Biological Annotation Research Articles

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.ResultsIn 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.ConclusionsTwo 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.

The protein-protein interaction ontology: for better representing and capturing the biological context of protein interaction

BackgroundWith the rapid increase in the amount of Protein-Protein Interaction (PPI) data, the establishment of an event-centered PPI ontology that contains temporal and spatial vocabularies is urgently needed to clarify PPI biological annotations. In this paper, we propose a precisely designed schema - PPIO (PPI Ontology) for representing the biological context of PPIs.ResultsInspired by the event model and the distinct characteristics of PPI events, PPIO consists of six core aspects of the information required for reporting a PPI event, including the interactor (who), the biological process (when), the subcellular location (where), the interaction type (how), the biological function (what) and the detection method (which). PPIO is implemented through the integration of appropriate terms from the corresponding vocabularies/ontologies, e.g., Gene Ontology, Protein Ontology, PSI-MI/MOD, etc. To assess PPIO, an approach based on PPIO in developed to extract PPI biological annotations from an open standard corpus “BioCreAtIvE-PPI”. The experiment results demonstrate PPIO’s high performance, a precision of 0.69, a recall of 0.72 and an F-score of 0.70.ConclusionsPPIO is a well-constructed essential ontology in the interpretation of PPI biological context. The results of the experiments conducted on the BioCreAtIvE corpus demonstrate that PPIO is able to facilitate PPI annotation extraction from biomedical literature effectively and enrich essential annotation for PPIs.

Multi-omics network-based functional annotation of unknown Arabidopsis genes.

Unraveling gene function is pivotal to understanding the signaling cascades that control plant development and stress responses. As experimental profiling is costly and labor intensive, there is a clear need for high-confidence computational annotation. In contrast to detailed gene-specific functional information, transcriptomics data are widely available for both model and crop species. Here, we describe a novel automated function prediction method, which leverages complementary information from multiple expression datasets by analyzing study-specific gene co-expression networks. First, we benchmarked the prediction performance on recently characterized Arabidopsis thaliana genes, and showed that our method outperforms state-of-the-art expression-based approaches. Next, we predicted biological process annotations for known (n = 15 790) and unknown (n = 11 865) genes in A. thaliana and validated our predictions using experimental protein-DNA and protein-protein interaction data (covering >220 000 interactions in total), obtaining a set of high-confidence functional annotations. Our method assigned at least one validated annotation to 5054 (42.6%) unknown genes, and at least one novel validated function to 3408 (53.0%) genes with computational annotations only. These omics-supported functional annotations shed light on a variety of developmental processes and molecular responses, such as flower and root development, defense responses to fungi and bacteria, and phytohormone signaling, and help fill the information gap on biological process annotations in Arabidopsis. An in-depth analysis of two context-specific networks, modeling seed development and response to water deprivation, shows how previously uncharacterized genes function within the respective networks. Moreover, our automated function prediction approach can be applied in future studies to facilitate gene discovery for crop improvement.

Biological data annotation via a human-augmenting AI-based labeling system

Biology has become a prime area for the deployment of deep learning and artificial intelligence (AI), enabled largely by the massive data sets that the field can generate. Key to most AI tasks is the availability of a sufficiently large, labeled data set with which to train AI models. In the context of microscopy, it is easy to generate image data sets containing millions of cells and structures. However, it is challenging to obtain large-scale high-quality annotations for AI models. Here, we present HALS (Human-Augmenting Labeling System), a human-in-the-loop data labeling AI, which begins uninitialized and learns annotations from a human, in real-time. Using a multi-part AI composed of three deep learning models, HALS learns from just a few examples and immediately decreases the workload of the annotator, while increasing the quality of their annotations. Using a highly repetitive use-case—annotating cell types—and running experiments with seven pathologists—experts at the microscopic analysis of biological specimens—we demonstrate a manual work reduction of 90.60%, and an average data-quality boost of 4.34%, measured across four use-cases and two tissue stain types.

CPLM 4.0: an updated database with rich annotations for protein lysine modifications.

