NaviGO: interactive tool for visualization and functional similarity and coherence analysis with gene ontology

Widespread genomic de novo DNA methylation occurs following CD8+ T cell activation and proliferation

This research investigates the intricate dynamics of DNA methylation in the hours following CD8+ T cell activation, during a critical yet understudied temporal window. DNA methylation is an epigenetic modification central to regulation of gene expression and directing immune responses. Our investigation spanned 96-h post-activation and unveils a nuanced tapestry of global and site-specific methylation changes. We identified 15,626 significant differentially methylated CpGs spread across the genome, with the most significant changes occurring within the genes ADAM10, ICA1, and LAPTM5. While many changes had modest effect sizes, approximately 120 CpGs exhibited a log2FC above 1.5, with cell activation and proliferation pathways the most affected. Relatively few of the differentially methylated CpGs occurred along adjacent gene regions. The exceptions were seven differentially methylated gene regions, with the Human T cell Receptor Alpha Joining Genes demonstrating consistent methylation change over a 3kb window. We also investigated whether an inflammatory environment could alter DNA methylation during activation, with proliferating cells exposed to the oxidant glycine chloramine. No substantial differential methylation was observed in this context. The temporal perspective of early activation adds depth to the evolving field of epigenetic immunology, offering insights with implications for therapeutic innovation and expanding our understanding of epigenetic modulation in immune function.

FLASH Proton Radiation Therapy Mitigates Inflammatory and Fibrotic Pathways and Preserves Cardiac Function in a Preclinical Mouse Model of Radiation-Induced Heart Disease

PURPOSE

Studies during the past 9 years suggest that delivering radiation at dose rates exceeding 40 Gy/s, known as "FLASH" radiation therapy, enhances the therapeutic index of radiation therapy (RT) by decreasing normal tissue damage while maintaining tumor response compared with conventional (or standard) RT. This study demonstrates the cardioprotective benefits of FLASH proton RT (F-PRT) compared with standard (conventional) proton RT (S-PRT), as evidenced by reduced acute and chronic cardiac toxicities.

METHODS AND MATERIALS

Mice were imaged using cone beam computed tomography to precisely determine the heart's apex as the beam isocenter. Irradiation was conducted using a shoot-through technique with a 5-mm diameter circular collimator. Bulk RNA-sequencing was performed on nonirradiated samples, as well as apexes treated with F-PRT or S-PRT, at 2 weeks after a single 40 Gy dose. Inflammatory responses were assessed through multiplex cytokine/chemokine microbead assay and immunofluorescence analyses. Levels of perivascular fibrosis were quantified using Masson's Trichrome and Picrosirius red staining. Additionally, cardiac tissue functionality was evaluated by 2-dimensional echocardiograms at 8- and 30-weeks post-PRT.

RESULTS

Radiation damage was specifically localized to the heart's apex. RNA profiling of cardiac tissues treated with PRT revealed that S-PRT uniquely upregulated pathways associated with DNA damage response, induction of tumor necrosis factor superfamily, and inflammatory response, and F-PRT primarily affected cytoplasmic translation, mitochondrion organization, and adenosine triphosphate synthesis. Notably, F-PRT led to a milder inflammatory response, accompanied by significantly attenuated changes in transforming growth factor β1 and α smooth muscle actin levels. Critically, F-PRT decreased collagen deposition and better preserved cardiac functionality compared with S-PRT.

CONCLUSIONS

This study demonstrated that F-PRT reduces the induction of an inflammatory environment with lower expression of inflammatory cytokines and profibrotic factors. Importantly, the results indicate that F-PRT better preserves cardiac functionality, as confirmed by echocardiography analysis, while also mitigating the development of long-term fibrosis.

GO2Sum: generating human-readable functional summary of proteins from GO terms

Understanding the biological functions of proteins is of fundamental importance in modern biology. To represent a function of proteins, Gene Ontology (GO), a controlled vocabulary, is frequently used, because it is easy to handle by computer programs avoiding open-ended text interpretation. Particularly, the majority of current protein function prediction methods rely on GO terms. However, the extensive list of GO terms that describe a protein function can pose challenges for biologists when it comes to interpretation. In response to this issue, we developed GO2Sum (Gene Ontology terms Summarizer), a model that takes a set of GO terms as input and generates a human-readable summary using the T5 large language model. GO2Sum was developed by fine-tuning T5 on GO term assignments and free-text function descriptions for UniProt entries, enabling it to recreate function descriptions by concatenating GO term descriptions. Our results demonstrated that GO2Sum significantly outperforms the original T5 model that was trained on the entire web corpus in generating Function, Subunit Structure, and Pathway paragraphs for UniProt entries.

