Gene Ontology Concepts Research Articles

Identification of molecular targets and small drug candidates for Huntington's disease via bioinformatics and a network-based screening approach.

Huntington's disease (HD) is a gradually severe neurodegenerative ailment characterised by an increase of a specific trinucleotide repeat sequence (cytosine-adenine-guanine, CAG). It is passed down as a dominant characteristic that worsens over time, creating a significant risk. Despite being monogenetic, the underlying mechanisms as well as biomarkers remain poorly understood. Furthermore, early detection of HD is challenging, and the available diagnostic procedures have low precision and accuracy. The research was conducted to provide knowledge of the biomarkers, pathways and therapeutic targets involved in the molecular processes of HD using informatic based analysis and applying network-based systems biology approaches. The gene expression profile datasets GSE97100 and GSE74201 relevant to HD were studied. As a consequence, 46 differentially expressed genes (DEGs) were identified. 10 hub genes (TPM1, EIF2S3, CCN2, ACTN1, ACTG2, CCN1, CSRP1, EIF1AX, BEX2 and TCEAL5) were further differentiated in the protein-protein interaction (PPI) network. These hub genes were typically down-regulated. Additionally, DEGs-transcription factors (TFs) connections (e.g. GATA2, YY1 and FOXC1), DEG-microRNA (miRNA) interactions (e.g. hsa-miR-124-3p and has-miR-26b-5p) were also comprehensively forecast. Additionally, related gene ontology concepts (e.g. sequence-specific DNA binding and TF activity) connected to DEGs in HD were identified using gene set enrichment analysis (GSEA). Finally, in silico drug design was employed to find candidate drugs for the treatment HD, and while the possible modest therapeutic compounds (e.g. cortistatin A, 13,16-Epoxy-25-hydroxy-17-cheilanthen-19,25-olide, Hecogenin) against HD were expected. Consequently, the results from this study may give researchers useful resources for the experimental validation of Huntington's diagnosis and therapeutic approaches.

Maize heat shock proteins—prospection, validation, categorization and in silico analysis of the different ZmHSP families

Among the plant molecular mechanisms capable of effectively mitigating the effects of adverse weather conditions, the heat shock proteins (HSPs), a group of chaperones with multiple functions, stand out. At a time of full progress on the omic sciences, they look very promising in the genetic engineering field, especially in order to conceive superior genotypes, potentially tolerant to abiotic stresses (AbSts). Recently, some works concerning certain families of maize HSPs (ZmHSPs) were published. However, there was still a lack of a study that, with a high degree of criteria, would fully conglomerate them. Using distinct but complementary strategies, we have prospected as many ZmHSPs candidates as possible, gathering more than a thousand accessions. After detailed data mining, we accounted for 182 validated ones, belonging to seven families, which were subcategorized into classes with potential for functional parity. In them, we identified dozens of motifs with some degree of similarity with proteins from different kingdoms, which may help explain some of their still poorly understood means of action. Through in silico and in vitro approaches, we compared their expression levels after controlled exposure to several AbSts' sources, applied at diverse tissues, on varied phenological stages. Based on gene ontology concepts, we still analyzed them from different perspectives of term enrichment. We have also searched, in model plants and close species, for potentially orthologous genes. With all these new insights, which culminated in a plentiful supplementary material, rich in tables, we aim to constitute a fertile consultation source for those maize researchers attracted by these interesting stress proteins.

Investigating the Lung Adenocarcinoma Stem Cell Biomarker Expressions Using Machine Learning Approaches.