Here, we reported the compendium of protein lysine modifications (CPLM 4.0, http://cplm.biocuckoo.cn/), a data resource for various post-translational modifications (PTMs) specifically occurred at the side-chain amino group of lysine residues in proteins. From the literature and public databases, we collected 450 378 protein lysine modification (PLM) events, and combined them with the existing data of our previously developed protein lysine modification database (PLMD 3.0). In total, CPLM 4.0 contained 592 606 experimentally identified modification events on 463 156 unique lysine residues of 105 673 proteins for up to 29 types of PLMs across 219 species. Furthermore, we carefully annotated the data using the knowledge from 102 additional resources that covered 13 aspects, including variation and mutation, disease-associated information, protein-protein interaction, protein functional annotation, DNA & RNA element, protein structure, chemical-target relation, mRNA expression, protein expression/proteomics, subcellular localization, biological pathway annotation, functional domain annotation, and physicochemical property. Compared to PLMD 3.0 and other existing resources, CPLM 4.0 achieved a >2-fold increase in collection of PLM events, with a data volume of ∼45GB. We anticipate that CPLM 4.0 can serve as a more useful database for further study of PLMs.

Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network

Rare diseases affect millions of people worldwide, and discovering their genetic causes is challenging. More than half of the individuals analyzed by the Undiagnosed Diseases Network (UDN) remain undiagnosed. The central hypothesis of this work is that many of these rare genetic disorders are caused by multiple variants in more than one gene. However, given the large number of variants in each individual genome, experimentally evaluating combinations of variants for potential to cause disease is currently infeasible. To address this challenge, we developed the digenic predictor (DiGePred), a random forest classifier for identifying candidate digenic disease gene pairs by features derived from biological networks, genomics, evolutionary history, and functional annotations. We trained the DiGePred classifier by using DIDA, the largest available database of known digenic-disease-causing gene pairs, and several sets of non-digenic gene pairs, including variant pairs derived from unaffected relatives of UDN individuals. DiGePred achieved high precision and recall in cross-validation and on a held-out test set (PR area under the curve > 77%), and we further demonstrate its utility by using digenic pairs from the recent literature. In contrast to other approaches, DiGePred also appropriately controls the number of false positives when applied in realistic clinical settings. Finally, to enable the rapid screening of variant gene pairs for digenic disease potential, we freely provide the predictions of DiGePred on all human gene pairs. Our work enables the discovery of genetic causes for rare non-monogenic diseases by providing a means to rapidly evaluate variant gene pairs for the potential to cause digenic disease.

Network Pharmacology Analysis of the Identification of Phytochemicals and Therapeutic Mechanisms of Paeoniae Radix Alba for the Treatment of Asthma.

BackgroundPaeoniae Radix Alba (PRA), the root of the plant Paeonia lactiflora Pall., has been suggested to play an important role for the treatment of asthma. A biochemical understanding of the clinical effects of Paeoniae Radix Alba is needed. Here, we explore the phytochemicals and therapeutic mechanisms via a systematic and comprehensive network pharmacology analysis. Methods Through TCMSP, PubChem, GeneCards database, and SwissTargetPrediction online tools, potential targets of active ingredients from PRA for the treatment of asthma were obtained. Cytoscape 3.7.2 was used to determine the target of active ingredients of PRA. Target protein interaction (PPI) network was constructed through the STRING database. The Gene Ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genes (KEGG) pathway enrichment analysis were analyzed through the biological information annotation database (DAVID). Results Our results indicate that PRA contains 21 candidate active ingredients with the potential to treat asthma. The enrichment analysis of GO and KEGG pathways found that the treatment of asthma by PRA may be related to the process of TNF (tumor necrosis factor) release, which can regulate and inhibit multiple signaling pathways such as ceramide signaling. Conclusions Our work provides a phytochemical basis and therapeutic mechanisms of PRA for the treatment of asthma, which provides new insights on further research on PRA.

Multi-scale inference of genetic trait architecture using biologically annotated neural networks.

In this article, we present Biologically Annotated Neural Networks (BANNs), a nonlinear probabilistic framework for association mapping in genome-wide association (GWA) studies. BANNs are feedforward models with partially connected architectures that are based on biological annotations. This setup yields a fully interpretable neural network where the input layer encodes SNP-level effects, and the hidden layer models the aggregated effects among SNP-sets. We treat the weights and connections of the network as random variables with prior distributions that reflect how genetic effects manifest at different genomic scales. The BANNs software uses variational inference to provide posterior summaries which allow researchers to simultaneously perform (i) mapping with SNPs and (ii) enrichment analyses with SNP-sets on complex traits. Through simulations, we show that our method improves upon state-of-the-art association mapping and enrichment approaches across a wide range of genetic architectures. We then further illustrate the benefits of BANNs by analyzing real GWA data assayed in approximately 2,000 heterogenous stock of mice from the Wellcome Trust Centre for Human Genetics and approximately 7,000 individuals from the Framingham Heart Study. Lastly, using a random subset of individuals of European ancestry from the UK Biobank, we show that BANNs is able to replicate known associations in high and low-density lipoprotein cholesterol content.

XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data.

The lack of explainability is one of the most prominent disadvantages of deep learning applications in omics. This ‘black box’ problem can undermine the credibility and limit the practical implementation of biomedical deep learning models. Here we present XOmiVAE, a variational autoencoder (VAE)-based interpretable deep learning model for cancer classification using high-dimensional omics data. XOmiVAE is capable of revealing the contribution of each gene and latent dimension for each classification prediction and the correlation between each gene and each latent dimension. It is also demonstrated that XOmiVAE can explain not only the supervised classification but also the unsupervised clustering results from the deep learning network. To the best of our knowledge, XOmiVAE is one of the first activation level-based interpretable deep learning models explaining novel clusters generated by VAE. The explainable results generated by XOmiVAE were validated by both the performance of downstream tasks and the biomedical knowledge. In our experiments, XOmiVAE explanations of deep learning-based cancer classification and clustering aligned with current domain knowledge including biological annotation and academic literature, which shows great potential for novel biomedical knowledge discovery from deep learning models.

Evolutionary Sparse Learning for Phylogenomics

We introduce a supervised machine learning approach with sparsity constraints for phylogenomics, referred to as evolutionary sparse learning (ESL). ESL builds models with genomic loci—such as genes, proteins, genomic segments, and positions—as parameters. Using the Least Absolute Shrinkage and Selection Operator, ESL selects only the most important genomic loci to explain a given phylogenetic hypothesis or presence/absence of a trait. ESL models do not directly involve conventional parameters such as rates of substitutions between nucleotides, rate variation among positions, and phylogeny branch lengths. Instead, ESL directly employs the concordance of variation across sequences in an alignment with the evolutionary hypothesis of interest. ESL provides a natural way to combine different molecular and nonmolecular data types and incorporate biological and functional annotations of genomic loci in model building. We propose positional, gene, function, and hypothesis sparsity scores, illustrate their use through an example, and suggest several applications of ESL. The ESL framework has the potential to drive the development of a new class of computational methods that will complement traditional approaches in evolutionary genomics, particularly for identifying influential loci and sequences given a phylogeny and building models to test hypotheses. ESL’s fast computational times and small memory footprint will also help democratize big data analytics and improve scientific rigor in phylogenomics.

Integrating Bioinformatic Strategies with Real-World Data to Infer Distinctive Immunocyte Infiltration Landscape and Immunologically Relevant Transcriptome Fingerprints in Ossification of Ligamentum Flavum.

PurposeOssification of the ligamentum flavum (OLF) is a multifactorial disease characterized by an insidious and debilitating process of abnormal bone formation in ligamentum tissues. However, its definite pathogenesis has not been fully elucidated. Potential links between the immune system and various forms of heterotopic ossification have been discussed for many years, whereas no research investigated the immune effects on the initiation and development of OLF. Therefore, we attempt to shed light on this issue.MethodsA series of bioinformatic algorithms were integrated to evaluate the immune score and the immunocyte infiltration patterns between OLF and normal samples, screen OLF-related and immune-related differentially expressed genes (OIDEGs), and analyze their biological functions. Correlation analysis inferred OIDEGs-related differentially expressed lncRNAs (OIDELs) and infiltrating immune cells (OIICs) to construct an immunoregulatory network.ResultsDifferential immune score and immune cell infiltration were determined between two groups, and 10 OIDEGs with diverse biological function annotations were identified and verified. A lncRNA-gene-immunocyte regulatory network further revealed 10 OIDEGs, 41 OIDELs and 7 OIICs that were highly correlated. Among them, CD1E and STAT3 were predicted as hub genes whether at the expression level or interaction level. cDCs emerged as having the most prominent differences and the highest degree of connectivity. FO393414.3, AC096734.1, LINC01137 and DLX6-AS1 with the greatest number of OIDEGs were thought to be more likely to participate in immunoregulation of OLF.ConclusionThis is the first research to preliminarily elucidate OLF-related immunocyte infiltration landscape and immune-associated transcriptome signatures based on bioinformatic strategies and real-world data, which may provide compelling insights into the pathogenesis and therapeutic targets of OLF.

