Text Mining to Support Gene Ontology Curation and Vice Versa

Finding Gene Associations by Text Mining and Annotating it with Gene Ontology

Digitalization of the research articles and their maintenance in a database was the first stage toward the development of biomedical research. With the large amounts of research being published daily, it has created a large gap in accessing all the articles for review to a given problem. To understand any biological process, an insight into the role of each element in the genome is essential. But with this gap in manual curation of literature, there are chances that important biological information may be lost. Hence, text mining plays an important role in bridging this gap and extracting important biological information from the text, finding associations among them and predicting annotations. An annotation may be gene, gene products, gene names, their physical and functional characteristics, and so on. The process of annotations may be classified as structural annotation, functional annotation, and relational annotation. In this chapter, a basic protocol utilizing text mining to extract biological information and predict their functional role based on Gene Ontology is provided.

Automatic consistency assurance for literature-based gene ontology annotation

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.

NLM-Gene, a richly annotated gold standard dataset for gene entities that addresses ambiguity and multi-species gene recognition

The automatic recognition of gene names and their corresponding database identifiers in biomedical text is an important first step for many downstream text-mining applications. While current methods for tagging gene entities have been developed for biomedical literature, their performance on species other than human is substantially lower due to the lack of annotation data. We therefore present the NLM-Gene corpus, a high-quality manually annotated corpus for genes developed at the US National Library of Medicine (NLM), covering ambiguous gene names, with an average of 29 gene mentions (10 unique identifiers) per document, and a broader representation of different species (including Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Arabidopsis thaliana, Danio rerio, etc.) when compared to previous gene annotation corpora. NLM-Gene consists of 550 PubMed abstracts from 156 biomedical journals, doubly annotated by six experienced NLM indexers, randomly paired for each document to control for bias. The annotators worked in three annotation rounds until they reached complete agreement. This gold-standard corpus can serve as a benchmark to develop & test new gene text mining algorithms. Using this new resource, we have developed a new gene finding algorithm based on deep learning which improved both on precision and recall from existing tools. The NLM-Gene annotated corpus is freely available at ftp://ftp.ncbi.nlm.nih.gov/pub/lu/NLMGene. We have also applied this tool to the entire PubMed/PMC with their results freely accessible through our web-based tool PubTator (www.ncbi.nlm.nih.gov/research/pubtator).

Common Genes Involved in Autophagy, Cellular Senescence and the Inflammatory Response in AMD and Drug Discovery Identified via Biomedical Databases

Purpose

Retinal pigment epithelial cell autophagy dysfunction, cellular senescence, and the retinal inflammatory response are key pathogenic factors in age-related macular degeneration (AMD), which has been reviewed in our previously work in 2019. This study aims to identify genes collectively involved in these three biological processes and target drugs in AMD.

Methods

The pubmed2ensembl database was used to perform text mining. The GeneCodis database was applied to analyze gene ontology biological process and the KEGG pathway. The STRING database was used to analyze protein–protein interaction analysis and hub genes were identified by the Cytoscape software. The Drug Gene Interaction Database was used to perform drug–gene interactions.

Results

We identified 62 genes collectively involved in AMD, autophagy, cellular senescence, and inflammatory response, 19 biological processes including 42 genes, 11 enriched KEGG pathways including 37 genes, and 12 hub genes step by step via the above biomedical databases. Finally, five hub genes (IL-6, VEGF-A, TP53, IL-1β, and transforming growth factor [TGF]-β1) and their specific interaction modes were identified, corresponding with 24 target drugs with therapeutic potential for AMD.

Conclusions

IL-6, VEGF-A, TP53, IL-1β, and TGF-β1 are pivotal in autophagy, cellular senescence, and the inflammatory response in AMD, corresponding with 24 drugs with therapeutic potential for AMD, providing definite molecular mechanisms for further research and new possibilities for AMD treatment in the future.

Translational Relevance

IL-6, VEGF-A, TP53, IL-1β, and TGF-β1 may be new targets for AMD gene therapy and drug development.

