Gene Ontology Consortium Research Articles

Updates to the Alliance of Genome Resources central infrastructure.

The Alliance of Genome Resources (Alliance) is an extensible coalition of knowledgebases focused on the genetics and genomics of intensively studied model organisms. The Alliance is organized as individual knowledge centers with strong connections to their research communities and a centralized software infrastructure, discussed here. Model organisms currently represented in the Alliance are budding yeast, Caenorhabditis elegans, Drosophila, zebrafish, frog, laboratory mouse, laboratory rat, and the Gene Ontology Consortium. The project is in a rapid development phase to harmonize knowledge, store it, analyze it, and present it to the community through a web portal, direct downloads, and application programming interfaces (APIs). Here, we focus on developments over the last 2 years. Specifically, we added and enhanced tools for browsing the genome (JBrowse), downloading sequences, mining complex data (AllianceMine), visualizing pathways, full-text searching of the literature (Textpresso), and sequence similarity searching (SequenceServer). We enhanced existing interactive data tables and added an interactive table of paralogs to complement our representation of orthology. To support individual model organism communities, we implemented species-specific "landing pages" and will add disease-specific portals soon; in addition, we support a common community forum implemented in Discourse software. We describe our progress toward a central persistent database to support curation, the data modeling that underpins harmonization, and progress toward a state-of-the-art literature curation system with integrated artificial intelligence and machine learning (AI/ML).

Interpreting Gene Ontology Annotations Derived from Sequence Homology Methods.

The Gene Ontology (GO) project describes the functions of the gene products of organisms from all kingdoms of life in a standardized way, enabling powerful analyses of experiments involving genome-wide analysis. The scientific literature is used to convert experimental results into GO annotations that systematically classify gene products' functions. However, to address the fact that only a minor fraction of all genes has been characterized experimentally, multiple predictive methods to assign GO annotations have been developed since the inception of GO. Sequence homologies between novel genes and genes with known functions help to approximate the roles of these non-characterized genes. Here we describe the main sequence homology methods to produce annotations: pairwise comparison (BLAST), protein profile models (InterPro), and phylogenetic-based annotation (PAINT). Some of these methods can be implemented with genome analysis pipelines (BLAST and InterPro2GO), while PAINT is curated by the GO consortium.

Transcriptomic analysis of Lacticaseibacillus paracasei Zhang in transition to the viable but non-culturable state by RNA sequencing.

Some bacteria enter the viable but non-culturable (VBNC) state to survive harsh environmental conditions and external stresses. This alters cell physiology and has implications for the food industry as some bacteria, such as lactobacilli, undergo similar changes during food processing. This study aimed to investigate the transcriptomic changes of a probiotic strain, Lacticaseibacillus paracasei Zhang (L. paracasei Zhang), upon transition to the VBNC state using high throughput RNA sequencing (RNA-seq). Bacteria were inoculated into the de Man, Rogosa, and Sharpe medium and maintained at low temperature and pH to induce cell transition to the VBNC state. Cells were harvested for analysis at five stages of VBNC induction: 0, 3, 30, and 180 days after induction and 210 days when the cells entered the VBNC state. Our results showed that the expression of 2,617, 2,642, 2,577, 2,829, and 2,840 genes was altered at these five different stages. The function of differentially expressed genes (DEGs, compared to healthy cells collected at day 0) and their encoded pathways were analyzed by the Gene Ontology Consortium and the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. A total of 10 DEGs were identified in cells that entered the VBNC state: five continuously upregulated (LCAZH_0621, LCAZH_1986, LCAZH_2038, LCAZH_2040, and LCAZH_2174) and five continuously downregulated (LCAZH_0024, LCAZH_0210, LCAZH_0339, LCAZH_0621, and LCAZH_0754). This study proposes a molecular model of the VBNC mechanism in L. paracasei Zhang, highlighting that changes in cell metabolism improve substrate utilization efficiency, thereby enhancing bacterial survival under adverse conditions. These data may be useful for improving the survival of probiotics in industrial food processing.

Transcriptomic correlates of cell cycle checkpoints with distinct prognosis, molecular characteristics, immunological regulation, and therapeutic response in colorectal adenocarcinoma.