PRRGO: A Tool for Visualizing and Mapping Globally Expressed Genes in Public Gene Expression Omnibus RNA-Sequencing Studies to PageRank-scored Gene Ontology Terms

We herein report PageRankeR Gene Ontology (PRRGO), a downloadable web application that can integrate differentially expressed gene (DEG) data from the gene expression omnibus (GEO) GEO2R web tool with the gene ontology (GO) database [1]. Unlike existing tools, PRRGO computes the PageRank for the entire GO network and can generate both interactive GO networks on the web interface and comma-separated values (CSV) files containing the DEG statistics categorized by GO term. These hierarchical and tabular GO-DEG data are especially conducive to hypothesis generation and overlap studies with the use of PageRank data, which can provide a metric of GO term centrality. We verified the tool for accuracy and reliability across nine independent heat shock (HS) studies for which the RNA-seq data was publicly available on GEO and found that the tool produced increasing concordance between study DEGs, GO terms, and select HS-specific GO terms.

GO2Sum: Generating Human Readable Functional Summary of Proteins from GO Terms

Understanding the biological functions of proteins is of fundamental importance in modern biology. To represent function of proteins, Gene Ontology (GO), a controlled vocabulary, is frequently used, because it is easy to handle by computer programs avoiding open-ended text interpretation. Particularly, the majority of current protein function prediction methods rely on GO terms. However, the extensive list of GO terms that describe a protein function can pose challenges for biologists when it comes to interpretation. In response to this issue, we developed GO2Sum (Gene Ontology terms Summarizer), a model that takes a set of GO terms as input and generates a human-readable summary using the T5 large language model. GO2Sum was developed by fine-tuning T5 on GO term assignments and free-text function descriptions for UniProt entries, enabling it to recreate function descriptions by concatenating GO term descriptions. Our results demonstrated that GO2Sum significantly outperforms the original T5 model that was trained on the entire web corpus in generating Function, Subunit Structure, and Pathway paragraphs for UniProt entries.

Domain-PFP allows protein function prediction using function-aware domain embedding representations

Domains are functional and structural units of proteins that govern various biological functions performed by the proteins. Therefore, the characterization of domains in a protein can serve as a proper functional representation of proteins. Here, we employ a self-supervised protocol to derive functionally consistent representations for domains by learning domain-Gene Ontology (GO) co-occurrences and associations. The domain embeddings we constructed turned out to be effective in performing actual function prediction tasks. Extensive evaluations showed that protein representations using the domain embeddings are superior to those of large-scale protein language models in GO prediction tasks. Moreover, the new function prediction method built on the domain embeddings, named Domain-PFP, substantially outperformed the state-of-the-art function predictors. Additionally, Domain-PFP demonstrated competitive performance in the CAFA3 evaluation, achieving overall the best performance among the top teams that participated in the assessment.

Domain-PFP: Protein Function Prediction Using Function-Aware Domain Embedding Representations

Domains are functional and structural units of proteins that govern various biological functions performed by the proteins. Therefore, the characterization of domains in a protein can serve as a proper functional representation of proteins. Here, we employ a self-supervised protocol to derive functionally consistent representations for domains by learning domain-Gene Ontology (GO) co-occurrences and associations. The domain embeddings we constructed turned out to be effective in performing actual function prediction tasks. Extensive evaluations showed that protein representations using the domain embeddings are superior to those of large-scale protein language models in GO prediction tasks. Moreover, the new function prediction method built on the domain embeddings, named Domain-PFP, significantly outperformed the state-of-the-art function predictors. Additionally, Domain-PFP demonstrated competitive performance in the CAFA3 evaluation, achieving overall the best performance among the top teams that participated in the assessment.

Disruption of CDK7 signaling leads to catastrophic chromosomal instability coupled with a loss of condensin-mediated chromatin compaction

Chromatin organization is highly dynamic and modulates DNA replication, transcription, and chromosome segregation. Condensin is essential for chromosome assembly during mitosis and meiosis, as well as maintenance of chromosome structure during interphase. While it is well established that sustained condensin expression is necessary to ensure chromosome stability, the mechanisms that control its expression are not yet known. Herein, we report that disruption of cyclin-dependent kinase 7 (CDK7), the core catalytic subunit of CDK-activating kinase, leads to reduced transcription of several condensin subunits, including structural maintenance of chromosomes 2 (SMC2). Live and static microscopy revealed that inhibiting CDK7 signaling prolongs mitosis and induces chromatin bridge formation, DNA double-strand breaks, and abnormal nuclear features, all of which are indicative of mitotic catastrophe and chromosome instability. Affirming the importance of condensin regulation by CDK7, genetic suppression of the expression of SMC2, a core subunit of this complex, phenocopies CDK7 inhibition. Moreover, analysis of genome-wide chromatin conformation using Hi-C revealed that sustained activity of CDK7 is necessary to maintain chromatin sublooping, a function that is ascribed to condensin. Notably, the regulation of condensin subunit gene expression is independent of superenhancers. Together, these studies reveal a new role for CDK7 in sustaining chromatin configuration by ensuring the expression of condensin genes, including SMC2.

