Assessing the precision of high-throughput computational and laboratory approaches for the genome-wide identification of protein subcellular localization in bacteria

BACKGROUND: Identification of a bacterial protein's subcellular localization (SCL) is important for genome annotation, function prediction and drug or vaccine target identification. Subcellular fractionation techniques combined with recent proteomics technology permits the identification of large numbers of proteins from distinct bacterial compartments. However, the fractionation of a complex structure like the cell into several subcellular compartments is not a trivial task. Contamination from other compartments may occur, and some proteins may reside in multiple localizations. New computational methods have been reported over the past few years that now permit much more accurate, genome-wide analysis of the SCL of protein sequences deduced from genomes. There is a need to compare such computational methods with laboratory proteomics approaches to identify the most effective current approach for genome-wide localization characterization and annotation. RESULTS: In this study, ten subcellular proteome analyses of bacterial compartments were reviewed. PSORTb version 2.0 was used to computationally predict the localization of proteins reported in these publications, and these computational predictions were then compared to the localizations determined by the proteomics study. By using a combined approach, we were able to identify a number of contaminants and proteins with dual localizations, and were able to more accurately identify membrane subproteomes. Our results allowed us to estimate the precision level of laboratory subproteome studies and we show here that, on average, recent high-precision computational methods such as PSORTb now have a lower error rate than laboratory methods. CONCLUSION: We have performed the first focused comparison of genome-wide proteomic and computational methods for subcellular localization identification, and show that computational methods have now attained a level of precision that is exceeding that of high-throughput laboratory approaches. We note that analysis of all cellular fractions collectively is required to effectively provide localization information from laboratory studies, and we propose an overall approach to genome-wide subcellular localization characterization that capitalizes on the complementary nature of current laboratory and computational methods

Evaluation of genomic island predictors using a comparative genomics approach

<p>Abstract</p> <p>Background</p> <p>Genomic islands (GIs) are clusters of genes in prokaryotic genomes of probable horizontal origin. GIs are disproportionately associated with microbial adaptations of medical or environmental interest. Recently, multiple programs for automated detection of GIs have been developed that utilize sequence composition characteristics, such as G+C ratio and dinucleotide bias. To robustly evaluate the accuracy of such methods, we propose that a dataset of GIs be constructed using criteria that are independent of sequence composition-based analysis approaches.</p> <p>Results</p> <p>We developed a comparative genomics approach (IslandPick) that identifies both very probable islands and non-island regions. The approach involves 1) flexible, automated selection of comparative genomes for each query genome, using a distance function that picks appropriate genomes for identification of GIs, 2) identification of regions unique to the query genome, compared with the chosen genomes (positive dataset) and 3) identification of regions conserved across all genomes (negative dataset). Using our constructed datasets, we investigated the accuracy of several sequence composition-based GI prediction tools.</p> <p>Conclusion</p> <p>Our results indicate that AlienHunter has the highest recall, but the lowest measured precision, while SIGI-HMM is the most precise method. SIGI-HMM and IslandPath/DIMOB have comparable overall highest accuracy. Our comparative genomics approach, IslandPick, was the most accurate, compared with a curated list of GIs, indicating that we have constructed suitable datasets. This represents the first evaluation, using diverse and, independent datasets that were not artificially constructed, of the accuracy of several sequence composition-based GI predictors. The caveats associated with this analysis and proposals for optimal island prediction are discussed.</p

FoodON: A Global Farm-to-Fork Food Ontology.

Several resources and standards for indexing food descriptors currently exist, but their content and interrelations are not semantically and logically coherent. Simultaneously, the need to represent knowledge about food is central to many fields including biomedicine and sustainable development. FoodON is a new ontology built to interoperate with the OBO Library and to represent entities which bear a “food role”. It encompasses materials in natural ecosystems and food webs as well as humancentric categorization and handling of food. The latter will be the initial focus of the ontology, and we aim to develop semantics for food safety, food security, the agricultural and animal husbandry practices linked to food production, culinary, nutritional and chemical ingredients and processes. The scope of FoodON is ambitious and will require input from multiple domains. FoodON will import or map to material in existing ontologies and standards and will create content to cover gaps in the representation of food-related products and processes. As a robust food ontology can only be created by consensus and wide adoption, we are currently forming an international consortium to build partnerships, solicit domain expertise, and gather use cases to guide the ontology’s development. The products of this work are being applied to research and clinical datasets such as those associated with the Canadian Healthy Infant Longitudinal Development (CHILD) study which examines the causal factors of asthma and allergy development in children, and the Integrated Rapid Infectious Disease Analysis (IRIDA) platform for genomic epidemiology and foodborne outbreak investigation

MicrobeDB: a locally maintainable database of microbial genomic sequences.

UnlabelledAnalysis of microbial genomes often requires the general organization and comparison of tens to thousands of genomes both from public repositories and unpublished sources. MicrobeDB provides a foundation for such projects by the automation of downloading published, completed bacterial and archaeal genomes from key sources, parsing annotations of all genomes (both public and private) into a local database, and allowing interaction with the database through an easy to use programming interface. MicrobeDB creates a simple to use, easy to maintain, centralized local resource for various large-scale comparative genomic analyses and a back-end for future microbial application design.AvailabilityMicrobeDB is freely available under the GNU-GPL at: http://github.com/mlangill/microbedb

Addressing privacy concerns in sharing viral sequences and minimum contextual data in a public repository during the COVID-19 pandemic

COVID-19 was declared to be a pandemic in March 2020 by the World Health Organization. Timely sharing of viral genomic sequencing data accompanied by a minimal set of contextual data is essential for informing regional, national, and international public health responses. Such contextual data is also necessary for developing, and improving clinical therapies and vaccines, and enhancing the scientific community’s understanding of the SARS-CoV-2 virus. The Canadian COVID-19 Genomics Network (CanCOGeN) was launched in April 2020 to coordinate and upscale existing genomics-based COVID-19 research and surveillance efforts. CanCOGeN is performing large-scale sequencing of both the genomes of SARS-CoV-2 virus samples (VirusSeq) and affected Canadians (HostSeq). This paper addresses the privacy concerns associated with sharing the viral sequence data with a pre-defined set of contextual data describing the sample source and case attribute of the sequence data in the Canadian context. Currently, the viral genome sequences are shared by provincial public health laboratories and their healthcare and academic partners, with the Canadian National Microbiology Laboratory and with publicly accessible databases. However, data sharing delays and the provision of incomplete contextual data often occur because publicly releasing such data triggers privacy and data governance concerns. The CanCOGeN Ethics and Governance Expert Working Group thus has investigated several privacy issues cited by CanCOGeN data providers/stewards. This paper addresses these privacy concerns and offers insights primarily in the Canadian context, although similar privacy considerations also exist in other jurisdictions. We maintain that sharing viral sequencing data and its limited associated contextual data in the public domain generally does not pose insurmountable privacy challenges. However, privacy risks associated with reidentification should be actively monitored due to advancements in reidentification methods and the evolving pandemic landscape. We also argue that during a global health emergency such as COVID-19, privacy should not be used as a blanket measure to prevent such genomic data sharing due to the significant benefits it provides towards public health responses and ongoing research activities