The objective of the study is to look at the activation of stem cell-related markers in lung adenocarcinoma. Utilizing an unsupervised machine learning approach centered on the mRNA expression of pluripotent stem cells as well as its subsequent developed progeny, the mRNA stemness index of further around 500 LUAD patients from The Cancer Genome Atlas dataset was generated. In LUADs, mRNAsi had first been investigated using differential variations, survivability analyses, medical phases, and sexuality. A computational approach is used for identifying cell clusters utilizing fuzzy clustering. There at transcriptional as well as protein stages, the interactions between the genetic markers were investigated. The functionality and processes of the important genes were annotated using expression values. The degree of gene expression related to the clinical symptoms and the likelihood of surviving have also been confirmed. In cancer patients, the mRNAsi genes were highly elevated. In particular, the mRNAsi score rises with advanced trials and varies markedly by sexuality. Within several years, reduced mRNAsi categories will have superior overall survivability in large LUADs. Individuals with chronic LUAD had greater mRNAsi and had reduced average survivability. The important genes and the distinguished categories have been chosen based on their mRNAsi connections. Some of the major genes related to cell proliferating Gene Ontology concepts were found enriched out from the cell cycle Kyoto Encyclopedia of Genes and Genomes (KEGG) process. Specific genes were found to be linked to CSC features. Their activation grew in lockstep with the progression of LUAD's pathology, so these markers appeared amplified in pan-cancers. These important markers were discovered to have substantial connections as a group, suggesting that they could be exploited as drug applications in the therapy of LUAD by suppressing stemness traits.

Gene Ontology synonym generation rules lead to increased performance in biomedical concept recognition.

BackgroundGene Ontology (GO) terms represent the standard for annotation and representation of molecular functions, biological processes and cellular compartments, but a large gap exists between the way concepts are represented in the ontology and how they are expressed in natural language text. The construction of highly specific GO terms is formulaic, consisting of parts and pieces from more simple terms.ResultsWe present two different types of manually generated rules to help capture the variation of how GO terms can appear in natural language text. The first set of rules takes into account the compositional nature of GO and recursively decomposes the terms into their smallest constituent parts. The second set of rules generates derivational variations of these smaller terms and compositionally combines all generated variants to form the original term. By applying both types of rules, new synonyms are generated for two-thirds of all GO terms and an increase in F-measure performance for recognition of GO on the CRAFT corpus from 0.498 to 0.636 is observed. Additionally, we evaluated the combination of both types of rules over one million full text documents from Elsevier; manual validation and error analysis show we are able to recognize GO concepts with reasonable accuracy (88 %) based on random sampling of annotations.ConclusionsIn this work we present a set of simple synonym generation rules that utilize the highly compositional and formulaic nature of the Gene Ontology concepts. We illustrate how the generated synonyms aid in improving recognition of GO concepts on two different biomedical corpora. We discuss other applications of our rules for GO ontology quality assurance, explore the issue of overgeneration, and provide examples of how similar methodologies could be applied to other biomedical terminologies. Additionally, we provide all generated synonyms for use by the text-mining community.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-016-0096-7) contains supplementary material, which is available to authorized users.

Is the crowd better as an assistant or a replacement in ontology engineering? An exploration through the lens of the Gene Ontology

Biomedical ontologies contain errors. Crowdsourcing, defined as taking a job traditionally performed by a designated agent and outsourcing it to an undefined large group of people, provides scalable access to humans. Therefore, the crowd has the potential to overcome the limited accuracy and scalability found in current ontology quality assurance approaches. Crowd-based methods have identified errors in SNOMED CT, a large, clinical ontology, with an accuracy similar to that of experts, suggesting that crowdsourcing is indeed a feasible approach for identifying ontology errors. This work uses that same crowd-based methodology, as well as a panel of experts, to verify a subset of the Gene Ontology (200 relationships). Experts identified 16 errors, generally in relationships referencing acids and metals. The crowd performed poorly in identifying those errors, with an area under the receiver operating characteristic curve ranging from 0.44 to 0.73, depending on the methods configuration. However, when the crowd verified what experts considered to be easy relationships with useful definitions, they performed reasonably well. Notably, there are significantly fewer Google search results for Gene Ontology concepts than SNOMED CT concepts. This disparity may account for the difference in performance – fewer search results indicate a more difficult task for the worker. The number of Internet search results could serve as a method to assess which tasks are appropriate for the crowd. These results suggest that the crowd fits better as an expert assistant, helping experts with their verification by completing the easy tasks and allowing experts to focus on the difficult tasks, rather than an expert replacement.

Discovering gene annotations in biomedical text databases.