Conducting bioinformatics analysis to predict sulforaphane-triggered adverse outcome pathways in healthy human cells

Sulforaphane (SFN) is a naturally occurring molecule present in plants from Brassica family. It becomes bioactive after hydrolytic reaction mediated by myrosinase or human gastrointestinal microbiota. Sulforaphane gained scientific popularity due to its antioxidant and anti-cancer properties. However, its toxicity profile and potential to cause adverse effects remain largely unidentified. Thus, this study aimed to generate SFN-triggered adverse outcome pathway (AOP) by looking at the relationship between SFN-chemical structure and its toxicity, as well as SFN-gene interactions. Quantitative structure-activity relationship (QSAR) analysis identified 2 toxophores (Derek Nexus software) that have the potential to cause chromosomal damage and skin sensitization in mammals or mutagenicity in bacteria. Data extracted from Comparative Toxicogenomics Database (CTD) linked SFN with previously proposed outcomes via gene interactions. The total of 11 and 146 genes connected SFN with chromosomal damage and skin diseases, respectively. However, network analysis (NetworkAnalyst tool) revealed that these genes function in wider networks containing 490 and 1986 nodes, respectively. The over-representation analysis (ExpressAnalyst tool) pointed out crucial biological pathways regulated by SFN-interfering genes. These pathways are uploaded to AOP-helpFinder tool which found the 2321 connections between 19 enriched pathways and SFN which were further considered as key events. Two major, interconnected AOPs were generated: first starting from disruption of biological pathways involved in cell cycle and cell proliferation leading to increased apoptosis, and the second one connecting activated immune system signaling pathways to inflammation and apoptosis. In both cases, chromosomal damage and/or skin diseases such as dermatitis or psoriasis appear as adverse outcomes.

Pan-Cancer Analysis Reveals Functional Similarity of Three lncRNAs across Multiple Tumors

Long non-coding RNAs (lncRNAs) are emerging as key regulators in many biological processes. The dysregulation of lncRNA expression has been associated with many diseases, including cancer. Mounting evidence suggests lncRNAs to be involved in cancer initiation, progression, and metastasis. Thus, understanding the functional implications of lncRNAs in tumorigenesis can aid in developing novel biomarkers and therapeutic targets. Rich cancer datasets, documenting genomic and transcriptomic alterations together with advancement in bioinformatics tools, have presented an opportunity to perform pan-cancer analyses across different cancer types. This study is aimed at conducting a pan-cancer analysis of lncRNAs by performing differential expression and functional analyses between tumor and non-neoplastic adjacent samples across eight cancer types. Among dysregulated lncRNAs, seven were shared across all cancer types. We focused on three lncRNAs, found to be consistently dysregulated among tumors. It has been observed that these three lncRNAs of interest are interacting with a wide range of genes across different tissues, yet enriching substantially similar biological processes, found to be implicated in cancer progression and proliferation.

PRO-Simat: Protein network simulation and design tool

PRO-Simat is a simulation tool for analysing protein interaction networks, their dynamic change and pathway engineering. It provides GO enrichment, KEGG pathway analyses, and network visualisation from an integrated database of more than 8 million protein-protein interactions across 32 model organisms and the human proteome. We integrated dynamical network simulation using the Jimena framework, which quickly and efficiently simulates Boolean genetic regulatory networks. It enables simulation outputs with in-depth analysis of the type, strength, duration and pathway of the protein interactions on the website. Furthermore, the user can efficiently edit and analyse the effect of network modifications and engineering experiments. In case studies, applications of PRO-Simat are demonstrated: (i) understanding mutually exclusive differentiation pathways in Bacillus subtilis, (ii) making Vaccinia virus oncolytic by switching on its viral replication mainly in cancer cells and triggering cancer cell apoptosis and (iii) optogenetic control of nucleotide processing protein networks to operate DNA storage. Multilevel communication between components is critical for efficient network switching, as demonstrated by a general census on prokaryotic and eukaryotic networks and comparing design with synthetic networks using PRO-Simat. The tool is available at https://prosimat.heinzelab.de/ as a web-based query server.

metaGOflow: a workflow for the analysis of marine Genomic Observatories shotgun metagenomics data

BACKGROUND

Genomic Observatories (GOs) are sites of long-term scientific study that undertake regular assessments of the genomic biodiversity. The European Marine Omics Biodiversity Observation Network (EMO BON) is a network of GOs that conduct regular biological community samplings to generate environmental and metagenomic data of microbial communities from designated marine stations around Europe. The development of an effective workflow is essential for the analysis of the EMO BON metagenomic data in a timely and reproducible manner.

