Protein-Protein Interactions (PPIs) are a crucial mechanism underpinning the function of the cell. So far, a wide range of machine-learning based methods have been proposed for predicting these relationships. Their success is heavily dependent on the construction of the underlying feature vectors, with most using a set of physico-chemical properties derived from the sequence. Few work directly with the sequence itself. In this paper, we explore the utility of sequence embeddings for predicting protein-protein interactions. We construct a protein pair feature vector by concatenating the embeddings of their constituent sequence. These feature vectors are then used as input to a binary classifier to make predictions. To learn sequence embeddings, we use two established Word2Vec based methods - Seq2Vec and BioVec - and we also introduce a novel feature construction method called SuperVecNW. The embeddings generated through SuperVecNW capture some network information in addition to the contextual information present in the sequences. We test the efficacy of our proposed approach on human and yeast PPI datasets and on three well-known networks: CD9, the Ras-Raf-Mek-Erk-Elk-Srf pathway, and a Wnt-related network. We demonstrate that low dimensional sequence embeddings provide better results than most alternative representations based on physico-chemical properties while offering a far simple approach to feature vector construction.

Genomic research emerges from collaborative work within and across different scientific disciplines. A diverse range of visualisation techniques has been employed to aid this research, yet relatively little is known as to how these techniques facilitate collaboration. We conducted a case study of collaborative research within a biomedical institute to learn more about the role visualisation plays in genomic mapping. Interviews were conducted with molecular biologists (N = 5) and bioinformaticians (N = 6). We found that genomic research comprises a variety of distinct disciplines engaged in complex analytic tasks that each resist simplification, and their complexity influences how visualisations were used. Visualisation use was impacted by group-specific interactions and temporal work patterns. Visualisations were also crucial to the scientific workflow, used for both question formation and confirmation of hypotheses, and acted as an anchor for the communication of ideas and discussion. In the latter case, two approaches were taken: providing collaborators with either interactive or static imagery representing a viewpoint. The use of generic software for simplified visualisations, and quick production and curation was also noted. We discuss these findings with reference to group-specific interactions and present recommendations for improving collaborative practices through visual analytics.

We are amidst an ongoing flood of sequence data arising from the application of high-throughput technologies, and a concomitant fundamental revision in our understanding of how genomes evolve individually and within the biosphere. Workflows for phylogenomic inference must accommodate data that are not only much larger than before, but often more error prone and perhaps misassembled, or not assembled in the first place. Moreover, genomes of microbes, viruses and plasmids evolve not only by tree-like descent with modification but also by incorporating stretches of exogenous DNA. Thus, next-generation phylogenomics must address computational scalability while rethinking the nature of orthogroups, the alignment of multiple sequences and the inference and comparison of trees. New phylogenomic workflows have begun to take shape based on so-called alignment-free (AF) approaches. Here, we review the conceptual foundations of AF phylogenetics for the hierarchical (vertical) and reticulate (lateral) components of genome evolution, focusing on methods based on k-mers. We reflect on what seems to be successful, and on where further development is needed.

BACKGROUND: Sequencing highly-variable 16S regions is a common and often effective approach to the study of microbial communities, and next-generation sequencing (NGS) technologies provide abundant quantities of data for analysis. However, the speed of existing analysis pipelines may limit our ability to work with these quantities of data. Furthermore, the limited coverage of existing 16S databases may hamper our ability to characterise these communities, particularly in the context of complex or poorly studied environments. RESULTS: In this article we present the SigClust algorithm, a novel clustering method involving the transformation of sequence reads into binary signatures. When compared to other published methods, SigClust yields superior cluster coherence and separation of metagenomic read data, while operating within substantially reduced timeframes. We demonstrate its utility on published Illumina datasets and on a large collection of labelled wound reads sourced from patients in a wound clinic. The temporal analysis is based on tracking the dominant clusters of wound samples over time. The analysis can identify markers of both healing and non-healing wounds in response to treatment. Prominent clusters are found, corresponding to bacterial species known to be associated with unfavourable healing outcomes, including a number of strains of Staphylococcus aureus. CONCLUSIONS: SigClust identifies clusters rapidly and supports an improved understanding of the wound microbiome without reliance on a reference database. The results indicate a promising use for a SigClust-based pipeline in wound analysis and prediction, and a possible novel method for wound management and treatment.

