Biology subjects

Zobel, J.

Publications and source records attributed to Zobel, J..

7 recordsLinked to original sources

bioRxiv2021

Automatic Consistency Assurance for Literature-based Gene Ontology Annotation

BackgroundLiterature-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. MethodIn 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. Results and ConclusionTwo models built using our method for distinct annotation consistency identification tasks achieved high precision and were robust to updates in the GO vocabulary. We provide detailed error analysis for demonstrating that the method achieves high precision on more confident predictions. Our approach demonstrates clear value for human-in-the-loop curation scenarios. Data availabilityThe synthetic dataset, and the code for generating it are available at https://github.com/jiyuc/BioConsistency.

bioinformatics↗

bioRxiv2020

GeneMates: an R package for Detecting Horizontal Gene Co-transfer between Bacteria Using Gene-gene Associations Controlled for Population Structure

Antimicrobial resistance (AMR) in bacteria has been a global threat to public health for decades. A well-known driving force for the emergence, evolution and dissemination of genetic AMR determinants in bacterial populations is horizontal gene transfer, which is frequently mediated by mobile genetic elements (MGEs). Some MGEs can capture, maintain, and rearrange multiple AMR genes in a donor bacterium before moving them into recipients, giving rise to a phenomenon called horizontal gene co-transfer (HGcoT). This physical linkage or co-localisation between mobile AMR genes is of particular concern because it facilitates rapid dissemination of multidrug resistance within and across bacterial populations, providing opportunities for co-selection of AMR genes and limiting our therapeutic options. The study of HGcoT can be benefited from large-scale whole-genome sequencing (WGS) data, however, by far most published studies of HGcoT only consider simple co-occurrence measures, which can be confounded by strong bacterial population structure due to clonal reproduction, leading to spurious associations. To address this issue, we present GeneMates, an R package implementing a network approach to identification of HGcoT using WGS data. The package enables users to test for associations between presence-absence of bacterial genes using univariate linear mixed models controlling for population structure based on core-genome variation. Furthermore, when physical distances between genes of interest are measurable in bacterial genomes, users can evaluate distance consistency to further support their inference of putative horizontally co-transferred genes, whose co-occurrence cannot be completely explained by the population structure. We demonstrate how this package can be used to identify co-transferred AMR genes and recover known MGEs from Escherichia coli and Salmonella Typhimurium WGS data. GeneMates is accessible at github.com/wanyuac/GeneMates.

genomics↗

bioRxiv2019

Quality Matters: Biocuration Experts on the Impact of Duplication and Other Data Quality Issues in Biological Databases

The volume of biological database records is growing rapidly, populated by complex records drawn from heterogeneous sources. A specific challenge is duplication, that is, the presence of redundancy (records with high similarity) or inconsistency (dissimilar records that correspond to the same entity). The characteristics (which records are duplicates), impact (why duplicates are significant), and solutions (how to address duplication), are not well understood. Studies on the topic are neither recent nor comprehensive. In addition, other data quality issues, such as inconsistencies and inaccuracies, are also of concern in the context of biological databases. A primary focus of this paper is to present and consolidate the opinions of over 20 experts and practitioners on the topic of duplication in biological sequence databases. The results reveal that survey participants believe that duplicate records are diverse; that the negative impacts of duplicates are severe, while positive impacts depend on correct identification of duplicates; and that duplicate detection methods need to be more precise, scalable, and robust. A secondary focus is to consider other quality issues. We observe that biocuration is the key mechanism used to ensure the quality of this data, and explore the issues through a case study of curation in UniProtKB/Swiss-Prot as well as an interview with an experienced biocurator. While biocuration is a vital solution for handling of data quality issues, a broader community effort is needed to provide adequate support for thorough biocuration in the face of widespread quality concerns.

scientific communication and education↗

bioRxiv2017

Literature Consistency of Bioinformatics Sequence Databases is Effective for Assessing Record Quality

Bioinformatics sequence databases such as Genbank or UniProt contain hundreds of millions of records of genomic data. These records are derived from direct submissions from individual laboratories, as well as from bulk submissions from large-scale sequencing centres; their diversity and scale means that they suffer from a range of data quality issues including errors, discrepancies, redundancies, ambiguities, incompleteness, and inconsistencies with the published literature. In this work, we seek to investigate and analyze the data quality of sequence databases from the perspective of a curator, who must detect anomalous and suspicious records.\n\nSpecifically, we emphasize the detection of inconsistent records with respect to the literature. Focusing on GenBank, we propose a set of 24 quality indicators, which are based on treating a record as a query into the published literature, and then use query quality predictors. We then carry out an analysis that shows that the proposed quality indicators and the quality of the records have a mutual relationship, in which one depends on the other. We propose to represent record-literature consistency as a vector of these quality indicators. By reducing the dimensionality of this representation for visualization purposes using Principal Component Analysis, we show that records which have been reported as inconsistent with the literature fall roughly in the same area, and therefore share similar characteristics. By manually analyzing records not previously known to be erroneous that fall in the same area than records know to be inconsistent, we show that 1 record out of 4 is inconsistent with respect to the literature. This high density of inconsistent record opens the way towards the development of automatic methods for the detection of faulty records. We conclude that literature inconsistency is a meaningful strategy for identifying suspicious records.