Colorectal adenocarcinoma (COAD), accounting for the most common subtype of colorectal cancer (CRC), is a kind of malignant digestive tumor. Some cell cycle checkpoints (CCCs) have been found to contribute to CRC progression, whereas the functional roles of a lot of CCCs, especially the integrated role of checkpoint mechanism in the cell cycle, remain unclear. The Genomic Data Commons (GDC) The Cancer Genome Atlas (TCGA) COAD cohort was retrieved as the training dataset, and GSE24551 and GSE29623 were downloaded from Gene Expression Omnibus (GEO) as the validation datasets. A total of 209 CCC-related genes were derived from the Gene Ontology Consortium and were subsequently enrolled in the univariate, multivariate, and least absolute shrinkage and selection operator (LASSO) Cox regression analyses, finally defining a CCC signature. Cell proliferation and Transwell assay analyses were utilized to evaluate the functional roles of signature-related CCCs. The underlying CCC signature, molecular characteristics, immune-related features, and therapeutic response were finally estimated. The Genomics of Drug Sensitivity in Cancer (GDSC) database was employed for the evaluation of chemotherapeutic responses. The aberrant gene expression of CCCs greatly contributed to COAD development and progression. Univariate Cox regression analysis identified 27 CCC-related genes significantly affecting the overall survival (OS) of COAD patients; subsequently, LASSO analysis determined a novel CCC signature. Noticeably, CDK5RAP2, MAD1L1, NBN, RGCC, and ZNF207 were first identified to be correlated with the prognosis of COAD, and it was proven that all of them were significantly correlated with the proliferation and invasion of HCT116 and SW480 cells. In TCGA COAD cohort, CCC signature robustly stratified COAD patients into high and low CCC score groups (median OS: 57.24 months vs. unreached, p< 0.0001), simultaneously, with the good AUC values for OS prediction at 1, 2, and 3 years were 0.74, 0.78, and 0.77. Furthermore, the prognostic capacity of the CCC signature was verified in the GSE24551 and GSE29623 datasets, and the CCC signature was independent of clinical features. Moreover, a higher CCC score always indicated worse OS, regardless of clinical features, histological subtypes, or molecular subgroups. Intriguingly, functional enrichment analysis confirmed the CCC score was markedly associated withextracellular, matrix and immune (chemokine)-related signaling, cell cycle-related signaling, and metabolisms. Impressively, a higher CCC score was positively correlated with a majority of chemokines, receptors, immunostimulators, and anticancer immunity, indicating a relatively immune-promoting microenvironment. In addition, GSE173839, GSE25066, GSE41998, and GSE194040 dataset analyses of the underlying CCC signature suggested that durvalumab with olaparib and paclitaxel, taxane-anthracycline chemotherapy, neoadjuvant cyclophosphamide/doxorubicin with ixabepilone or paclitaxel, and immunotherapeutic strategies might be suitable for COAD patients with higher CCC score. Eventually, the GDSC database analysis showed that lower CCC scores were likely to be more sensitive to 5-fluorouracil, bosutinib, gemcitabine, gefitinib, methotrexate, mitomycin C, and temozolomide, while patients with higher CCC score seemed to have a higher level of sensitivity to bortezomib and elesclomol. The novel CCC signature exhibited a good ability for prognosis prediction for COAD patients, and the CCC score was found to be highly correlated with molecular features, immune-related characteristics, and therapeutic responses, which would greatly promote clinical management and precision medicine for COAD.

Semantic representation of neural circuit knowledge in Caenorhabditis elegans

In modern biology, new knowledge is generated quickly, making it challenging for researchers to efficiently acquire and synthesise new information from the large volume of primary publications. To address this problem, computational approaches that generate machine-readable representations of scientific findings in the form of knowledge graphs have been developed. These representations can integrate different types of experimental data from multiple papers and biological knowledge bases in a unifying data model, providing a complementary method to manual review for interacting with published knowledge. The Gene Ontology Consortium (GOC) has created a semantic modelling framework that extends individual functional gene annotations to structured descriptions of causal networks representing biological processes (Gene Ontology–Causal Activity Modelling, or GO–CAM). In this study, we explored whether the GO–CAM framework could represent knowledge of the causal relationships between environmental inputs, neural circuits and behavior in the model nematode C. elegans [C. elegans Neural–Circuit Causal Activity Modelling (CeN–CAM)]. We found that, given extensions to several relevant ontologies, a wide variety of author statements from the literature about the neural circuit basis of egg-laying and carbon dioxide (CO2) avoidance behaviors could be faithfully represented with CeN–CAM. Through this process, we were able to generate generic data models for several categories of experimental results. We also discuss how semantic modelling may be used to functionally annotate the C. elegans connectome. Thus, Gene Ontology-based semantic modelling has the potential to support various machine-readable representations of neurobiological knowledge.Graphical