BackgroundGenes and gene products are frequently annotated with Gene Ontology concepts based on the evidence provided in genomics articles. Manually locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. Hence, there is clearly a need forautomated computational tools to annotate the genes and gene products with Gene Ontology concepts by computationally capturing the related knowledge embedded in textual data.ResultsIn this article, we present an automated genomic entity annotation system, GEANN, which extracts information about the characteristics of genes and gene products in article abstracts from PubMed, and translates the discoveredknowledge into Gene Ontology (GO) concepts, a widely-used standardized vocabulary of genomic traits. GEANN utilizes textual "extraction patterns", and a semantic matching framework to locate phrases matching to a pattern and produce Gene Ontology annotations for genes and gene products.In our experiments, GEANN has reached to the precision level of 78% at therecall level of 61%. On a select set of Gene Ontology concepts, GEANN either outperforms or is comparable to two other automated annotation studies. Use of WordNet for semantic pattern matching improves the precision and recall by 24% and 15%, respectively, and the improvement due to semantic pattern matching becomes more apparent as the Gene Ontology terms become more general.ConclusionGEANN is useful for two distinct purposes: (i) automating the annotation of genomic entities with Gene Ontology concepts, and (ii) providing existing annotations with additional "evidence articles" from the literature. The use of textual extraction patterns that are constructed based on the existing annotations achieve high precision. The semantic pattern matching framework provides a more flexible pattern matching scheme with respect to "exactmatching" with the advantage of locating approximate pattern occurrences with similar semantics. Relatively low recall performance of our pattern-based approach may be enhanced either by employing a probabilistic annotation framework based on the annotation neighbourhoods in textual data, or, alternatively, the statistical enrichment threshold may be adjusted to lower values for applications that put more value on achieving higher recall values.

Combining Evidence, Specificity, and Proximity towards the Normalization of Gene Ontology Terms in Text

Structured information provided by manual annotation of proteins with Gene Ontology concepts represents a high-quality reliable data source for the research community. However, a limited scope of proteins is annotated due to the amount of human resources required to fully annotate each individual gene product from the literature. We introduce a novel method for automatic identification of GO terms in natural language text. The method takes into consideration several features: (1) the evidence for a GO term given by the words occurring in text, (2) the proximity between the words, and (3) the specificity of the GO terms based on their information content. The method has been evaluated on the BioCreAtIvE corpus and has been compared to current state of the art methods. The precision reached 0.34 at a recall of 0.34 for the identified terms at rank 1. In our analysis, we observe that the identification of GO terms in the "cellular component" subbranch of GO is more accurate than for terms from the other two subbranches. This observation is explained by the average number of words forming the terminology over the different subbranches.

Text processing through Web services: calling Whatizit

Text-mining (TM) solutions are developing into efficient services to researchers in the biomedical research community. Such solutions have to scale with the growing number and size of resources (e.g. available controlled vocabularies), with the amount of literature to be processed (e.g. about 17 million documents in PubMed) and with the demands of the user community (e.g. different methods for fact extraction). These demands motivated the development of a server-based solution for literature analysis. Whatizit is a suite of modules that analyse text for contained information, e.g. any scientific publication or Medline abstracts. Special modules identify terms and then link them to the corresponding entries in bioinformatics databases such as UniProtKb/Swiss-Prot data entries and gene ontology concepts. Other modules identify a set of selected annotation types like the set produced by the EBIMed analysis pipeline for proteins. In the case of Medline abstracts, Whatizit offers access to EBI's in-house installation via PMID or term query. For large quantities of the user's own text, the server can be operated in a streaming mode (http://www.ebi.ac.uk/webservices/whatizit).

Linking molecular imaging terminology to the gene ontology (GO).

The rapidly developing domain of molecular imaging represents the merging of current advances in the fields of molecular biology and imaging research. Despite this merger, an information gap continues to exist between the scientists who discover new gene products and the imaging scientists who can exploit this information. The Gene Ontology (GO) Consortium seeks to provide a set of structured terminologies for the conceptual annotation of gene product function, process and location in databases. However, no such structured set of concept-oriented terminology exists for the molecular imaging domain. Since the purpose of GO is to capture the information about the role of gene products, we propose that the mapping of GO's established ontological concepts to a molecular imaging terminology will provide the necessary bridge to fill the information gap between the two fields. We have extracted terms and definitions from an already published molecular imaging glossary as well as molecular imaging research articles, and developed molecular imaging concepts. We then mapped our molecular imaging concepts to the existing gene ontology concepts as a method to comprehensively represent molecular imaging.