Defining functionFunction is like beauty-its definition lies in the eye of the beholder or, in this case, the researcher.At the broadest level, we define organismal function-the function that the protein plays in the overall organism.This function can be observed by understanding the impact on the organism of deletion or mutation of the protein.Physiological function is the function the protein plays in pathways, such as metabolic or signaling pathways.Another level of function is the cellular level.Approaches to understanding cellular function attempt to identify a protein's interaction partners and its location within the cell.Biochemical or molecular function is another level of function, one that identifies the molecular functional details of a functional site, including reaction mechanism, substrate binding, and molecular details of the binding of regulatory molecules.The International Union of Pure and Applied Chemistry (IUPAC) Enzyme Classification (EC) system was an early approach to identifying molecular function.The Gene Ontology (GO) system of classifying function recognizes ways of defining function, using distinct cellular components, molecular function, and biological process hierarchies [1].The emphasis in the collection of research articles that comprise this Focus Feature is on understanding the different molecular functions that can exist within a protein superfamily and the features that define those differences.

Peroxiredoxins (Prxs or Prdxs) are a large protein superfamily of antioxidant enzymes that rapidly detoxify damaging peroxides and/or affect signal transduction and, thus, have roles in proliferation, differentiation, and apoptosis. Prx superfamily members are widespread across phylogeny and multiple methods have been developed to classify them. Here we present an updated atlas of the Prx superfamily identified using a novel method called MISST (Multi-level Iterative Sequence Searching Technique). MISST is an iterative search process developed to be both agglomerative, to add sequences containing similar functional site features, and divisive, to split groups when functional site features suggest distinct functionally-relevant clusters. Superfamily members need not be identified initially-MISST begins with a minimal representative set of known structures and searches GenBank iteratively. Further, the method's novelty lies in the manner in which isofunctional groups are selected; rather than use a single or shifting threshold to identify clusters, the groups are deemed isofunctional when they pass a self-identification criterion, such that the group identifies itself and nothing else in a search of GenBank. The method was preliminarily validated on the Prxs, as the Prxs presented challenges of both agglomeration and division. For example, previous sequence analysis clustered the Prx functional families Prx1 and Prx6 into one group. Subsequent expert analysis clearly identified Prx6 as a distinct functionally relevant group. The MISST process distinguishes these two closely related, though functionally distinct, families. Through MISST search iterations, over 38,000 Prx sequences were identified, which the method divided into six isofunctional clusters, consistent with previous expert analysis. The results represent the most complete computational functional analysis of proteins comprising the Prx superfamily. The feasibility of this novel method is demonstrated by the Prx superfamily results, laying the foundation for potential functionally relevant clustering of the universe of protein sequences.

BACKGROUND: Development of automatable processes for clustering proteins into functionally relevant groups is a critical hurdle as an increasing number of sequences are deposited into databases. Experimental function determination is exceptionally time-consuming and can't keep pace with the identification of protein sequences. A tool, DASP (Deacon Active Site Profiler), was previously developed to identify protein sequences with active site similarity to a query set. Development of two iterative, automatable methods for clustering proteins into functionally relevant groups exposed algorithmic limitations to DASP. RESULTS: The accuracy and efficiency of DASP was significantly improved through six algorithmic enhancements implemented in two stages: DASP2 and DASP3. Validation demonstrated DASP3 provides greater score separation between true positives and false positives than earlier versions. In addition, DASP3 shows similar performance to previous versions in clustering protein structures into isofunctional groups (validated against manual curation), but DASP3 gathers and clusters protein sequences into isofunctional groups more efficiently than DASP and DASP2. CONCLUSIONS: DASP algorithmic enhancements resulted in improved efficiency and accuracy of identifying proteins that contain active site features similar to those of the query set. These enhancements provide incremental improvement in structure database searches and initial sequence database searches; however, the enhancements show significant improvement in iterative sequence searches, suggesting DASP3 is an appropriate tool for the iterative processes required for clustering proteins into isofunctional groups.

UNLABELLED: Standard and Consensus Clustering Analysis Tool for Microarray Data (SC²ATmd) is a MATLAB-implemented application specifically designed for the exploration of microarray gene expression data via clustering. Implementation of two versions of the clustering validation method figure of merit allows for performance comparisons between different clustering algorithms, and tailors the cluster analysis process to the varying characteristics of each dataset. Along with standard clustering algorithms this application also offers a consensus clustering method that can generate reproducible clusters across replicate experiments or different clustering algorithms. This application was designed specifically for the analysis of gene expression data, but may be used with any numerical data as long as it is in the right format. AVAILABILITY: SC²ATmd may be freely downloaded from http://www.compbiosci.wfu.edu/tools.htm.