P03.10.A IDENTIFYING DNA DAMAGE RESPONSE (DDR) RELATED FACTORS ESSENTIAL FOR RADIOTHERAPY RESPONSE IN GLIOBLASTOMA USING A NOVEL CRISPR/CAS9 LIBRARY, DDRKOL

Abstract The standard treatment of glioblastoma, which is the most common and aggressive primary brain tumor, includes combined radiotherapy (IR) and chemotherapy (temozolomide (TMZ)) after surgery. The basic working principle behind radiotherapy is to induce DNA double-strand breaks (DSBs), which is the most lethal form of DNA damage and the primary cause of cell death, and to finally kill cancer cells. Additionally, exposure to IR induces the activation of a multicomponent signal transduction network of DNA damage response (DDR). In this study, we developed a new CRISPR/Cas9-based tool to investigate the role of DDR mechanisms and identify novel genes affecting radiotherapy response in glioblastoma. We generated a focused sgRNA library termed DDRKOL (DNA Damage Response Knock Out Library) targeting the DDR pathway to identify novel modulators of TMZ and radiotherapy response with CRISPR/Cas9 screening in vitro. DDRKOL includes a total of 9000 sgRNAs targeting 888 genes (10 sgRNA/gene) containing all DNA damage-related pathways, curated from Gene Ontology Consortium, 35 sgRNA positive controls targeting cell essential genes, 90 non-targeting control sgRNAs, and 35 internal control genes. The composition and coverage of the library were validated, and DDRKOL was utilized to investigate modulators of TMZ response in U87MG cells. Our future work will involve characterizing different effects of DDRKOL members in radiotherapy response, in different subtypes of gliomas with different genetic BACKGROUND s. Cell response to radiation is important, therefore optimization experiments were performed with different radiation doses in A172, U373, and U373-60Gy cell lines to choose the optimal dose suitable for screening. Accordingly, 2 Gy radiation was chosen for large-scale screens. DDRKOL will serve as an important tool to study not only glioblastoma but also many other diseases that threaten global health. Discovering novel genes that affect the therapy response will be stimulating because it will help understand how to overcome therapy resistance and develop new strategies to design effective therapeutic strategies, which can ultimately translate into clinics.This study is supported by TUBITAK 1001 program (#221S439).

FunTaxIS-lite: a simple and light solution to investigate protein functions in all living organisms.

defining the full domain of protein functions belonging to an organism is a complex challenge that is due to the huge heterogeneity of the taxonomy, where single or small groups of species can bear unique functional characteristics. FunTaxIS-lite provides a solution to this challenge by determining taxon-based constraints on Gene Ontology (GO) terms, which specify the functions that an organism can or cannot perform. The tool employs a set of rules to generate and spread the constraints across both the taxon hierarchy and the GO graph. the taxon-based constraints produced by FunTaxIS-lite extend those provided by the Gene Ontology Consortium by an average of 300%. The implementation of these rules significantly reduces errors in function predictions made by automatic algorithms and can assist in correcting inconsistent protein annotations in databases. FunTaxIS-lite is available on https://www.medcomp.medicina.unipd.it/funtaxis-lite and from https://github.com/MedCompUnipd/FunTaxIS-lite. Supplementary data are available at Bioinformatics online.

Xenbase: key features and resources of the Xenopus model organism knowledgebase.