Chronic wounds present a significant risk to the patient and a substantial drain on health budgets, with the problem likely to worsen markedly with increased incidence of type II diabetes. The wound fluid microbiome is known to influence wound healing outcomes, but is poorly characterised. Next Generation Sequencing approaches yield abundant data from wound samples, but progress in understanding these microbial communities may be hampered by the speed of existing analysis pipelines and limitations on coverage by 16S databases. This paper presents SigClust, a novel clustering method based on binary signatures derived from sequence reads. SigClust yields superior cluster coherence and separation of metagenomic read data in timeframes substantially reduced from those of alternative methods. We demonstrate its utility in the wound context on a preliminary set of labelled patient data. We show how a time course analysis based on tracking the dominant clusters over successive wound samples can identify markers of both successful wound healing and wounds refractory to treatment. Clusters prominent in these analyses are found to correspond to bacterial species known to be implicated as a determinant of wound outcomes, notably a number of strains of Staphylococcus aureus. The clusters obtained rapidly via SigClust support improved understanding of the wound microbiome without direct reliance on a reference database, offering the promise of a SigClust-based pipeline for wound analysis and prediction, and potentially novel methods for wound treatment and management.

Sequence comparison is a fundamental task in computational biology, traditionally dominated by alignment-based methods such as the Smith-Waterman and Needleman-Wunsch algorithms, or by alignment based heuristics such as BLAST, the ubiquitous Basic Local Alignment Search Tool. For more than a decade researchers have examined a range of alignment-free alternatives to these approaches, citing concerns over scalability in the era of Next Generation Sequencing, the emergence of petascale sequence archives, and a lack of robustness of alignment methods in the face of structural sequence rearrangements. While some of these approaches have proven successful for particular tasks, many continue to exhibit a marked decline in sensitivity as closely related sequence sets diverge. Avoiding the alignment step allows the methods to scale to the challenges of modern sequence collections, but only at the cost of noticeably inferior search. In this paper we re-examine the problem of similarity measures for alignment-free sequence comparison, and introduce a new method which we term Similarity Projection. Similarity Projection offers markedly enhanced sensitivity - comparable to alignment based methods - while retaining the scalability characteristic of alignment-free approaches. As before, we rely on collections of k-mers; overlapping substrings of the molecular sequence of length k, collected without reference to position, but similarity relies on variants of the Hausdorff set distance, allowing similarity to be scored more effectively to the reflect those components which match, while lessening the impact of those which do not. Formally, the algorithm generates a large mutual similarity matrix between sequence pairs based on their component fragments; successive reduction steps yield a final score over the sequences. However, only a small fraction of these underlying comparisons need be performed, and by use of an approximate scheme based on vector quantization, we are able to achieve an order of magnitude improvement in execution time over the naive approach. We evaluate the approach on two large protein collections obtained from UniProtKB, showing that Similarity Projection achieves accuracy rivalling, and at times clearly exceeding, that of BLAST, while exhibiting markedly superior execution speed.

Over the last decade, the development of a range of Next Generation Sequencing (NGS) technologies has led to an enormous increase in the size of the data sets available in molecular biology. The scale of these data presents new challenges for researchers, and visualisation is widely regarded as an essential tool for exploration and detailed analysis of candidate relationships. Inevitably, there are cognitive and technical limits on the information which may usefully be displayed on a particular device, and there may be some tension between the analytical utility of a representation and its coverage of the relationships available within the data. Careful attention must be given to the overall design of the visualisation, and to the channels selected, and these tasks are further complicated if the intent is to support interactive exploration by a number of collocated researchers or inclusion within a collaborative workflow. This paper is concerned with the design of a visualisation for regulatory interactions in bacteria, the complex relationships that exist between a set of proteins and the much larger set of genes whose action they control. Modelling these interactions yields equally complex network diagrams, and even classical hairball representations when visualised. In this work we explore the iterative refinement of an alternative visualisation for data of this kind, moving away from the traditional hairball to a 'field' of smaller structures, the intent being to support effective comparison across many dozens of strains and species rather than the exhaustive documentation of a full set of interactions for the one organism. While the study did not directly compare insights obtained using TRNDiff with those obtained using other tools, formal evaluations have allowed us to settle on an effective set of representations and visual channels, and interactive features to support analysis. Our approach has produced a far more effective visualisation of these important data sets, and offers useful lessons for tool developers and insights into the utility of touch devices and larger displays for visual analytics and generation of insight at scale.