FINDINGS

Based on the established MGnify resource, we developed metaGOflow. metaGOflow supports the fast inference of taxonomic profiles from GO-derived data based on ribosomal RNA genes and their functional annotation using the raw reads. Thanks to the Research Object Crate packaging, relevant metadata about the sample under study, and the details of the bioinformatics analysis it has been subjected to, are inherited to the data product while its modular implementation allows running the workflow partially. The analysis of 2 EMO BON samples and 1 Tara Oceans sample was performed as a use case.

CONCLUSIONS

metaGOflow is an efficient and robust workflow that scales to the needs of projects producing big metagenomic data such as EMO BON. It highlights how containerization technologies along with modern workflow languages and metadata package approaches can support the needs of researchers when dealing with ever-increasing volumes of biological data. Despite being initially oriented to address the needs of EMO BON, metaGOflow is a flexible and easy-to-use workflow that can be broadly used for one-sample-at-a-time analysis of shotgun metagenomics data.

The First Pituitary Proteome Landscape From Matched Anterior and Posterior Lobes for a Better Understanding of the Pituitary Gland

To date, very few mass spectrometry (MS)-based proteomics studies are available on the anterior and posterior lobes of the pituitary. In the past, MS-based investigations have focused exclusively on the whole pituitary gland or anterior pituitary lobe. In this study, for the first time, we performed a deep MS-based analysis of five anterior and five posterior matched lobes to build the first lobe-specific pituitary proteome map, which documented 4090 proteins with isoforms, mostly mapped into chromosomes 1, 2, and 11. About 1446 differentially expressed significant proteins were identified, which were studied for lobe specificity, biological pathway enrichment, protein-protein interaction, regions specific to comparison of human brain and other neuroendocrine glands from Human Protein Atlas to identify pituitary-enriched proteins. Hormones specific to each lobe were also identified and validated with parallel reaction monitoring-based target verification. The study identified and validated hormones, growth hormone and thyroid-stimulating hormone subunit beta, exclusively to the anterior lobe whereas oxytocin-neurophysin 1 and arginine vasopressin to the posterior lobe. The study also identified proteins POU1F1 (pituitary-specific positive transcription factor 1), POMC (pro-opiomelanocortin), PCOLCE2 (procollagen C-endopeptidase enhancer 2), and NPTX2 (neuronal pentraxin-2) as pituitary-enriched proteins and was validated for their lobe specificity using parallel reaction monitoring. In addition, three uPE1 proteins, namely THEM6 (mesenchymal stem cell protein DSCD75), FSD1L (coiled-coil domain-containing protein 10), and METTL26 (methyltransferase-like 26), were identified using the NeXtProt database, and depicted tumor markers S100 proteins having high expression in the posterior lobe. In summary, the study documents the first matched anterior and posterior pituitary proteome map acting as a reference control for a better understanding of functional and nonfunctional pituitary adenomas and extrapolating the aim of the Human Proteome Project towards the investigation of the proteome of life.

Flavobacterial exudates disrupt cell cycle progression and metabolism of the diatom Thalassiosira pseudonana

Phytoplankton and bacteria form the base of marine ecosystems and their interactions drive global biogeochemical cycles. The effects of bacteria and bacteria-produced compounds on diatoms range from synergistic to pathogenic and can affect the physiology and transcriptional patterns of the interacting diatom. Here, we investigate physiological and transcriptional changes in the marine diatom Thalassiosira pseudonana induced by extracellular metabolites of a known antagonistic bacterium Croceibacter atlanticus. Mono-cultures of C. atlanticus released compounds that inhibited diatom cell division and elicited a distinctive morphology of enlarged cells with increased chloroplast content and enlarged nuclei, similar to what was previously observed when the diatom was co-cultured with live bacteria. The extracellular C. atlanticus metabolites induced transcriptional changes in diatom pathways that include recognition and signaling pathways, cell cycle regulation, carbohydrate and amino acid production, as well as cell wall stability. Phenotypic analysis showed a disruption in the diatom cell cycle progression and an increase in both intra- and extracellular carbohydrates in diatom cultures after bacterial exudate treatment. The transcriptional changes and corresponding phenotypes suggest that extracellular bacterial metabolites, produced independently of direct bacterial-diatom interaction, may modulate diatom metabolism in ways that support bacterial growth.