Xenbase (https://www.xenbase.org/), the Xenopus model organism knowledgebase, is a web-accessible resource that integrates the diverse genomic and biological data from research on the laboratory frogs Xenopus laevis and Xenopus tropicalis. The goal of Xenbase is to accelerate discovery and empower Xenopus research, to enhance the impact of Xenopus research data, and to facilitate the dissemination of these data. Xenbase also enhances the value of Xenopus data through high-quality curation, data integration, providing bioinformatics tools optimized for Xenopus experiments, and linking Xenopus data to human data, and other model organisms. Xenbase also plays an indispensable role in making Xenopus data interoperable and accessible to the broader biomedical community in accordance with FAIR principles. Xenbase provides annotated data updates to organizations such as NCBI, UniProtKB, Ensembl, the Gene Ontology consortium, and most recently, the Alliance of Genomic Resources, a common clearing house for data from humans and model organisms. This article provides a brief overview of key and recently added features of Xenbase. New features include processing of Xenopus high-throughput sequencing data from the NCBI Gene Expression Omnibus; curation of anatomical, physiological, and expression phenotypes with the newly created Xenopus Phenotype Ontology; Xenopus Gene Ontology annotations; new anatomical drawings of the Normal Table of Xenopus development; and integration of the latest Xenopus laevis v10.1 genome annotations. Finally, we highlight areas for future development at Xenbase as we continue to support the Xenopus research community.

Abstract PR002: Transcriptomic influences of racial disparities in Black patients with pancreatic neuroendocrine tumors

Abstract Introduction: There are known disparities in outcomes between Black and White patients with pancreatic neuroendocrine tumors (pNETs). Recently, Black patients have been shown to have higher rates of lymph node metastasis in smaller tumors than White patients, indicating possible differences in tumor biology. Numerous prognostic gene expression differences between racial groups have been reported in other cancers, but no such analysis has been conducted in pNETs. This study aims to evaluate pNET transcriptomes for differential expression that may be influencing racially disparate outcomes. Methods: Quality control of archival resected pNETs and demarcation of cancer cells were performed by a board-certified pathologist. Cells were laser microdissected from formalin-fixed, paraffin-embedded specimens, and RNA isolated. Sequencing was performed on an Illumina NextSeq550 at 30 million reads/sample. Alignments to the GRCh38 transcriptome annotation were performed using Salmon and differentially expressed genes (DEG’s) determined using DESeq2. Significant DEGs were determined by FDR-adjusted p-value (q-value; qv) &amp;lt; 0.05 and log2 fold-change (log2FC) ≥ ±2. Gene set enrichment analysis was performed using clusterProfiler and the Gene Ontology (GO) consortium gene sets. Ingenuity Pathway Analysis (IPA) was then conducted to determine regulator effect networks. Results: RNA sequencing was conducted on 14 and 16 grade and sex-matched primary pNETs from self-identified Black and White patients, respectively. Mean age was 51 for Black and 56 for White patients. 11/16 (69%) of White and 9/14 (64%) of Black patients were female. 8 Black patients and 8 White patients had grade 1 tumors, while 6 Black patients and 8 White patients had Grade 2 tumors. Metastatic disease was present in 4 Black and 5 White patients. Overall, 372 genes were significantly differentially expressed and 179 GO gene sets were differentially enriched between groups. Notably, among the top 10 most significantly enriched biological processes were: angiogenesis/blood vessel and vasculature development (qv=1.34e-07, normalized enrichment score [NES]=1.89), positive regulation of cell migration and locomotion (qv=1.34e-07, NES=1.91), and humoral immune response (qv= 9.8e-07, NES=-2.06). Among the top 5 regulator effect networks identified by IPA were: angiogenesis of lesion/cell movement of monocytes (consistency score [CS]=19.3), activation of blood cells (CS=18.9), and activation of cells (17.9). Conclusion: Numerous gene sets and pathways related to blood vessel development and cellular migration, representing key elements in the development of metastatic disease, are significantly enriched in pNETs from Black patients. Additionally, gene sets and pathways related to the immune response are downregulated in Black patients. These data indicate differences in tumor biology that may influence disparate outcomes reported in Black patients with pNETS. Sequencing of additional samples and incorporation of genetic ancestry are necessary to validate these findings. Citation Format: Brendon Herring, Rachael Guenter, Deepti Dhall, Herbert Chen, Clayton Yates, John Bart Rose. Transcriptomic influences of racial disparities in Black patients with pancreatic neuroendocrine tumors [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr PR002.