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.

DES-ROD: Exploring Literature to Develop New Links between RNA Oxidation and Human Diseases

Normal cellular physiology and biochemical processes require undamaged RNA molecules. However, RNAs are frequently subjected to oxidative damage. Overproduction of reactive oxygen species (ROS) leads to RNA oxidation and disturbs redox (oxidation-reduction reaction) homeostasis. When oxidation damage affects RNA carrying protein-coding information, this may result in the synthesis of aberrant proteins as well as a lower efficiency of translation. Both of these, as well as imbalanced redox homeostasis, may lead to numerous human diseases. The number of studies on the effects of RNA oxidative damage in mammals is increasing by year due to the understanding that this oxidation fundamentally leads to numerous human diseases. To enable researchers in this field to explore information relevant to RNA oxidation and effects on human diseases, we developed DES-ROD, an online knowledgebase that contains processed information from 298,603 relevant documents that consist of PubMed abstracts and PubMed Central full-text articles. The system utilizes concepts/terms from 38 curated thematic dictionaries mapped to the analyzed documents. Researchers can explore enriched concepts, as well as enriched pairs of putatively associated concepts. In this way, one can explore mutual relationships between any combinations of two concepts from used dictionaries. Dictionaries cover a wide range of biomedical topics, such as human genes and proteins, pathways, Gene Ontology categories, mutations, noncoding RNAs, enzymes, toxins, metabolites, and diseases. This makes insights into different facets of the effects of RNA oxidation and the control of this process possible. The usefulness of the DES-ROD system is demonstrated by case studies on some known information, as well as potentially novel information involving RNA oxidation and diseases. DES-ROD is the first knowledgebase based on text and data mining that focused on the exploration of RNA oxidation and human diseases.

Applying citizen science to gene, drug and disease relationship extraction from biomedical abstracts

MOTIVATION

Biomedical literature is growing at a rate that outpaces our ability to harness the knowledge contained therein. To mine valuable inferences from the large volume of literature, many researchers use information extraction algorithms to harvest information in biomedical texts. Information extraction is usually accomplished via a combination of manual expert curation and computational methods. Advances in computational methods usually depend on the time-consuming generation of gold standards by a limited number of expert curators. Citizen science is public participation in scientific research. We previously found that citizen scientists are willing and capable of performing named entity recognition of disease mentions in biomedical abstracts, but did not know if this was true with relationship extraction (RE).

RESULTS

In this article, we introduce the Relationship Extraction Module of the web-based application Mark2Cure (M2C) and demonstrate that citizen scientists can perform RE. We confirm the importance of accurate named entity recognition on user performance of RE and identify design issues that impacted data quality. We find that the data generated by citizen scientists can be used to identify relationship types not currently available in the M2C Relationship Extraction Module. We compare the citizen science-generated data with algorithm-mined data and identify ways in which the two approaches may complement one another. We also discuss opportunities for future improvement of this system, as well as the potential synergies between citizen science, manual biocuration and natural language processing.

AVAILABILITY AND IMPLEMENTATION

Mark2Cure platform: https://mark2cure.org; Mark2Cure source code: https://github.com/sulab/mark2cure; and data and analysis code for this article: https://github.com/gtsueng/M2C_rel_nb.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Literature-Based Enrichment Insights into Redox Control of Vascular Biology

