Methods of Gene Ontology Term Similarity Analysis in Graph Database Environment

2014 ◽  
pp. 345-354 ◽  
Author(s):  
Łukasz Stypka ◽  
Michał Kozielski
Keyword(s):  
Gene Ontology ◽  
Similarity Analysis ◽  
Graph Database ◽  
Gene Ontology Term ◽  
Ontology Term ◽  
Term Similarity

scholarly journals Transcriptome Analysis Reveals Dynamic Cultivar-Dependent Patterns of Gene Expression in Potato Spindle Tuber Viroid-Infected Pepper

Plants ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 2687
Author(s):  
Nikol Hadjieva ◽  
Elena Apostolova ◽  
Vesselin Baev ◽  
Galina Yahubyan ◽  
Mariyana Gozmanova
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Differentially Expressed Genes ◽  
Transcriptome Analysis ◽  
Quantitative Polymerase Chain Reaction ◽  
Potato Spindle Tuber Viroid ◽  
Fine Tuning ◽  
Differentially Expressed ◽  
Gene Ontology Term ◽  
Ontology Term

Potato spindle tuber viroid (PSTVd) infects various plants. PSTVd pathogenesis is associated with interference with the cellular metabolism and defense signaling pathways via direct interaction with host factors or via the transcriptional or post-transcriptional modulation of gene expression. To better understand host defense mechanisms to PSTVd infection, we analyzed the gene expression in two pepper cultivars, Capsicum annuum Kurtovska kapia (KK) and Djulunska shipka (DS), which exhibit mild symptoms of PSTVd infection. Deep sequencing-based transcriptome analysis revealed differential gene expression upon infection, with some genes displaying contrasting expression patterns in KK and DS plants. More genes were downregulated in DS plants upon infection than in KK plants, which could underlie the more severe symptoms seen in DS plants. Gene ontology enrichment analysis revealed that most of the downregulated differentially expressed genes in both cultivars were enriched in the gene ontology term photosynthesis. The genes upregulated in DS plants fell in the biological process of gene ontology term defense response. We validated the expression of six overlapping differentially expressed genes that are involved in photosynthesis, plant hormone signaling, and defense pathways by quantitative polymerase chain reaction. The observed differences in the responses of the two cultivars to PSTVd infection expand the understanding of the fine-tuning of plant gene expression that is needed to overcome the infection.

scholarly journals PFP-WGAN: Protein function prediction by discovering Gene Ontology term correlations with generative adversarial networks

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0244430
Author(s):  
Seyyede Fatemeh Seyyedsalehi ◽  
Mahdieh Soleymani ◽  
Hamid R. Rabiee ◽  
Mohammad R. K. Mofrad
Keyword(s):  
Gene Ontology ◽  
Computational Methods ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Protein Sequencing ◽  
Generative Adversarial Networks ◽  
Gene Ontology Term ◽  
Generative Adversarial Network ◽  
Ontology Term

Understanding the functionality of proteins has emerged as a critical problem in recent years due to significant roles of these macro-molecules in biological mechanisms. However, in-laboratory techniques for protein function prediction are not as efficient as methods developed and processed for protein sequencing. While more than 70 million protein sequences are available today, only the functionality of around one percent of them are known. These facts have encouraged researchers to develop computational methods to infer protein functionalities from their sequences. Gene Ontology is the most well-known database for protein functions which has a hierarchical structure, where deeper terms are more determinative and specific. However, the lack of experimentally approved annotations for these specific terms limits the performance of computational methods applied on them. In this work, we propose a method to improve protein function prediction using their sequences by deeply extracting relationships between Gene Ontology terms. To this end, we construct a conditional generative adversarial network which helps to effectively discover and incorporate term correlations in the annotation process. In addition to the baseline algorithms, we compare our method with two recently proposed deep techniques that attempt to utilize Gene Ontology term correlations. Our results confirm the superiority of the proposed method compared to the previous works. Moreover, we demonstrate how our model can effectively help to assign more specific terms to sequences.

scholarly journals The Use of Gene Ontology Term and KEGG Pathway Enrichment for Analysis of Drug Half-Life