Investigating the Multitarget Pharmacological Mechanism of Ursolic Acid Acting on Colon Cancer: A Network Pharmacology Approach.

Objective To explore the mechanisms of ursolic acid for treating colon cancer based on network pharmacology. Method In this study, the potential targets of ursolic acid against colon cancer were predicted and screened through the TCMSP, SYMMAP, Drug Bank, UNI-PROT, and DISGENET databases. The protein interaction (PPI) network was constructed based on the STRING database, and graphs were drawn with the help of Cytoscape software. GO and KEGG enrichment analyses were performed on the targets by using the DAVID database for biological information annotation. Results Ursolic acid has 113 targets in the treatment of colon cancer. The core targets included interleukin-6 (IL-6), mitogen-activated protein kinase 3 (MAPK3), vascular endothelial growth factor receptor (VEGFA), prostaglandin endoperoxide synthase 2 (PTGS2), caspase-3 (CASP3), mitogen-activated protein kinase 8 (MAPK8), tumor necrosis factor (TNF), cyclin D1 (CCND1), JUN, signal transducer and transcriptional activator 3 (STAT3), and other targets. The first 10 pathways related to colon cancer were screened out. The main signaling pathways included the TNF signaling pathway and the AGE-RAGE signaling pathway in diabetic complications and human colon cancer infections. Conclusion This study revealed that ursolic acid played a multitarget and multichannel antitumor role by inhibiting the proliferation of tumor cells, inducing apoptosis, and enhancing antiangiogenesis.

3DBionotes COVID-19 edition.

SummaryThe web platform 3DBionotes-WS integrates multiple web services and an interactive web viewer to provide a unified environment in which biological annotations can be analyzed in their structural context. Since the COVID-19 outbreak, new structural data from many viral proteins have been provided at a very fast pace. This effort includes many cryogenic electron microscopy (cryo-EM) studies, together with more traditional ones (X-rays, NMR), using several modeling approaches and complemented with structural predictions. At the same time, a plethora of new genomics and interactomics information (including fragment screening and structure-based virtual screening efforts) have been made available from different servers. In this context, we have developed 3DBionotes-COVID-19 as an answer to: (i) the need to explore multiomics data in a unified context with a special focus on structural information and (ii) the drive to incorporate quality measurements, especially in the form of advanced validation metrics for cryo-EM.Availability and implementation https://3dbionotes.cnb.csic.es/ws/covid19.Supplementary information Supplementary data are available at Bioinformatics online.

Accelerating Biological Insight for Understudied Genes.

The rapid expansion of genome sequence data is increasing the discovery of protein-coding genes across all domains of life. Annotating these genes with reliable functional information is necessary to understand evolution, to define the full biochemical space accessed by nature, and to identify target genes for biotechnology improvements. The majority of proteins are annotated based on sequence conservation with no specific biological, biochemical, genetic, or cellular function identified. Recent technical advances throughout the biological sciences enable experimental research on these understudied protein-coding genes in a broader collection of species. However, scientists have incentives and biases to continue focusing on well documented genes within their preferred model organism. This perspective suggests a research model that seeks to break historic silos of research bias by enabling interdisciplinary teams to accelerate biological functional annotation. We propose an initiative to develop coordinated projects of collaborating evolutionary biologists, cell biologists, geneticists, and biochemists that will focus on subsets of target genes in multiple model organisms. Concurrent analysis in multiple organisms takes advantage of evolutionary divergence and selection, which causes individual species to be better suited as experimental models for specific genes. Most importantly, multisystem approaches would encourage transdisciplinary critical thinking and hypothesis testing that is inherently slow in current biological research.

Mol* Viewer: modern web app for 3D visualization and analysis of large biomolecular structures.