Modeling of biological networks is a difficult endeavor, but exploration of this problem is essential for understanding the systems behavior of biological processes. In this contribution, developed for sparse data, we present a new continuous Bayesian graphical learning algorithm to cotemporally model proteins in signaling networks and genes in transcriptional regulatory networks. In this continuous Bayesian algorithm, the correlation matrix is singular because the number of time points is less than the number of biological entities (genes or proteins). A suitable restriction on the degree of the graph's vertices is applied and a Metropolis-Hastings algorithm is guided by a BIC-based posterior probability score. Ten independent and diverse runs of the algorithm are conducted, so that the probability space is properly well-explored. Diagnostics to test the applicability of the algorithm to the specific data sets are developed; this is a major benefit of the methodology. This novel algorithm is applied to two time course experimental data sets: 1) protein modification data identifying a potential signaling network in chondrocytes, and 2) gene expression data identifying the transcriptional regulatory network underlying dendritic cell maturation. This method gives high estimated posterior probabilities to many of the proteins' directed edges that are predicted by the literature; for the gene study, the method gives high posterior probabilities to many of the literature-predicted sibling edges. In simulations, the method gives substantially higher estimated posterior probabilities for true edges and true subnetworks than for their false counterparts.

Multiple approaches for reverse-engineering biological networks from time-series data have been proposed in the computational biology literature. These approaches can be classified by their underlying mathematical algorithms, such as Bayesian or algebraic techniques, as well as by their time paradigm, which includes next-state and co-temporal modeling. The types of biological relationships, such as parent-child or siblings, discovered by these algorithms are quite varied. It is important to understand the strengths and weaknesses of the various algorithms and time paradigms on actual experimental data. We assess how well the co-temporal implementations of three algorithms, continuous Bayesian, discrete Bayesian, and computational algebraic, can 1) identify two types of entity relationships, parent and sibling, between biological entities, 2) deal with experimental sparse time course data, and 3) handle experimental noise seen in replicate data sets. These algorithms are evaluated, using the shuffle index metric, for how well the resulting models match literature models in terms of siblings and parent relationships. Results indicate that all three co-temporal algorithms perform well, at a statistically significant level, at finding sibling relationships, but perform relatively poorly in finding parent relationships.

The reverse engineering of protein modification networks from protein modification time sequence data is an important and challenging problem. We utilize continuous regression-based techniques to generate next-state models. Three statistical diagnostics are applied to test the stationary Markov, the equal time interval influence, and the continuous regression assumptions. The standard Metropolis-Hastings algorithm is modified to use a focused model initialization based on Pearson correlations. Additionally, an infrequent step forcing multiple changes, as opposed to the predominate single change, is incorporated into the standard Metropolis-Hastings algorithm which improves the opportunity to escape local minima of information criterion scores. Both Bayesian and corrected Akaike information criteria are used in conjunction with the Metropolis-Hastings algorithm. Real protein modification data often involves only a small number of proteins which allows a direct computation of some models. Our techniques and diagnostics are applied to two sets of time course protein modification data to produce models which score optimally with respect to information criteria. These techniques and diagnostics lend themselves to applications with similar data sets.

An interdisciplinary bioinformatics course has been taught at Wake Forest for three semesters. Undergraduate and graduate students from multiple academic specialties are brought together in a single classroom. In addition to focusing on traditional bioinformatics topics, this course concentrates on interdisciplinary collaboration in the in-class exercises and the research-based course project. A team of faculty from complementary disciplines teach the course. Productive communication is one key goal of this course.

Many scientists and engineers spend much of their lives writing, debugging, and maintaining software, but only a handful have ever been taught how to do this effectively: after a couple of introductory courses, they are left to rediscover (or reinvent) the rest of programming on their own.The result?Most spend far too much time wrestling with software, instead of doing research, but have no idea how reliable or efficient their programs are.''-Greg Wilson [1] A s Greg Wilson's American Scientist article [2] circulated on the ''bio-IT'' e-mail lists and blogosphere this past winter, many of us sighed, groaned, and smiled in recognition.The field of computational biology crosses the span between engineering and science-a surprisingly (to some) large gulf that typically is uncovered in the process of developing scientific software.Why opine on best practices for scientific software projects now?Computational biologists are taking on increasingly important roles in this Internet-enabled, information-rich, high-throughput era of biology [3].Analytics and algorithms must operate on disparate and relatively large datasets.Curation and peer review is essential to critical analysis of computational conclusions.Software applications are needed to aggregate, integrate, and manage data, tools, results, and discoveries.Computational biologists are involved as advisors to technical teams developing and maintaining long-lived data resources, as product owners for software development, as coding and algorithm experts, and as reviewers of proposals and manuscripts.Whether code is developed for use in a single laboratory or as part of a larger, multiinstitutional project, there are best practices worth knowing and following.We are starting with the premise that scientific software development brings together different cultures.A ''certified technology stack'' might mean a robust n-tiered architecture to some and an expensive waste of resources to others.We want to avoid fanning controversy over interdisciplinary science [4,5] and misunderstandings inherent at the interface between engineering and science [6,7].We hope to provide a common understanding so that we all-specialists and generalists-can work effectively on scientific software projects, increasing project efficiency, software longevity, user community acceptance, and translational impact.We see important similarities between the way scientists and software engineers approach and attack problems which may provide a general framework for successful scientific software development.Scientists are taught the scientific method from the time they perform their first experiments.Similarly, software engineers are taught about the software development life cycle before they write their first ''if'' statement.By understanding similarities between these approaches, we can layer some practical methods from the