A haplotype-resolved genome assembly of the Nile rat facilitates exploration of the genetic basis of diabetes

Background

The Nile rat (Avicanthis niloticus) is an important animal model because of its robust diurnal rhythm, a cone-rich retina, and a propensity to develop diet-induced diabetes without chemical or genetic modifications. A closer similarity to humans in these aspects, compared to the widely used Mus musculus and Rattus norvegicus models, holds the promise of better translation of research findings to the clinic.

Results

We report a 2.5 Gb, chromosome-level reference genome assembly with fully resolved parental haplotypes, generated with the Vertebrate Genomes Project (VGP). The assembly is highly contiguous, with contig N50 of 11.1 Mb, scaffold N50 of 83 Mb, and 95.2% of the sequence assigned to chromosomes. We used a novel workflow to identify 3613 segmental duplications and quantify duplicated genes. Comparative analyses revealed unique genomic features of the Nile rat, including some that affect genes associated with type 2 diabetes and metabolic dysfunctions. We discuss 14 genes that are heterozygous in the Nile rat or highly diverged from the house mouse.

Conclusions

Our findings reflect the exceptional level of genomic resolution present in this assembly, which will greatly expand the potential of the Nile rat as a model organism.

BioAlign: An Accurate Global PPI Network Alignment Algorithm

Motivation

The advancement of high-throughput PPI profiling techniques results in generating a large amount of PPI data. The alignment of the PPI networks uncovers the relationship between the species that can help understand the biological systems. The comparative study reveals the conserved biological interactions of the proteins across the species. It can also help study the biological pathways and signal networks of the cells. Although several network alignment algorithms are developed to study and compare the PPI data, the development of the aligner that aligns the PPI networks with high biological similarity and coverage is still challenging.

Results

This paper presents a novel global network alignment algorithm, BioAlign, that incorporates a significant amount of biological information. Existing studies use global sequence and/or 3D-structure similarity to align the PPI networks. In contrast, BioAlign uses the local sequence similarity, predicted secondary structure motifs, and remote homology in addition to global sequence and 3D-structure similarity. The extra sources of biological information help BioAlign to align the proteins with high biological similarity. BioAlign produces significantly better results in terms of AFS and Coverage (6-32 and 7-34 with respect to MF and BP, respectively) than the existing algorithms. BioAlign aligns a much larger number of proteins that have high biological similarities as compared to the existing aligners. BioAlign helps in studying the functionally similar protein pairs across the species.

Comparative Transcriptome Analysis of Organ-Specific Adaptive Responses to Hypoxia Provides Insights to Human Diseases

The common carp is a hypoxia-tolerant fish, and the understanding of its ability to live in low-oxygen environments has been applied to human health issues such as cancer and neuron degeneration. Here, we investigated differential gene expression changes during hypoxia in five common carp organs including the brain, the gill, the head kidney, the liver, and the intestine. Based on RNA sequencing, gene expression changes under hypoxic conditions were detected in over 1800 genes in common carp. The analysis of these genes further revealed that all five organs had high expression-specific properties. According to the results of the GO and KEGG, the pathways involved in the adaptation to hypoxia provided information on responses specific to each organ in low oxygen, such as glucose metabolism and energy usage, cholesterol synthesis, cell cycle, circadian rhythm, and dopamine activation. DisGeNET analysis showed that some human diseases such as cancer, diabetes, epilepsy, metabolism diseases, and social ability disorders were related to hypoxia-regulated genes. Our results suggested that common carp undergo various gene regulations in different organs under hypoxic conditions, and integrative bioinformatics may provide some potential targets for advancing disease research.

GeneTonic: an R/Bioconductor package for streamlining the interpretation of RNA-seq data

BACKGROUND

The interpretation of results from transcriptome profiling experiments via RNA sequencing (RNA-seq) can be a complex task, where the essential information is distributed among different tabular and list formats-normalized expression values, results from differential expression analysis, and results from functional enrichment analyses. A number of tools and databases are widely used for the purpose of identification of relevant functional patterns, yet often their contextualization within the data and results at hand is not straightforward, especially if these analytic components are not combined together efficiently.

RESULTS

We developed the GeneTonic software package, which serves as a comprehensive toolkit for streamlining the interpretation of functional enrichment analyses, by fully leveraging the information of expression values in a differential expression context. GeneTonic is implemented in R and Shiny, leveraging packages that enable HTML-based interactive visualizations for executing drilldown tasks seamlessly, viewing the data at a level of increased detail. GeneTonic is integrated with the core classes of existing Bioconductor workflows, and can accept the output of many widely used tools for pathway analysis, making this approach applicable to a wide range of use cases. Users can effectively navigate interlinked components (otherwise available as flat text or spreadsheet tables), bookmark features of interest during the exploration sessions, and obtain at the end a tailored HTML report, thus combining the benefits of both interactivity and reproducibility.