Large biomolecular structures are being determined experimentally on a daily basis using established techniques such as crystallography and electron microscopy. In addition, emerging integrative or hybrid methods (I/HM) are producing structural models of huge macromolecular machines and assemblies, sometimes containing 100s of millions of non-hydrogen atoms. The performance requirements for visualization and analysis tools delivering these data are increasing rapidly. Significant progress in developing online, web-native three-dimensional (3D) visualization tools was previously accomplished with the introduction of the LiteMol suite and NGL Viewers. Thereafter, Mol* development was jointly initiated by PDBe and RCSB PDB to combine and build on the strengths of LiteMol (developed by PDBe) and NGL (developed by RCSB PDB). The web-native Mol* Viewer enables 3D visualization and streaming of macromolecular coordinate and experimental data, together with capabilities for displaying structure quality, functional, or biological context annotations. High-performance graphics and data management allows users to simultaneously visualise up to hundreds of (superimposed) protein structures, stream molecular dynamics simulation trajectories, render cell-level models, or display huge I/HM structures. It is the primary 3D structure viewer used by PDBe and RCSB PDB. It can be easily integrated into third-party services. Mol* Viewer is open source and freely available at https://molstar.org/.

Proteomic Analysis of Non-human Primate Peripheral Blood Mononuclear Cells During Burkholderia mallei Infection Reveals a Role of Ezrin in Glanders Pathogenesis.

Burkholderia mallei, the causative agent of glanders, is a gram-negative intracellular bacterium. Depending on different routes of infection, the disease is manifested by pneumonia, septicemia, and chronic infections of the skin. B. mallei poses a serious biological threat due to its ability to infect via aerosol route, resistance to multiple antibiotics and to date there are no US Food and Drug Administration (FDA) approved vaccines available. Induction of innate immunity, inflammatory cytokines and chemokines following B. mallei infection, have been observed in in vitro and small rodent models; however, a global characterization of host responses has never been systematically investigated using a non-human primate (NHP) model. Here, using a liquid chromatography-tandem mass spectrometry (LC-MS/MS) approach, we identified alterations in expression levels of host proteins in peripheral blood mononuclear cells (PBMCs) originating from naïve rhesus macaques (Macaca mulatta), African green monkeys (Chlorocebus sabaeus), and cynomolgus macaques (Macaca fascicularis) exposed to aerosolized B. mallei. Gene ontology (GO) analysis identified several statistically significant overrepresented biological annotations including complement and coagulation cascade, nucleoside metabolic process, vesicle-mediated transport, intracellular signal transduction and cytoskeletal protein binding. By integrating an LC-MS/MS derived proteomics dataset with a previously published B. mallei host-pathogen interaction dataset, a statistically significant predictive protein-protein interaction (PPI) network was constructed. Pharmacological perturbation of one component of the PPI network, specifically ezrin, reduced B. mallei mediated interleukin-1β (IL-1β). On the contrary, the expression of IL-1β receptor antagonist (IL-1Ra) was upregulated upon pretreatment with the ezrin inhibitor. Taken together, inflammasome activation as demonstrated by IL-1β production and the homeostasis of inflammatory response is critical during the pathogenesis of glanders. Furthermore, the topology of the network reflects the underlying molecular mechanism of B. mallei infections in the NHP model.

Muscle transcriptional networks linked to resistance exercise training hypertrophic response heterogeneity.

The skeletal muscle hypertrophic response to resistance exercise training (RT) is highly variable across individuals. The molecular underpinnings of this heterogeneity are unclear. This study investigated transcriptional networks linked to RT-induced muscle hypertrophy, classified as 1) predictive of hypertrophy, 2) responsive to RT independent of muscle hypertrophy, or 3) plastic with hypertrophy. Older adults (n = 31, 18 F/13 M, 70 ± 4 yr) underwent 14-wk RT (3 days/wk, alternating high-low-high intensity). Muscle hypertrophy was assessed by pre- to post-RT change in mid-thigh muscle cross-sectional area (CSA) [computed tomography (CT), primary outcome] and thigh lean mass [dual-energy X-ray absorptiometry (DXA), secondary outcome]. Transcriptome-wide poly-A RNA-seq was performed on vastus lateralis tissue collected pre- (n = 31) and post-RT (n = 22). Prediction networks (using only baseline RNA-seq) were identified by weighted gene correlation network analysis (WGCNA). To identify Plasticity networks, WGCNA change indices for paired samples were calculated and correlated to changes in muscle size outcomes. Pathway-level information extractor (PLIER) was applied to identify Response networks and link genes to biological annotation. Prediction networks (n = 6) confirmed transcripts previously connected to resistance/aerobic training adaptations in the MetaMEx database while revealing novel member genes that should fuel future research to understand the influence of baseline muscle gene expression on hypertrophy. Response networks (n = 6) indicated RT-induced increase in aerobic metabolism and reduced expression of genes associated with spliceosome biology and type-I myofibers. A single exploratory Plasticity network was identified. Findings support that interindividual differences in baseline gene expression may contribute more than RT-induced changes in gene networks to muscle hypertrophic response heterogeneity. Code/Data: https://github.com/kallavin/MASTERS_manuscript/tree/master.

