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1.  EPEPT: A web service for enhanced P-value estimation in permutation tests 
BMC Bioinformatics  2011;12:411.
In computational biology, permutation tests have become a widely used tool to assess the statistical significance of an event under investigation. However, the common way of computing the P-value, which expresses the statistical significance, requires a very large number of permutations when small (and thus interesting) P-values are to be accurately estimated. This is computationally expensive and often infeasible. Recently, we proposed an alternative estimator, which requires far fewer permutations compared to the standard empirical approach while still reliably estimating small P-values [1].
The proposed P-value estimator has been enriched with additional functionalities and is made available to the general community through a public website and web service, called EPEPT. This means that the EPEPT routines can be accessed not only via a website, but also programmatically using any programming language that can interact with the web. Examples of web service clients in multiple programming languages can be downloaded. Additionally, EPEPT accepts data of various common experiment types used in computational biology. For these experiment types EPEPT first computes the permutation values and then performs the P-value estimation. Finally, the source code of EPEPT can be downloaded.
Different types of users, such as biologists, bioinformaticians and software engineers, can use the method in an appropriate and simple way.
PMCID: PMC3277916  PMID: 22024252
2.  SEQADAPT: an adaptable system for the tracking, storage and analysis of high throughput sequencing experiments 
BMC Bioinformatics  2010;11:377.
High throughput sequencing has become an increasingly important tool for biological research. However, the existing software systems for managing and processing these data have not provided the flexible infrastructure that research requires.
Existing software solutions provide static and well-established algorithms in a restrictive package. However as high throughput sequencing is a rapidly evolving field, such static approaches lack the ability to readily adopt the latest advances and techniques which are often required by researchers. We have used a loosely coupled, service-oriented infrastructure to develop SeqAdapt. This system streamlines data management and allows for rapid integration of novel algorithms. Our approach also allows computational biologists to focus on developing and applying new methods instead of writing boilerplate infrastructure code.
The system is based around the Addama service architecture and is available at our website as a demonstration web application, an installable single download and as a collection of individual customizable services.
PMCID: PMC2916924  PMID: 20630057
3.  Adaptable data management for systems biology investigations 
BMC Bioinformatics  2009;10:79.
Within research each experiment is different, the focus changes and the data is generated from a continually evolving barrage of technologies. There is a continual introduction of new techniques whose usage ranges from in-house protocols through to high-throughput instrumentation. To support these requirements data management systems are needed that can be rapidly built and readily adapted for new usage.
The adaptable data management system discussed is designed to support the seamless mining and analysis of biological experiment data that is commonly used in systems biology (e.g. ChIP-chip, gene expression, proteomics, imaging, flow cytometry). We use different content graphs to represent different views upon the data. These views are designed for different roles: equipment specific views are used to gather instrumentation information; data processing oriented views are provided to enable the rapid development of analysis applications; and research project specific views are used to organize information for individual research experiments. This management system allows for both the rapid introduction of new types of information and the evolution of the knowledge it represents.
Data management is an important aspect of any research enterprise. It is the foundation on which most applications are built, and must be easily extended to serve new functionality for new scientific areas. We have found that adopting a three-tier architecture for data management, built around distributed standardized content repositories, allows us to rapidly develop new applications to support a diverse user community.
PMCID: PMC2670281  PMID: 19265554
4.  Systems biology driven software design for the research enterprise 
BMC Bioinformatics  2008;9:295.
In systems biology, and many other areas of research, there is a need for the interoperability of tools and data sources that were not originally designed to be integrated. Due to the interdisciplinary nature of systems biology, and its association with high throughput experimental platforms, there is an additional need to continually integrate new technologies. As scientists work in isolated groups, integration with other groups is rarely a consideration when building the required software tools.
We illustrate an approach, through the discussion of a purpose built software architecture, which allows disparate groups to reuse tools and access data sources in a common manner. The architecture allows for: the rapid development of distributed applications; interoperability, so it can be used by a wide variety of developers and computational biologists; development using standard tools, so that it is easy to maintain and does not require a large development effort; extensibility, so that new technologies and data types can be incorporated; and non intrusive development, insofar as researchers need not to adhere to a pre-existing object model.
By using a relatively simple integration strategy, based upon a common identity system and dynamically discovered interoperable services, a light-weight software architecture can become the focal point through which scientists can both get access to and analyse the plethora of experimentally derived data.
PMCID: PMC2478690  PMID: 18578887
5.  ProbCD: enrichment analysis accounting for categorization uncertainty 
BMC Bioinformatics  2007;8:383.
As in many other areas of science, systems biology makes extensive use of statistical association and significance estimates in contingency tables, a type of categorical data analysis known in this field as enrichment (also over-representation or enhancement) analysis. In spite of efforts to create probabilistic annotations, especially in the Gene Ontology context, or to deal with uncertainty in high throughput-based datasets, current enrichment methods largely ignore this probabilistic information since they are mainly based on variants of the Fisher Exact Test.
We developed an open-source R-based software to deal with probabilistic categorical data analysis, ProbCD, that does not require a static contingency table. The contingency table for the enrichment problem is built using the expectation of a Bernoulli Scheme stochastic process given the categorization probabilities. An on-line interface was created to allow usage by non-programmers and is available at: .
We present an analysis framework and software tools to address the issue of uncertainty in categorical data analysis. In particular, concerning the enrichment analysis, ProbCD can accommodate: (i) the stochastic nature of the high-throughput experimental techniques and (ii) probabilistic gene annotation.
PMCID: PMC2169266  PMID: 17935624
6.  Simcluster: clustering enumeration gene expression data on the simplex space 
BMC Bioinformatics  2007;8:246.
Transcript enumeration methods such as SAGE, MPSS, and sequencing-by-synthesis EST "digital northern", are important high-throughput techniques for digital gene expression measurement. As other counting or voting processes, these measurements constitute compositional data exhibiting properties particular to the simplex space where the summation of the components is constrained. These properties are not present on regular Euclidean spaces, on which hybridization-based microarray data is often modeled. Therefore, pattern recognition methods commonly used for microarray data analysis may be non-informative for the data generated by transcript enumeration techniques since they ignore certain fundamental properties of this space.
Here we present a software tool, Simcluster, designed to perform clustering analysis for data on the simplex space. We present Simcluster as a stand-alone command-line C package and as a user-friendly on-line tool. Both versions are available at:
Simcluster is designed in accordance with a well-established mathematical framework for compositional data analysis, which provides principled procedures for dealing with the simplex space, and is thus applicable in a number of contexts, including enumeration-based gene expression data.
PMCID: PMC2147035  PMID: 17625017

Results 1-6 (6)