancer development (Lin et al. 2007). This suggests a need to target only a deleterious subset of aberrant proteins, since any tre- ment must aim to improve health to justify its potential side effects. Treatment for cancer must become highly individualized, focusing on the specific aberrant driver proteins in an individual. This drives a need for informatics in cancer far beyond the need in other diseases. For instance, routine treatment with statins has become widespread for minimizing heart disease, with most patients responding to standard doses (Wilt et al. 2004). In contrast, standard treatment for cancer must become tailored to the molecular phenotype of an individual tumor, with each patient receiving a different combination of therapeutics aimed at the specific aberrant proteins driving the cancer. Tracking the aberrations that drive cancers, identifying biomarkers unique to each individual for molecular-level di- nosis and treatment response, monitoring adverse events and complex dosing schedules, and providing annotated molecular data for ongoing research to improve treatments comprise a major biomedical informatics need.

Biomedical Informatics for Cancer Research

During exploratory data analysis, visualizations are often useful for making sense of complex data sets. However, as data sets increase in size and complexity, static information visualizations decrease in comprehensibility. Interactive techniques can yield valuable discoveries, but current data analysis tools typically support only opportunistic exploration that may be inefficient and incomplete.We present a refined architecture that uses systematic yet flexible (SYF) design goals to guide domain expert users through complex exploration of data over days, weeks and months. The SYF system aims to support exploratory data analysis with some of the simplicity of an e-commerce check-out while providing added flexibility to pursue insights. The SYF system provides an overview of the analysis process, suggests unexplored states, allows users to annotate useful states, supports collaboration, and enables reuse of successful strategies. The affordances of the SYF system are demonstrated by integrating it into a social network analysis tool employed by social scientists and intelligence analysts. The SYF system is a tool-independent component and can be incorporated into other data analysis tools.

/pdf/systematic-yet-flexible-discovery-guiding-domain-experts-1kcvq5i6vp.pdf

Systematic yet flexible discovery: guiding domain experts through exploratory data analysis

https://air.unimi.it/bitstream/2434/201601/2/Enabling%20collaborative%20research%20using%20the%20Biomedical%20Informatics%20Research%20Network%20%28BIRN%29.pdf

Enabling collaborative research using the Biomedical Informatics Research Network (BIRN)

In its early development, Grid computing has focused on providing the computational power necessary for solving computationally intensive scientific problems. However, the scientific process in the life sciences is less demanding on computational power but contains a high degree of inherent heterogeneity, and semantic and task complexity. The myGrid project has developed a Grid-enabled middleware framework to manage this complexity associated with the scientific process within the bioinformatics domain. The drug discovery process is an example of a complex scientific problem that involves managing vast amounts of information. The technology developed by the myGrid project is applicable for managing many aspects of drug discovery and development by leveraging its technology for data storage, workflow enactment, change event notification, resource discovery and provenance management.

/pdf/mygrid-and-the-drug-discovery-process-3s3iw4zuzt.pdf

{myGrid} and the drug discovery process

As web service technology matures there is growing interest in exploiting workflow techniques to coordinate web services. Bioinformaticians are a user community who combine web resources to perform in silico experiments. These users are scientists and not information technology experts they require workflow solutions that have a low cost of entry for service users and providers. Problems satisfying these requirements with current techniques led to the development of the Simple conceptual unified flow language (Scufl). Scufl is supported by the Freefluo enactment engine [1], and the Taverna editing workbench [3]. The extensibility of Scufl, supported by these tools, means that workflows coordinating web services can be matched to how users view their problems. The Taverna workbench exploits the web to keep Scufl simple by retrieving detail from URIs when required, and by scavenging the web for services. Scufl and its tools are not bioinformatics specific. They can be exploited by other communities who require user-driven composition and execution of workflows coordinating web resources.

/pdf/delivering-web-service-coordination-capability-to-users-2kyjeyd3gg.pdf

Delivering web service coordination capability to users

The data management problems in grid computing are often challenging in many aspects such as data volumes, heterogeneity, structural complexity and semantic content. Thus, e-Scientists and scientific application developers stand to benefit from tools and environments that either hide, or help to manage, the inherent complexity involved in accessing and making concerted use of the diverse resources. This paper describes OGSA-DQP, a high level data integration tool for service-based grids, and illustrates how it can be used to support grid users, via an example scientific study in bioinformatics. The paper also discusses various options for employing OGSA-DQP to handle data integration tasks as service orchestrations involving both data and analysis services.

/pdf/using-ogsa-dqp-to-support-scientific-applications-for-the-1q1p0kja2k.pdf

Using OGSA-DQP to support scientific applications for the grid

The increasingly computationally- and data-intensive nature of experimental science motivates recent interest in workflows, as a way to specify complex data processing and integration pipelines in a fairly intuitive way. Such workflows orchestrate the invocation of data retrieval services in a way that resembles, to some extent, Search Computing query plans. While the former are manually specified, however, the latter are the result of an automated translation process. Using lessons learnt from experience in workflow design, in this chapter we discuss some of the requirements on service curation that make automated, on-demand data integration processes possible and realistic.

Workflows for information integration in the life sciences

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{myGrid} and the drug discovery process

Delivering web service coordination capability to users

Using OGSA-DQP to support scientific applications for the grid

Workflows for information integration in the life sciences