Chromosome-level genome reference and genome editing of the tea geometrid.

The tea geometrid is a destructive insect pest on tea plants, which seriously affects tea production in terms of both yield and quality and causes severe economic losses. The tea geometrid also provides an important study system to address the ecological adaptive mechanisms underlying its unique host plant adaptation and protective resemblance. In this study, we fully sequenced and de novo assembled the reference genome of the tea geometrid, Ectropis grisescens, using long sequencing reads. We presented a highly continuous, near-complete genome reference (787.4Mb; scaffold N50: 26.9Mb), along with the annotation of 18,746 protein-coding genes and 53.3% repeat contents. Importantly, we successfully placed 97.8% of the assembly in 31 chromosomes based on Hi-C interactions and characterized the sex chromosome based on sex-biased sequencing coverage. Multiple quality-control assays and chromosome-scale synteny with the model species all supported the high quality of the presented genome reference. We focused biological annotations on gene families related to the host plant adaptation and camouflage in the tea geometrid and performed comparisons with other representative lepidopteran species. Important findings include the E.grisescens-specific expansion of CYP6 P450 genes that might be involved in metabolism of tea defensive chemicals and unexpected massive expansion of gustatory receptor gene families that suggests potential polyphagy for this tea pest. Furthermore, we developed an efficient genome editing system based on CRISPR/Cas9 technology and successfully implement mutagenesis of a Hox gene in the tea geometrid. Our study provides key genomic resources both for exploring unique mechanisms underlying the ecological adaptation of tea geometrids and for developing environment-friendly strategies for tea pest management.

Establishing a consensus for the hallmarks of cancer based on gene ontology and pathway annotations

BackgroundThe hallmarks of cancer provide a highly cited and well-used conceptual framework for describing the processes involved in cancer cell development and tumourigenesis. However, methods for translating these high-level concepts into data-level associations between hallmarks and genes (for high throughput analysis), vary widely between studies. The examination of different strategies to associate and map cancer hallmarks reveals significant differences, but also consensus.ResultsHere we present the results of a comparative analysis of cancer hallmark mapping strategies, based on Gene Ontology and biological pathway annotation, from different studies. By analysing the semantic similarity between annotations, and the resulting gene set overlap, we identify emerging consensus knowledge. In addition, we analyse the differences between hallmark and gene set associations using Weighted Gene Co-expression Network Analysis and enrichment analysis.ConclusionsReaching a community-wide consensus on how to identify cancer hallmark activity from research data would enable more systematic data integration and comparison between studies. These results highlight the current state of the consensus and offer a starting point for further convergence. In addition, we show how a lack of consensus can lead to large differences in the biological interpretation of downstream analyses and discuss the challenges of annotating changing and accumulating biological data, using intermediate knowledge resources that are also changing over time.

Bioinformatics Identification of Green Tea Anticancer Properties: a Network-Based Approach

Background and objectives: Promising anticancer properties are associated with the consumption of green tea. On the other hand, lung cancer has been showing to possess the highest number of death compared to other types of cancer. The aim of this study was to understand the mechanisms by which green tea shows this effect; bioinformatics study of proteome profile could be essential. For this reason, the proteomics analysis of human lung adenocarcinoma A-549 cells treated with green tea extract was chosen for protein-protein interaction (PPI) network analysis. Methods: Cytoscape v.3.8.2 and its applications analyzed a number of 14 differentially expressed proteins (DEPs) from green tea treatment experiment as two networks. The biological annotations and action type exploration of the hub-bottlenecks of the PPI network were then carried out. Results. The investigation indicated that among 14 queries DEPs, HNRNPA2B1, PCBP1, and HNRNPC may show substantial role. Moreover, HSPA8 was the top hub-bottleneck and half of the central protein groups were enriched with heterogeneous nuclear ribonucleoproteins complex family (HNRNPs). Conclusion. The anticancer bioinformatics study of green tea suggests a complex nature for green tea. This finding urges complementary evaluations to validate whether green tea is applicable as an anticancer agent in medicine.