CONCLUSION

GeneTonic is distributed as an R package in the Bioconductor project ( https://bioconductor.org/packages/GeneTonic/ ) under the MIT license. Offering both bird's-eye views of the components of transcriptome data analysis and the detailed inspection of single genes, individual signatures, and their relationships, GeneTonic aims at simplifying the process of interpretation of complex and compelling RNA-seq datasets for many researchers with different expertise profiles.

Doxycycline Changes the Transcriptome Profile of mIMCD3 Renal Epithelial Cells

Tetracycline-inducible gene expression systems have been used successfully to study gene function in vivo and in vitro renal epithelial models but the effects of the common inducing agent, doxycycline (DOX), on gene expression are not well appreciated. Here, we evaluated the DOX effects on the transcriptome of a widely used renal epithelial cell model, mIMCD3 cells, to establish a reference. Cells were grown on permeable filter supports in the absence and presence of DOX (3 or 6 days), and genome-wide transcriptome profiles were assessed using RNA-Seq. We found DOX significantly altered the transcriptome profile, changing the abundance of 1,549 transcripts at 3 days and 2,643 transcripts at 6 days. Within 3 days of treatment, DOX significantly decreased the expression of multiple signaling pathways (ERK, cAMP, and Notch) that are associated with cell proliferation and differentiation. Genes associated with cell cycle progression were subsequently downregulated in cells treated with DOX for 6 days, as were genes involved in cellular immune response processes and several cytokines and chemokines, correlating with a remarkable repression of genes encoding cell proliferation markers. The results provide new insight into responses of renal epithelial cells to DOX and a establish a resource for DOX-mediated gene expression systems.

Disturbance of phylogenetic layer-specific adaptation of human brain gene expression in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with typical neuropathological hallmarks, such as neuritic plaques and neurofibrillary tangles, preferentially found at layers III and V. The distribution of both hallmarks provides the basis for the staging of AD, following a hierarchical pattern throughout the cerebral cortex. To unravel the background of this layer-specific vulnerability, we evaluated differential gene expression of supragranular and infragranular layers and subcortical white matter in both healthy controls and AD patients. We identified AD-associated layer-specific differences involving protein-coding and non-coding sequences, most of those present in the subcortical white matter, thus indicating a critical role for long axons and oligodendrocytes in AD pathomechanism. In addition, GO analysis identified networks containing synaptic vesicle transport, vesicle exocytosis and regulation of neurotransmitter levels. Numerous AD-associated layer-specifically expressed genes were previously reported to undergo layer-specific switches in recent hominid brain evolution between layers V and III, i.e., those layers that are most vulnerable to AD pathology. Against the background of our previous finding of accelerated evolution of AD-specific gene expression, here we suggest a critical role in AD pathomechanism for this phylogenetic layer-specific adaptation of gene expression, which is most prominently seen in the white matter compartment.

Profiling of 3D Genome Organization in Nasopharyngeal Cancer Needle Biopsy Patient Samples by a Modified Hi-C Approach

Nasopharyngeal cancer (NPC), a cancer derived from epithelial cells in the nasopharynx, is a cancer common in China, Southeast Asia, and Africa. The three-dimensional (3D) genome organization of nasopharyngeal cancer is poorly understood. A major challenge in understanding the 3D genome organization of cancer samples is the lack of a method for the characterization of chromatin interactions in solid cancer needle biopsy samples. Here, we developed Biop-C, a modified in situ Hi-C method using solid cancer needle biopsy samples. We applied Biop-C to characterize three nasopharyngeal cancer solid cancer needle biopsy patient samples. We identified topologically associated domains (TADs), chromatin interaction loops, and frequently interacting regions (FIREs) at key oncogenes in nasopharyngeal cancer from the Biop-C heatmaps. We observed that the genomic features are shared at some important oncogenes, but the patients also display extensive heterogeneity at certain genomic loci. On analyzing the super enhancer landscape in nasopharyngeal cancer cell lines, we found that the super enhancers are associated with FIREs and can be linked to distal genes via chromatin loops in NPC. Taken together, our results demonstrate the utility of our Biop-C method in investigating 3D genome organization in solid cancers.