Effects of Triiodothyronine on Human Osteoblast-Like Cells: Novel Insights From a Global Transcriptome Analysis.

Background: Thyroid hormones play a significant role in bone development and maintenance, with triiodothyronine (T3) particularly being an important modulator of osteoblast differentiation, proliferation, and maintenance. However, details of the biological processes (BPs) and molecular pathways affected by T3 in osteoblasts remain unclear. Methods: To address this issue, primary cultures of human adipose-derived mesenchymal stem cells were subjected to our previously established osteoinduction protocol, and the resultant osteoblast-like cells were treated with 1 nm or 10 nm T3 for 72 h. RNA sequencing (RNA-Seq) was performed using the Illumina platform, and differentially expressed genes (DEGs) were identified from the raw data using Kallisto and DESeq2. Enrichment analysis of DEGs was performed against the Gene Ontology Consortium database for BP terms using the R package clusterProfiler and protein network analysis by STRING. Results: Approximately 16,300 genes were analyzed by RNA-Seq, with 343 DEGs regulated in the 1 nm T3 group and 467 upregulated in the 10 nm T3 group. Several independent BP terms related to bone metabolism were significantly enriched, with a number of genes shared among them (FGFR2, WNT5A, WNT3, ROR2, VEGFA, FBLN1, S1PR1, PRKCZ, TGFB3, and OSR1 for 1nM T3; and FZD1, SMAD6, NOG, NEO1, and ENG for 10 nm T3). An osteoblast-related search in the literature regarding this set of genes suggests that both T3 doses are unfavorable for osteoblast development, mainly hindering BMP and canonical and non-canonical WNT signaling. Conclusions: Therefore, this study provides new directions toward the elucidation of the mechanisms of T3 action on osteoblast metabolism, with potential future implications for the treatment of endocrine-related bone pathologies.

The Role of Testosterone in Spermatogenesis: Lessons From Proteome Profiling of Human Spermatozoa in Testosterone Deficiency.

Testosterone is essential to maintain qualitative spermatogenesis. Nonetheless, no studies have been yet performed in humans to analyze the testosterone-mediated expression of sperm proteins and their importance in reproduction. Thus, this study aimed to identify sperm protein alterations in male hypogonadism using proteomic profiling. We have performed a comparative proteomic analysis comparing sperm from fertile controls (a pool of 5 normogonadic normozoospermic fertile men) versus sperm from patients with secondary hypogonadism (a pool of 5 oligozoospermic hypogonadic patients due to isolated LH deficiency). Sperm protein composition was analyzed, after peptide labelling with Isobaric Tags, via liquid chromatography followed by tandem mass spectrometry (LC-MS/MS) on an LTQ Velos-Orbitrap mass spectrometer. LC-MS/MS data were analyzed using Proteome Discoverer. Criteria used to accept protein identification included a false discovery rate (FDR) of 1% and at least 1 peptide match per protein. Up to 986 proteins were identified and, of those, 43 proteins were differentially expressed: 32 proteins were under-expressed and 11 were over-expressed in the pool of hypogonadic patients compared to the controls. Bioinformatic analyses were performed using UniProt Knowledgebase, and the Gene Ontology Consortium database based on PANTHER. Notably, 13 of these 43 differentially expressed proteins have been previously reported to be related to sperm function and spermatogenesis. Western blot analyses for A-Kinase Anchoring Protein 3 (AKAP3) and the Prolactin Inducible Protein (PIP) were used to confirm the proteomics data. In summary, a high-resolution mass spectrometry-based proteomic approach was used for the first time to describe alterations of the sperm proteome in secondary male hypogonadism. Some of the differential sperm proteins described in this study, which include Prosaposin, SMOC-1, SERPINA5, SPANXB1, GSG1, ELSPBP1, fibronectin, 5-oxoprolinase, AKAP3, AKAP4, HYDIN, ROPN1B, ß-Microseminoprotein and Protein S100-A8, could represent new targets for the design of infertility treatments due to androgen deficiency.

An evidence-based lexical pattern approach for quality assurance of Gene Ontology relations.