In cellular physiology and signaling, reactive oxygen species (ROS) play one of the most critical roles. ROS overproduction leads to cellular oxidative stress. This may lead to an irrecoverable imbalance of redox (oxidation-reduction reaction) function that deregulates redox homeostasis, which itself could lead to several diseases including neurodegenerative disease, cardiovascular disease, and cancers. In this study, we focus on the redox effects related to vascular systems in mammals. To support research in this domain, we developed an online knowledge base, DES-RedoxVasc, which enables exploration of information contained in the biomedical scientific literature. The DES-RedoxVasc system analyzed 233399 documents consisting of PubMed abstracts and PubMed Central full-text articles related to different aspects of redox biology in vascular systems. It allows researchers to explore enriched concepts from 28 curated thematic dictionaries, as well as literature-derived potential associations of pairs of such enriched concepts, where associations themselves are statistically enriched. For example, the system allows exploration of associations of pathways, diseases, mutations, genes/proteins, miRNAs, long ncRNAs, toxins, drugs, biological processes, molecular functions, etc. that allow for insights about different aspects of redox effects and control of processes related to the vascular system. Moreover, we deliver case studies about some existing or possibly novel knowledge regarding redox of vascular biology demonstrating the usefulness of DES-RedoxVasc. DES-RedoxVasc is the first compiled knowledge base using text mining for the exploration of this topic.

Accelerating annotation of articles via automated approaches: evaluation of the neXtA5 curation-support tool by neXtProt

The development of efficient text-mining tools promises to boost the curation workflow by significantly reducing the time needed to process the literature into biological databases. We have developed a curation support tool, neXtA5, that provides a search engine coupled with an annotation system directly integrated into a biocuration workflow. neXtA5 assists curation with modules optimized for the thevarious curation tasks: document triage, entity recognition and information extraction.Here, we describe the evaluation of neXtA5 by expert curators. We first assessed the annotations of two independent curators to provide a baseline for comparison. To evaluate the performance of neXtA5, we submitted requests and compared the neXtA5 results with the manual curation. The analysis focuses on the usability of neXtA5 to support the curation of two types of data: biological processes (BPs) and diseases (Ds). We evaluated the relevance of the papers proposed as well as the recall and precision of the suggested annotations.The evaluation of document triage by neXtA5 precision showed that both curators agree with neXtA5 for 67 (BP) and 63% (D) of abstracts, while curators agree on accepting or rejecting an abstract ~80% of the time. Hence, the precision of the triage system is satisfactory.For concept extraction, curators approved 35 (BP) and 25% (D) of the neXtA5 annotations. Conversely, neXtA5 successfully annotated up to 36 (BP) and 68% (D) of the terms identified by curators. The user feedback obtained in these tests highlighted the need for improvement in the ranking function of neXtA5 annotations. Therefore, we transformed the information extraction component into an annotation ranking system. This improvement results in a top precision (precision at first rank) of 59 (D) and 63% (BP). These results suggest that when considering only the first extracted entity, the current system achieves a precision comparable with expert biocurators.

Evaluating Computational Gene Ontology Annotations

Two avenues to understanding gene function are complementary and often overlapping: experimental work and computational prediction. While experimental annotation generally produces high-quality annotations, it is low throughput. Conversely, computational annotations have broad coverage, but the quality of annotations may be variable, and therefore evaluating the quality of computational annotations is a critical concern.In this chapter, we provide an overview of strategies to evaluate the quality of computational annotations. First, we discuss why evaluating quality in this setting is not trivial. We highlight the various issues that threaten to bias the evaluation of computational annotations, most of which stem from the incompleteness of biological databases. Second, we discuss solutions that address these issues, for example, targeted selection of new experimental annotations and leveraging the existing experimental annotations.

The Vision and Challenges of the Gene Ontology

The overarching goal of the Gene Ontology (GO) Consortium is to provide researchers in biology and biomedicine with all current functional information concerning genes and the cellular context under which these occur. When the GO was started in the 1990s surprisingly little attention had been given to how functional information about genes was to be uniformly captured, structured in a computable form, and made accessible to biologists. Because knowledge of gene, protein, ncRNA, and molecular complex roles is continuously accumulating and changing, the GO needed to be a dynamic resource, accurately tracking ongoing research results over time. Here I describe the progress that has been made over the years towards this goal, and the work that still remains to be done, to make of the Gene Ontology (GO) Consortium realize its goal of offering the most comprehensive and up-to-date resource for information on gene function.

Triage by ranking to support the curation of protein interactions

Database URL

http://candy.hesge.ch/nextA5.