bioinformatics↗

bioRxiv2017

Automated Detection of Records in Biological Sequence Databases that are Inconsistent with the Literature

We investigate and analyse the data quality of nucleotide sequence databases with the objective of automatic detection of data anomalies and suspicious records. Specifically, we demonstrate that the published literature associated with each data record can be used to automatically evaluate its quality, by cross-checking the consistency of the key content of the database record with the referenced publications. Focusing on GenBank, we describe a set of quality indicators based on the relevance paradigm of information retrieval (IR). Then, we use these quality indicators to train an anomaly detection algorithm to classify records as \"confident\" or \"suspicious\".\n\nOur experiments on the PubMed Central collection show assessing the coherence between the literature and database records, through our algorithms, is an effective mechanism for assisting curators to perform data cleansing. Although fewer than 0.25% of the records in our data set are known to be faulty, we would expect that there are many more in GenBank that have not yet been identified. By automated comparison with literature they can be identified with a precision of up to 10% and a recall of up to 30%, while strongly outperforming several baselines. While these results leave substantial room for improvement, they reflect both the very imbalanced nature of the data, and the limited explicitly labelled data that is available. Overall, the obtained results show promise for the development of a new kind of approach to detecting low-quality and suspicious sequence records based on literature analysis and consistency. From a practical point of view, this will greatly help curators in identifying inconsistent records in large-scale sequence databases by highlighting records that are likely to be inconsistent with the literature.

bioinformatics↗

bioRxiv2016

Duplicates, redundancies, and inconsistencies in the primary nucleotide databases: a descriptive study

GenBank, the EMBL European Nucleotide Archive, and the DNA DataBank of Japan, known collectively as the International Nucleotide Sequence Database Collaboration or INSDC, are the three most significant nucleotide sequence databases. Their records are derived from laboratory work undertaken by different individuals, by different teams, with a range of technologies and assumptions, and over a period of decades. As a consequence, they contain a great many duplicates, redundancies, and inconsistencies, but neither the prevalence nor the characteristics of various types of duplicates have been rigorously assessed. Existing duplicate detection methods in bioinformatics only address specific duplicate types, with inconsistent assumptions; and the impact of duplicates in bioinformatics databases has not been carefully assessed, making it difficult to judge the value of such methods. Our goal is to assess the scale, kinds, and impact of duplicates in bioinformatics databases, through a retrospective analysis of merged groups in INSDC databases. Our outcomes are threefold: (1) We analyse a benchmark dataset consisting of duplicates manually identified in INSDC - a dataset of 67,888 merged groups with 111,823 duplicate pairs across 21 organisms from INSDC databases - in terms of the prevalence, types, and impacts of duplicates. (2) We categorise duplicates at both sequence and annotation level, with supporting quantitative statistics, showing that different organisms have different prevalence of distinct kinds of duplicate. (3) We show that the presence of duplicates has practical impact via a simple case study on duplicates, in terms of GC content and melting temperature. We demonstrate that duplicates not only introduce redundancy, but can lead to inconsistent results for certain tasks. Our findings lead to a better understanding of the problem of duplication in biological databases.

bioinformatics↗

bioRxiv2016

Benchmarks for Measurement of Duplicate Detection Methods in Nucleotide Databases

Duplication of information in databases is a major data quality challenge. The presence of duplicates, implying either redundancy or inconsistency, can have a range of impacts on the quality of analyses that use the data. To provide a sound basis for research on this issue in databases of nucleotide sequences, we have developed new, large-scale validated collections of duplicates, which can be used to test the effectiveness of duplicate detection methods. Previous collections were either designed primarily to test efficiency, or contained only a limited number of duplicates of limited kinds. To date, duplicate detection methods have been evaluated on separate, inconsistent benchmarks, leading to results that cannot be compared and, due to limitations of the benchmarks, of questionable generality.\n\nIn this study we present three nucleotide sequence database benchmarks, based on information drawn from a range of resources, including information derived from mapping to Swiss-Prot and TrEMBL. Each benchmark has distinct characteristics. We quantify these characteristics and argue for their complementary value in evaluation. The benchmarks collectively contain a vast number of validated biological duplicates; the largest has nearly half a billion duplicate pairs (although this is probably only a tiny fraction of the total that is present). They are also the first benchmarks targeting the primary nucleotide databases. The records include the 21 most heavily studied organisms in molecular biology research. Our quantitative analysis shows that duplicates in the different benchmarks, and in different organisms, have different characteristics. It is thus unreliable to evaluate duplicate detection methods against any single benchmark. For example, the benchmark derived from Swiss-Prot mappings identifies more diverse types of duplicates, showing the importance of expert curation, but is limited to coding sequences. Overall, these benchmarks form a resource that we believe will be of great value for development and evaluation of the duplicate detection methods that are required to help maintain these essential resources.\n\nAvailability: The benchmark data sets are available at https://bitbucket.org/biodbqual/benchmarks.

bioinformatics↗