PLoS ONE ◽  
2016 ◽  
Vol 11 (10) ◽  
pp. e0165496 ◽  
Author(s):  
Yu-Hang Zhang ◽  
Chen Chu ◽  
Shaopeng Wang ◽  
Lei Chen ◽  
Jing Lu ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Half Life ◽  
Kegg Pathway ◽  
Gene Ontology Term ◽  
Ontology Term ◽  
Pathway Enrichment

scholarly journals Mass spectrometry-based profiling of the carbon starved Escherichia coli proteome reveals upregulation of stress-inducible pathways implicated in biofilm formation and antibiotic resistance

2015 ◽  
Author(s):  
Rakhan Aimbetov ◽  
Vasily Ogryzko
Keyword(s):  
Escherichia Coli ◽  
Gene Ontology ◽  
Antibiotic Resistance ◽  
Nutrient Deficiency ◽  
Carbon Starvation ◽  
Label Free ◽  
Ontology Term ◽  
Complex Adaptive ◽  
Proteome Level ◽  
Proteomic Investigation

Starvation is a complex adaptive response to insufficiency of nutrients that has been known to implicate a number of stress networks, and modulate pathogenicity and antibiotic resistance in bacteria. However, naturally occurring abrupt elimination of nutrients and prolonged periods of their complete absence, e.g. when bacteria are placed in natural or artificial water reservoirs, are qualitatively different from in-culture late stationary phase energy source diminution. Despite the obvious importance of proteomic investigation of bacteria exposed to nutrient deficiency, no comprehensive study on the subject has been published. In order to address the said shortage of knowledge, we decided to quantitatively look into the proteome-level alterations elicited by the complete lack of nutrients that constitute a viable source of carbon, i.e. carbon starvation, in the Escherichia coli HT115-derived SLE1 strain cells using the combination of label-free and SILAC-based proteomics. As a result, we obtained protein ratios for 1,757 and 1,241 protein groups for each technique respectively, 2D-annotated the quantifiable proteins present in both datasets, identified over- and underrepresented Gene Ontology terms, and isolated protein groups ≥2-fold up- and downregulated in response to carbon starvation (44 and 36 protein groups respectively). We observed upregulation of proteins implicated in various stress-related networks, most notably those that constitute the Gene Ontology term 'Biological adhesion', as well as various terms related to stress. Additionally, we identified several uncharacterized proteins, and our report is the first to ascribe them to a stress-induced proteome. Our data are available via ProteomeXchange with identifier PXD003255 and DOI:10.6019/PXD003255.

scholarly journals Term Matrix: a novel Gene Ontology annotation quality control system based on ontology term co-annotation patterns

Open Biology ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 200149 ◽  
Author(s):  
Valerie Wood ◽  
Seth Carbon ◽  
Midori A. Harris ◽  
Antonia Lock ◽  
Stacia R. Engel ◽  
...  
Keyword(s):  
Quality Control ◽  
Gene Ontology ◽  
Biological Processes ◽  
Gene Products ◽  
Model Species ◽  
Ontology Term ◽  
Protein Coding ◽  
Ontology Structure ◽  
Exclusive Processes ◽  
Annotation Quality

Biological processes are accomplished by the coordinated action of gene products. Gene products often participate in multiple processes, and can therefore be annotated to multiple Gene Ontology (GO) terms. Nevertheless, processes that are functionally, temporally and/or spatially distant may have few gene products in common, and co-annotation to unrelated processes probably reflects errors in literature curation, ontology structure or automated annotation pipelines. We have developed an annotation quality control workflow that uses rules based on mutually exclusive processes to detect annotation errors, based on and validated by case studies including the three we present here: fission yeast protein-coding gene annotations over time; annotations for cohesin complex subunits in human and model species; and annotations using a selected set of GO biological process terms in human and five model species. For each case study, we reviewed available GO annotations, identified pairs of biological processes which are unlikely to be correctly co-annotated to the same gene products (e.g. amino acid metabolism and cytokinesis), and traced erroneous annotations to their sources. To date we have generated 107 quality control rules, and corrected 289 manual annotations in eukaryotes and over 52 700 automatically propagated annotations across all taxa.

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