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1.  MaTSE: the gene expression time-series explorer 
BMC Bioinformatics  2013;14(Suppl 19):S1.
Background
High throughput gene expression time-course experiments provide a perspective on biological functioning recognized as having huge value for the diagnosis, treatment, and prevention of diseases. There are however significant challenges to properly exploiting this data due to its massive scale and complexity. In particular, existing techniques are found to be ill suited to finding patterns of changing activity over a limited interval of an experiments time frame. The Time-Series Explorer (TSE) was developed to overcome this limitation by allowing users to explore their data by controlling an animated scatter-plot view. MaTSE improves and extends TSE by allowing users to visualize data with missing values, cross reference multiple conditions, highlight gene groupings, and collaborate by sharing their findings.
Results
MaTSE was developed using an iterative software development cycle that involved a high level of user feedback and evaluation. The resulting software combines a variety of visualization and interaction techniques which work together to allow biologists to explore their data and reveal temporal patterns of gene activity. These include a scatter-plot that can be animated to view different temporal intervals of the data, a multiple coordinated view framework to support the cross reference of multiple experimental conditions, a novel method for highlighting overlapping groups in the scatter-plot, and a pattern browser component that can be used with scatter-plot box queries to support cooperative visualization. A final evaluation demonstrated the tools effectiveness in allowing users to find unexpected temporal patterns and the benefits of functionality such as the overlay of gene groupings and the ability to store patterns.
Conclusions
We have developed a new exploratory analysis tool, MaTSE, that allows users to find unexpected patterns of temporal activity in gene expression time-series data. Overall, the study acted well to demonstrate the benefits of an iterative software development life cycle and allowed us to investigate some visualization problems that are likely to be common in the field of bioinformatics. The subjects involved in the final evaluation were positive about the potential of MaTSE to help them find unexpected patterns in their data and characterized MaTSE as an exploratory tool valuable for hypothesis generation and the creation of new biological knowledge.
doi:10.1186/1471-2105-14-S19-S1
PMCID: PMC3981573  PMID: 24564291
Information Visualization; Animation; Bioinformatics; Gene Expression; Time-course
2.  VIPER: a visualisation tool for exploring inheritance inconsistencies in genotyped pedigrees 
BMC Bioinformatics  2012;13(Suppl 8):S5.
Background
Pedigree genotype datasets are used for analysing genetic inheritance and to map genetic markers and traits. Such datasets consist of hundreds of related animals genotyped for thousands of genetic markers and invariably contain multiple errors in both the pedigree structure and in the associated individual genotype data. These errors manifest as apparent inheritance inconsistencies in the pedigree, and invalidate analyses of marker inheritance patterns across the dataset. Cleaning raw datasets of bad data points (incorrect pedigree relationships, unreliable marker assays, suspect samples, bad genotype results etc.) requires expert exploration of the patterns of exposed inconsistencies in the context of the inheritance pedigree. In order to assist this process we are developing VIPER (Visual Pedigree Explorer), a software tool that integrates an inheritance-checking algorithm with a novel space-efficient pedigree visualisation, so that reported inheritance inconsistencies are overlaid on an interactive, navigable representation of the pedigree structure.
Methods and results
This paper describes an evaluation of how VIPER displays the different scales and types of dataset that occur experimentally, with a description of how VIPER's display interface and functionality meet the challenges presented by such data. We examine a range of possible error types found in real and simulated pedigree genotype datasets, demonstrating how these errors are exposed and explored using the VIPER interface and we evaluate the utility and usability of the interface to the domain expert.
Evaluation was performed as a two stage process with the assistance of domain experts (geneticists). The initial evaluation drove the iterative implementation of further features in the software prototype, as required by the users, prior to a final functional evaluation of the pedigree display for exploring the various error types, data scales and structures.
Conclusions
The VIPER display was shown to effectively expose the range of errors found in experimental genotyped pedigrees, allowing users to explore the underlying causes of reported inheritance inconsistencies. This interface will provide the basis for a full data cleaning tool that will allow the user to remove isolated bad data points, and reversibly test the effect of removing suspect genotypes and pedigree relationships.
doi:10.1186/1471-2105-13-S8-S5
PMCID: PMC3355332  PMID: 22607476
3.  Highlights of the 1st IEEE Symposium on Biological Data Visualization 
BMC Bioinformatics  2012;13(Suppl 8):S1.
doi:10.1186/1471-2105-13-S8-S1
PMCID: PMC3385424  PMID: 22607328
4.  The minimum information about a genome sequence (MIGS) specification 
Nature biotechnology  2008;26(5):541-547.
With the quantity of genomic data increasing at an exponential rate, it is imperative that these data be captured electronically, in a standard format. Standardization activities must proceed within the auspices of open-access and international working bodies. To tackle the issues surrounding the development of better descriptions of genomic investigations, we have formed the Genomic Standards Consortium (GSC). Here, we introduce the minimum information about a genome sequence (MIGS) specification with the intent of promoting participation in its development and discussing the resources that will be required to develop improved mechanisms of metadata capture and exchange. As part of its wider goals, the GSC also supports improving the ‘transparency’ of the information contained in existing genomic databases.
doi:10.1038/nbt1360
PMCID: PMC2409278  PMID: 18464787

Results 1-4 (4)