Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics.

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included

CvManGO, a method for leveraging computational predictions to improve literature-based Gene Ontology annotations

The set of annotations at the Saccharomyces Genome Database (SGD) that classifies the cellular function of S. cerevisiae gene products using Gene Ontology (GO) terms has become an important resource for facilitating experimental analysis. In addition to capturing and summarizing experimental results, the structured nature of GO annotations allows for functional comparison across organisms as well as propagation of functional predictions between related gene products. Due to their relevance to many areas of research, ensuring the accuracy and quality of these annotations is a priority at SGD. GO annotations are assigned either manually, by biocurators extracting experimental evidence from the scientific literature, or through automated methods that leverage computational algorithms to predict functional information. Here, we discuss the relationship between literature-based and computationally predicted GO annotations in SGD and extend a strategy whereby comparison of these two types of annotation identifies genes whose annotations need review. Our method, CvManGO (Computational versus Manual GO annotations), pairs literature-based GO annotations with computational GO predictions and evaluates the relationship of the two terms within GO, looking for instances of discrepancy. We found that this method will identify genes that require annotation updates, taking an important step towards finding ways to prioritize literature review. Additionally, we explored factors that may influence the effectiveness of CvManGO in identifying relevant gene targets to find in particular those genes that are missing literature-supported annotations, but our survey found that there are no immediately identifiable criteria by which one could enrich for these under-annotated genes. Finally, we discuss possible ways to improve this strategy, and the applicability of this method to other projects that use the GO for curation

Modeling forward base camps as complex adaptive sociotechnical systems

Work for this thesis focuses on managing complexity within complex adaptive sociotechnical systems by using model based systems engineering and virtual engineering tools. The hypothesis of the work is that integrated virtual models can be used to increase the understanding of these complex adaptive sociotechnical systems, resulting in a reduction in the perceived complexity. This was tested by the use of a two factor survey given to experts of a system (the customer and members of the model design team) and to a target user-group. This group received a demonstration and had hands on experience with a preliminary model of the same system. Results of the survey show that new system designers using an integrated virtual modeling tool view the system as less complex than experts involved with designing the same system without using a tool. Further data is required to support this conclusion, and a plan for gathering more data is described. The application of this method to an emergency response system is then discussed to show how it can be applied to other complex sociotechnical systems and guidelines for applying this methodology are proposed

Some reflections on the quality of administrative data for Indigenous Australians: The importance of knowing something about the unknown(s)

The Repeat Offenders Database, which has been collated by the New South Wales Bureau of Crime Statistics and Research, offers a unique opportunity to analyse data quality issues for an important source of administrative data for Indigenous people. This paper provides several independent estimates of the population of Indigenous offenders by estimating the number of people with unknown Indigenous status who are likely to be identified as Indigenous in other circumstances. The main finding is that the Indigenous population of offenders are substantially undercounted in administrative data collections. The failure to account for this will understate the ‘gap’ between Indigenous and non-Indigenous outcomes