Chancellery Research - Research Publications
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ItemAutomatic consistency assurance for literature-based gene ontology annotation(BMC, 2021-11-25)BACKGROUND: Literature-based gene ontology (GO) annotation is a process where expert curators use uniform expressions to describe gene functions reported in research papers, creating computable representations of information about biological systems. Manual assurance of consistency between GO annotations and the associated evidence texts identified by expert curators is reliable but time-consuming, and is infeasible in the context of rapidly growing biological literature. A key challenge is maintaining consistency of existing GO annotations as new studies are published and the GO vocabulary is updated. RESULTS: In this work, we introduce a formalisation of biological database annotation inconsistencies, identifying four distinct types of inconsistency. We propose a novel and efficient method using state-of-the-art text mining models to automatically distinguish between consistent GO annotation and the different types of inconsistent GO annotation. We evaluate this method using a synthetic dataset generated by directed manipulation of instances in an existing corpus, BC4GO. We provide detailed error analysis for demonstrating that the method achieves high precision on more confident predictions. CONCLUSIONS: Two models built using our method for distinct annotation consistency identification tasks achieved high precision and were robust to updates in the GO vocabulary. Our approach demonstrates clear value for human-in-the-loop curation scenarios.
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ItemQuality Matters: Biocuration Experts on the Impact of Duplication and Other Data Quality Issues in Biological Databases.(Elsevier, 2020-04)Biological databases represent an extraordinary collective volume of work. Diligently built up over decades and comprising many millions of contributions from the biomedical research community, biological databases provide worldwide access to a massive number of records (also known as entries) [1]. Starting from individual laboratories, genomes are sequenced, assembled, annotated, and ultimately submitted to primary nucleotide databases such as GenBank [2], European Nucleotide Archive (ENA) [3], and DNA Data Bank of Japan (DDBJ) [4] (collectively known as the International Nucleotide Sequence Database Collaboration, INSDC). Protein records, which are the translations of these nucleotide records, are deposited into central protein databases such as the UniProt KnowledgeBase (UniProtKB) [5] and the Protein Data Bank (PDB) [6]. Sequence records are further accumulated into different databases for more specialized purposes: RFam [7] and PFam [8] for RNA and protein families, respectively; DictyBase [9] and PomBase [10] for model organisms; as well as ArrayExpress [11] and Gene Expression Omnibus (GEO) [12] for gene expression profiles. These databases are selected as examples; the list is not intended to be exhaustive. However, they are representative of biological databases that have been named in the “golden set” of the 24th Nucleic Acids Research database issue (in 2016). The introduction of that issue highlights the databases that “consistently served as authoritative, comprehensive, and convenient data resources widely used by the entire community and offer some lessons on what makes a successful database” [13]. In addition, the associated information about sequences is also propagated into non-sequence databases, such as PubMed (https://www.ncbi.nlm.nih.gov/pubmed/) for scientific literature or Gene Ontology (GO) [14] for function annotations. These databases in turn benefit individual studies, many of which use these publicly available records as the basis for their own research.
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ItemGeneMates: an R package for detecting horizontal gene co-transfer between bacteria using gene-gene associations controlled for population structure(BMC, 2020-09-24)BACKGROUND: Horizontal gene transfer contributes to bacterial evolution through mobilising genes across various taxonomical boundaries. It is frequently mediated by mobile genetic elements (MGEs), which may capture, maintain, and rearrange mobile genes and co-mobilise them between bacteria, causing horizontal gene co-transfer (HGcoT). This physical linkage between mobile genes poses a great threat to public health as it facilitates dissemination and co-selection of clinically important genes amongst bacteria. Although rapid accumulation of bacterial whole-genome sequencing data since the 2000s enables study of HGcoT at the population level, results based on genetic co-occurrence counts and simple association tests are usually confounded by bacterial population structure when sampled bacteria belong to the same species, leading to spurious conclusions. RESULTS: We have developed a network approach to explore WGS data for evidence of intraspecies HGcoT and have implemented it in R package GeneMates ( github.com/wanyuac/GeneMates ). The package takes as input an allelic presence-absence matrix of interested genes and a matrix of core-genome single-nucleotide polymorphisms, performs association tests with linear mixed models controlled for population structure, produces a network of significantly associated alleles, and identifies clusters within the network as plausible co-transferred alleles. GeneMates users may choose to score consistency of allelic physical distances measured in genome assemblies using a novel approach we have developed and overlay scores to the network for further evidence of HGcoT. Validation studies of GeneMates on known acquired antimicrobial resistance genes in Escherichia coli and Salmonella Typhimurium show advantages of our network approach over simple association analysis: (1) distinguishing between allelic co-occurrence driven by HGcoT and that driven by clonal reproduction, (2) evaluating effects of population structure on allelic co-occurrence, and (3) direct links between allele clusters in the network and MGEs when physical distances are incorporated. CONCLUSION: GeneMates offers an effective approach to detection of intraspecies HGcoT using WGS data.