Gene Ontology (GO) is widely used in the biological domain. It is the most comprehensive ontology providing formal representation of gene functions (GO concepts) and relations between them. However, unintentional quality defects (e.g. missing or erroneous relations) in GO may exist due to the large size of GO concepts and complexity of GO structures. Such quality defects would impact the results of GO-based analyses and applications. In this work, we introduce a novel evidence-based lexical pattern approach for quality assurance of GO relations. We leverage two layers of evidence to suggest potentially missing relations in GO as follows. We first utilize related concept pairs (i.e. existing relations) in GO to extract relationship-specific lexical patterns, which serve as the first layer evidence to automatically suggest potentially missing relations between unrelated concept pairs. For each suggested missing relation, we further identify two other existing relations as the second layer of evidence that resemble the difference between the missing relation and the existing relation based on which the missing relation is suggested. Applied to the 15 December 2021 release of GO, this approach suggested a total of 866 potentially missing relations. Local domain experts evaluated the entire set of potentially missing relations, and identified 821 as missing relations and 45 indicate erroneous existing relations. We submitted these findings to the GO consortium for further validation and received encouraging feedback. These indicate that our evidence-based approach can be utilized to uncover missing relations and erroneous existing relations in GO.

Whole-Genome Sequence Resource of Fusarium oxysporum Strain TH15, a Plant Growth Promoting Endophytic Fungus Isolated from Tetrastigma hemsleyanum

Whole-Genome Sequence Resource of <i>Fusarium oxysporum</i> Strain TH15, a Plant Growth Promoting Endophytic Fungus Isolated from <i>Tetrastigma hemsleyanum</i>

Preclinical Studies on Convalescent Human Immune Plasma-Derived Exosome: Omics and Antiviral Properties to SARS-CoV-2.

The scale of the COVID-19 pandemic forced urgent measures for the development of new therapeutics. One of these strategies is the use of convalescent plasma (CP) as a conventional source for passive immunity. Recently, there has been interest in CP-derived exosomes. In this report, we present a structural, biochemical, and biological characterization of our proprietary product, convalescent human immune plasma-derived exosome (ChipEXO), following the guidelines set forth by the Turkish Ministry of Health and the Turkish Red Crescent, the Good Manufacturing Practice, the International Society for Extracellular Vesicles, and the Gene Ontology Consortium. The data support the safety and efficacy of this product against SARS-CoV-2 infections in preclinical models.

Screening of Parkinson's Differential MicroRNA Based on GEO Database and Its Clinical Verification.

Objective This study is set out to explore the potential difference of miR in PD through GEO data and provide diagnostic indicators for clinical practice. Methods In this study, differential miR was screened through the Gene Expression Omnibus (GEO) database, 68 PD patients treated in our hospital from May 2017 to March 2018 were collected as the research group (RG), and 50 normal subjects who underwent physical examination in our hospital during the same period were collected as the control group (CG). Quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect the expression and diagnostic value of miR-374a-5p in serum of patients. The potential target genes of miR-374a-5p were predicted, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Ontology Consortium (GO) were carried out. Results GEO2R analysis revealed that 193 miRs are expressed differentially, of which 78 were highly expressed and 115 were poorly expressed. The miR-374a-5p expression in the serum of the RG was reduced markedly and had a diagnostic value. Targetscan and miRDB online websites were used to predict their target genes, with 415 common target genes. miR-374a-5p may participate in 27 functional pathways and 8 signal pathways. Conclusion miR-335-5p has low expression in PD and is expected to be a potential diagnostic indicator.

Gene Ontology curation of the blood-brain barrier to improve the analysis of Alzheimer's and other neurological diseases.

The role of the blood–brain barrier (BBB) in Alzheimer’s and other neurodegenerative diseases is still the subject of many studies. However, those studies using high-throughput methods have been compromised by the lack of Gene Ontology (GO) annotations describing the role of proteins in the normal function of the BBB. The GO Consortium provides a gold-standard bioinformatics resource used for analysis and interpretation of large biomedical data sets. However, the GO is also used by other research communities and, therefore, must meet a variety of demands on the breadth and depth of information that is provided. To meet the needs of the Alzheimer’s research community we have focused on the GO annotation of the BBB, with over 100 transport or junctional proteins prioritized for annotation. This project has led to a substantial increase in the number of human proteins associated with BBB-relevant GO terms as well as more comprehensive annotation of these proteins in many other processes. Furthermore, data describing the microRNAs that regulate the expression of these priority proteins have also been curated. Thus, this project has increased both the breadth and depth of annotation for these prioritized BBB proteins.Database URLhttps://www.ebi.ac.uk/QuickGO/