TGF-β/activin signaling promotes CDK7 inhibitor resistance in triple-negative breast cancer cells through upregulation of multidrug transporters

Cyclin-dependent kinase 7 (CDK7) is a master regulatory kinase that drives cell cycle progression and stimulates expression of oncogenes in a myriad of cancers. Inhibitors of CDK7 (CDK7i) are currently in clinical trials; however, as with many cancer therapies, patients will most likely experience recurrent disease due to acquired resistance. Identifying targets underlying CDK7i resistance will facilitate prospective development of new therapies that can circumvent such resistance. Here we utilized triple-negative breast cancer as a model to discern mechanisms of resistance as it has been previously shown to be highly responsive to CDK7 inhibitors. After generating cell lines with acquired resistance, high-throughput RNA sequencing revealed significant upregulation of genes associated with efflux pumps and transforming growth factor-beta (TGF-β) signaling pathways. Genetic silencing or pharmacological inhibition of ABCG2, an efflux pump associated with multidrug resistance, resensitized resistant cells to CDK7i, indicating a reliance on these transporters. Expression of activin A (INHBA), a member of the TGF-β family of ligands, was also induced, whereas its intrinsic inhibitor, follistatin (FST), was repressed. In resistant cells, increased phosphorylation of SMAD3, a downstream mediator, confirmed an increase in activin signaling, and phosphorylated SMAD3 directly bound the ABCG2 promoter regulatory region. Finally, pharmacological inhibition of TGF-β/activin receptors or genetic silencing of SMAD4, a transcriptional partner of SMAD3, reversed the upregulation of ABCG2 in resistant cells and phenocopied ABCG2 inhibition. This study reveals that inhibiting the TGF-β/Activin-ABCG2 pathway is a potential avenue for preventing or overcoming resistance to CDK7 inhibitors.

EnrichVisBox: A Versatile and Powerful Web Toolbox for Visualizing Complex Functional Enrichment Results of Omics Data

Efficient visualization helps researchers obtain valuable mechanistic insights and present interesting results with regard to the functional enrichment analysis of omics data. However, the functions of existing published tools used to implement relevant visualization are neither sufficiently comprehensive nor easily accessible. Most of these tools require users to have professional programming skills. This study alleviates this issue by proposing EnrichVisBox, a web application developed for integrative and versatile data visualization, including bubble plots, UpSet plots, polar bar plots, rectangle plots, ridgeline plots, network plots, and variant chord plots. Specifically, scientists can use these insightful plots to conveniently present functional enrichment analysis results of omics data with a simple mouse click through a user-friendly interface.

Diagnosis of corona diseases from associated genes and X-ray images using machine learning algorithms and deep CNN

Novel Coronavirus with its highly transmittable characteristics is rapidly spreading, endangering millions of human lives and the global economy. To expel the chain of alteration and subversive expansion, early and effective diagnosis of infected patients is immensely important. Unfortunately, there is a lack of testing equipment in many countries as compared with the number of infected patients. It would be desirable to have a swift diagnosis with identification of COVID-19 from disease genes or from CT or X-Ray images. COVID-19 causes flus, cough, pneumonia, and lung infection in patients, wherein massive alveolar damage and progressive respiratory failure can lead to death. This paper proposes two different detection methods - the first is a Gene-based screening method to detect Corona diseases (Middle East respiratory syndrome-related coronavirus, Severe acute respiratory syndrome coronavirus 2, and Human coronavirus HKU1) and differentiate it from Pneumonia. This novel approach to healthcare utilizes disease genes to build functional semantic similarity among genes. Different machine learning algorithms - eXtreme Gradient Boosting, Naïve Bayes, Regularized Random Forest, Random Forest Rule-Based Model, Random Ferns, C5.0 and Multi-Layer Perceptron, are trained and tested on the semantic similarities to classify Corona and Pneumonia diseases. The best performing models are then ensembled, yielding an accuracy of nearly 93%. The second diagnosis technique proposed herein is an automated COVID-19 diagnostic method which uses chest X-ray images to classify Normal versus COVID-19 and Pneumonia versus COVID-19 images using the deep-CNN technique, achieving 99.87% and 99.48% test accuracy. Thus, this research can be an assistance for providing better treatment against COVID-19.

The Translational Machine: A novel machine-learning approach to illuminate complex genetic architectures

The Translational Machine (TM) is a machine learning (ML)-based analytic pipeline that translates genotypic/variant call data into biologically contextualized features that richly characterize complex variant architectures and permit greater interpretability and biological replication. It also reduces potentially confounding effects of population substructure on outcome prediction. The TM consists of three main components. First, replicable but flexible feature engineering procedures translate genome-scale data into biologically informative features that appropriately contextualize simple variant calls/genotypes within biological and functional contexts. Second, model-free, nonparametric ML-based feature filtering procedures empirically reduce dimensionality and noise of both original genotype calls and engineered features. Third, a powerful ML algorithm for feature selection is used to differentiate risk variant contributions across variant frequency and functional prediction spectra. The TM simultaneously evaluates potential contributions of variants operative under polygenic and heterogeneous models of genetic architecture. Our TM enables integration of biological information (e.g., genomic annotations) within conceptual frameworks akin to geneset-/pathways-based and collapsing methods, but overcomes some of these methods' limitations. The full TM pipeline is executed in R. Our approach and initial findings from its application to a whole-exome schizophrenia case-control data set are presented. These TM procedures extend the findings of the primary investigation and yield novel results.