Abstract While many measures of viewpoint goodness have been proposed in computer graphics, none have been evaluated for ribbon representations of protein secondary structure. To fill this gap, we conducted a user study on Amazon's Mechanical Turk platform, collecting human viewpoint preferences from 65 participants for 4 representative superfamilies of protein domains. In particular, we evaluated viewpoint entropy, which was previously shown to be a good predictor for human viewpoint preference of other, mostly non‐abstract objects. In a second study, we asked 7 experts in molecular biology to find the best viewpoint of the same protein domains and compared their choices with viewpoint entropy. Our results indicate that viewpoint entropy overall is a significant predictor of human viewpoint preference for ribbon representations of protein secondary structure. However, the accuracy depends on the type and composition of the structure: while most participants agree on good viewpoints for structures with mainly beta sheets, viewpoint preference varies considerably for complex arrangements of alpha helices. Finally, experts tend to choose viewpoints of both low and high viewpoint entropy to emphasize different aspects of the respective structure.

Automated marking of student programming assignments has long been a goal of IT educators. Much of this work has focused on the correctness of small student programs, and only limited attention has been given to systematic assessment of the effectiveness of student testing. In this work, we introduce SAM (the Seeded Auto Marker), a system for automated assessment of student submissions which assesses both program code and unit tests supplied by the students. Our central contribution is the use of programs seeded with specific bugs to analyse the effectiveness of the students' unit tests. Beginning with our intended solution program, and guided by our own set of unit tests, we create a suite of minor variations to the solution, each seeded with a single error. Ideally, a student's unit tests should not only identify the presence of the bug, but should do so via the failure of as small a number of tests as possible, indicating focused test cases with minimal redundancy. We describe our system, the creation of seeded test programs and report our experiences in using the approach in practice. In particular, we find that students often fail to provide appropriate coverage, and that their tests frequently suffer from their poor understanding of the limitations imposed by the abstraction.

MapReduce frameworks such as Hadoop are well suited to handling large sets of data which can be processed separately and independently, with canonical applications in information retrieval and sales record analysis. Rapid advances in sequencing technology have ensured an explosion in the availability of genomic data, with a consequent rise in the importance of large scale comparative genomics, often involving operations and data relationships which deviate from the classical Map Reduce structure. This work examines the application of Hadoop to patterns of this nature, using as our focus a well established workflow for identifying promoters - binding sites for regulatory proteins - across multiple gene regions and organisms, coupled with the unifying step of assembling these results into a consensus sequence. Our approach demonstrates the utility of Hadoop for problems of this nature, showing how the tyranny of the "dominant decomposition" can be at least partially overcome. It also demonstrates how load balance and the granularity of parallelism can be optimized by pre-processing that splits and reorganizes input files, allowing a wide range of related problems to be brought under the same computational umbrella.

Abstract Bioinformatics is dominated by online databases and sophisticated web‐accessible tools. As such, it is ideally placed to benefit from the rapid, purpose specific combination of services achievable via web mashups. The recent introduction of a number of sophisticated frameworks has greatly simplified the mashup creation process, making them accessible to scientists with limited programming expertise. In this paper we investigate the feasibility of mashups as a new approach to bioinformatic experimentation, focusing on an exploratory niche between interactive web usage and robust workflows, and attempting to identify the range of computations for which mashups may be employed. While we treat each of the major frameworks, we illustrate the ideas with a series of examples developed under the Popfly framework ‡ . Copyright © 2010 John Wiley & Sons, Ltd.

This work describes recent extensions to the GPFlow scientific workflow system in development at MQUTeR (www.mquter.qut.edu.au), which facilitate interactive experimentation, automatic lifting of computations from single-case to collection-oriented computation and automatic correlation and synthesis of collections. A GPFlow workflow presents as an acyclic data flow graph, yet provides powerful iteration and collection formation capabilities.

Bioinformatics is dominated by on-line databases and sophisticated web-accessible tools. As such, it is ideally placed to benefit from the rapid, purpose specific combination of services and tools achievable via web mashups. The recent introduction of a number of sophisticated frameworks has greatly simplified the mashup creation process, making them accessible to scientists with limited programming expertise. We investigate the feasibility of mashups as a new approach to bioinformatic experimentation, focusing on an exploratory niche between interactive web usage and robust workflows and attempting to identify the range of computations for which mashups may be employed. While we treat each of the major frameworks, we illustrate the ideas with a series of examples developed under the Popfly framework.