Formalization of gene regulation knowledge using ontologies and gene ontology causal activity models

Gene regulation computational research requires handling and integrating large amounts of heterogeneous data. The Gene Ontology has demonstrated that ontologies play a fundamental role in biological data interoperability and integration. Ontologies help to express data and knowledge in a machine processable way, which enables complex querying and advanced exploitation of distributed data. Contributing to improve data interoperability in gene regulation is a major objective of the GREEKC Consortium, which aims to develop a standardized gene regulation knowledge commons. GREEKC proposes the use of ontologies and semantic tools for developing interoperable gene regulation knowledge models, which should support data annotation. In this work, we study how such knowledge models can be generated from cartoons of gene regulation scenarios. The proposed method consists of generating descriptions in natural language of the cartoons; extracting the entities from the texts; finding those entities in existing ontologies to reuse as much content as possible, especially from well known and maintained ontologies such as the Gene Ontology, the Sequence Ontology, the Relations Ontology and ChEBI; and implementation of the knowledge models. The models have been implemented using Protégé, a general ontology editor, and Noctua, the tool developed by the Gene Ontology Consortium for the development of causal activity models to capture more comprehensive annotations of genes and link their activities in a causal framework for Gene Ontology Annotations. We applied the method to two gene regulation scenarios and illustrate how to apply the models generated to support the annotation of data from research articles.

A GO catalogue of human DNA-binding transcription factors

To control gene transcription, DNA-binding transcription factors recognise specific sequence motifs in gene regulatory regions. A complete and reliable GO annotation of all DNA-binding transcription factors is key to investigating the delicate balance of gene regulation in response to environmental and developmental stimuli. The need for such information is demonstrated by the many lists of transcription factors that have been produced over the past decade.The COST Action Gene Regulation Ensemble Effort for the Knowledge Commons (GREEKC) Consortium brought together experts in the field of transcription with the aim of providing high quality and interoperable gene regulatory data. The Gene Ontology (GO) Consortium provides strict definitions for gene product function, including factors that regulate transcription. The collaboration between the GREEKC and GO Consortia has enabled the application of those definitions to produce a new curated catalogue of over 1400 human DNA-binding transcription factors, that can be accessed at https://www.ebi.ac.uk/QuickGO/targetset/dbTF.This catalogue has facilitated an improvement in the GO annotation of human DNA-binding transcription factors and led to the GO annotation of almost sixty thousand DNA-binding transcription factors in over a hundred species. Thus, this work will aid researchers investigating the regulation of transcription in both biomedical and basic science.

Model organism databases are in jeopardy.

Model organism databases are in jeopardy.

Analysing Complex Oral Protein Samples: Complete Workflow and Case Analysis of Salivary Pellicles.

Studies on small quantity, highly complex protein samples, such as salivary pellicle, have been enabled by recent major technological and analytical breakthroughs. Advances in mass spectrometry-based computational proteomics such as Multidimensional Protein Identification Technology have allowed precise identification and quantification of complex protein samples on a proteome-wide scale, which has enabled the determination of corresponding genes and cellular functions at the protein level. The latter was achieved via protein-protein interaction mapping with Gene Ontology annotation. In recent years, the application of these technologies has broken various barriers in small-quantity-complex-protein research such as salivary pellicle. This review provides a concise summary of contemporary proteomic techniques contributing to (1) increased complex protein (up to hundreds) identification using minute sample sizes (µg level), (2) precise protein quantification by advanced stable isotope labelling or label-free approaches and (3) the emerging concepts and techniques regarding computational integration, such as the Gene Ontology Consortium and protein-protein interaction mapping. The latter integrates the structural, genomic, and biological context of proteins and genes to predict protein interactions and functional connections in a given biological context. The same technological breakthroughs and computational integration concepts can also be applied to other low-volume oral protein complexes such as gingival crevicular or peri-implant sulcular fluids.