MOTIVATION: Identifying bacterial promoters is an important step towards understanding gene regulation. In this paper, we address the problem of predicting the location of promoters and their transcription start sites (TSSs) in Escherichia coli. The accepted method for this problem is to use position weight matrices (PWMs), which define conserved motifs at the sigma-factor binding site. However this method is known to result in large numbers of false positive predictions. RESULTS: Our approaches to TSS prediction are based upon an ensemble of support vector machines (SVMs) employing a variant of the mismatch string kernel. This classifier is subsequently combined with a PWM and a model based on distribution of distances from TSS to gene start. We investigate the effect of different scoring techniques and quantify performance using area under a detection-error tradeoff curve. When tested on a biologically realistic task, our method provides performance comparable with or superior to the best reported for this task. False positives are significantly reduced, an improvement of great significance to biologists. AVAILABILITY: The trained ensemble-SVM model with instructions on usage can be downloaded from http://eresearch.fit.qut.edu.au/downloads

Identifying promoters is the key to understanding gene expression in bacteria. Promoters lie in tightly constrained positions relative to the transcription start site (TSS). In this paper, we address the problem of predicting transcription start sites in Escherichia coli. Knowing the TSS position, one can then predict the promoter position to within a few base pairs, and vice versa. The accepted method for promoter prediction is to use a pair of position weight matrices (PWMs), which define conserved motifs at the sigma-factor binding site. However this method is known to result in a large number of false positive predictions, thereby limiting its usefulness to the experimental biologist. We adopt an alternative approach based on the Support Vector Machine (SVM) using a modified mismatch spectrum kernel. Our modifications involve tagging the motifs with their location, and selectively pruning the feature set. We quantify the performance of several SVM models and a PWM model using a performance metric of area under the detection-error tradeoff (DET) curve. SVM models are shown to outperform the PWM on a biologically realistic TSS prediction task. We also describe a more broadly applicable peak scoring technique which reduces the number of false positive predictions, greatly enhancing the utility of our results.

It has been argued for many years that software engineering lacks the repeatability and well-defined monitoring characteristic of the traditional engineering disciplines. Over time, numerous authors have addressed this issue by proposing a range of software metrics although it is generally agreed that no one measure is sufficient to capture software quality, and a well chosen suite of metrics must be employed. While substantial progress has been made, adoption of metrics has been limited in the software development community, and metrics have long suffered from a lack of comprehensibility. Further, critics have argued that many metrics have been introduced in isolation, with little regard for their relationship to existing measures, and without appropriate validation against a sufficient body of source code. This work introduces the Java Metrics Reporter, a new tool which addresses a number of these concerns in the domain of object oriented languages. The tool provides integrated tutorial support and benchmarking of user code against professional code bases. Moreover, the architecture allows for adaptation to other languages, and extension to other metrics through a straightforward plug-in approach. The paper provides detailed consideration of the architecture and the metrics selected, together with aspects of the tool which lend to its usability and assist in interpretation of metrics. Finally, the paper outlines plans for the further development of the tool, together with its release to the professional and research communities.

The industry-wide demand for rapid development in concert with greater process maturity has seen many software development firms adopt tightly structured iterative processes. While a number of commercial vendors offer suitable process infrastructure and tool support, the cost of licensing, configuration and staff training may be prohibitive for the small and medium size enterprises (SMEs) which dominate the Asia-Pacific software industry. This work addresses these problems through the introduction of the Real World Software Process (RWSP), a freely available, Web-based iterative scheme designed specifically for small teams and organisations. RWSP provides a detailed process description, high quality document templates - including code review and inspection guidelines - and the integrated tutorial support necessary for successful usage by inexperienced developers and teams. In particular it is intended that the process be readily usable by software houses which at present do not follow a formal process, and that the free RWSP process infrastructure should be a vehicle for improving industry standards.

This work presents a new class of neural network models constrained by biological levels of sparsity and weight-precision, and employing only local weight updates. Concept learning is accomplished through the rapid recruitment of existing network knowledge - complex knowledge being realised as a combination of existing basis concepts. Prior network knowledge is here obtained through the random generation of feedforward networks, with the resulting concept library tailored through distributional bias to suit a particular target class. Learning is exclusively local - through supervised Hebbian and Winnow updates - avoiding the necessity for backpropagation of error and allowing remarkably rapid learning. The approach is demonstrated upon concepts of varying difficulty, culminating in the well-known Monks and LED benchmark problems.