Changes in the phosphorylation of nucleotide metabolism‑associated proteins by leukemia inhibitory factor in mouse embryonic stem cells

Leukemia inhibitory factor (LIF) is a stem cell growth factor that maintains self‑renewal of mouse embryonic stem cells (mESCs). LIF is a cytokine in the interleukin‑6 family and signals via the common receptor subunit gp130 and ligand‑specific LIF receptor. LIF causes heterodimerization of the LIF receptor and gp130, activating the Janus kinase/STAT and MAPK pathways, resulting in changes in protein phosphorylation. The present study profiled LIF‑mediated protein phosphorylation changes in mESCs via proteomic analysis. mESCs treated in the presence or absence of LIF were analyzed via two‑dimensional differential in‑gel electrophoresis and protein and phosphoprotein staining. Protein identification was performed by matrix‑assisted laser desorption/ionization‑time of flight mass spectrophotometry. Increased phosphorylation of 16 proteins and decreased phosphorylation of 34 proteins in response to LIF treatment was detected. Gene Ontology terms enriched in these proteins included 'organonitrogen compound metabolic process', 'regulation of mRNA splicing via spliceosome' and 'nucleotide metabolic process'. The present results revealed that LIF modulated phosphorylation levels of nucleotide metabolism‑associated proteins, thus providing insight into the mechanism underlying LIF action in mESCs.

Genetic Variability in Molecular Pathways Implicated in Alzheimer's Disease: A Comprehensive Review

Alzheimer's disease (AD) is a complex neurodegenerative disease, affecting a significant part of the population. The majority of AD cases occur in the elderly with a typical age of onset of the disease above 65 years. AD presents a major burden for the healthcare system and since population is rapidly aging, the burden of the disease will increase in the future. However, no effective drug treatment for a full-blown disease has been developed to date. The genetic background of AD is extensively studied; numerous genome-wide association studies (GWAS) identified significant genes associated with increased risk of AD development. This review summarizes more than 100 risk loci. Many of them may serve as biomarkers of AD progression, even in the preclinical stage of the disease. Furthermore, we used GWAS data to identify key pathways of AD pathogenesis: cellular processes, metabolic processes, biological regulation, localization, transport, regulation of cellular processes, and neurological system processes. Gene clustering into molecular pathways can provide background for identification of novel molecular targets and may support the development of tailored and personalized treatment of AD.

Are the 50's, the transition decade, in choroid plexus aging?

The choroid plexus (CP) is an important structure for the brain. Besides its major role in the production of cerebrospinal fluid (CSF), it conveys signals originating from the brain, and from the circulatory system, shaping brain function in health and in pathology. Previous studies in rodents have revealed altered transcriptome both during aging and in various diseases of the central nervous system, including Alzheimer's disease. In the present study, a high-throughput sequencing of the CP transcriptome was performed in postmortem samples of clinically healthy individuals aged 50's through 80's. The data shows an age-related profile, with the main changes occurring in the transition from the 50's to the 60's, stabilizing thereafter. Specifically, neuronal and membrane functions distinguish the transcriptome between the 50's and the 60's, while neuronal and axon development and extracellular structure organization differentiate the 50's from the 70's. These findings suggest that changes in the CP transcriptome occur early in the aging process. Future studies will unravel whether these relate with processes occurring in late- onset brain diseases.

Embeddings from deep learning transfer GO annotations beyond homology

Knowing protein function is crucial to advance molecular and medical biology, yet experimental function annotations through the Gene Ontology (GO) exist for fewer than 0.5% of all known proteins. Computational methods bridge this sequence-annotation gap typically through homology-based annotation transfer by identifying sequence-similar proteins with known function or through prediction methods using evolutionary information. Here, we propose predicting GO terms through annotation transfer based on proximity of proteins in the SeqVec embedding rather than in sequence space. These embeddings originate from deep learned language models (LMs) for protein sequences (SeqVec) transferring the knowledge gained from predicting the next amino acid in 33 million protein sequences. Replicating the conditions of CAFA3, our method reaches an Fmax of 37 ± 2%, 50 ± 3%, and 57 ± 2% for BPO, MFO, and CCO, respectively. Numerically, this appears close to the top ten CAFA3 methods. When restricting the annotation transfer to proteins with < 20% pairwise sequence identity to the query, performance drops (Fmax BPO 33 ± 2%, MFO 43 ± 3%, CCO 53 ± 2%); this still outperforms naïve sequence-based transfer. Preliminary results from CAFA4 appear to confirm these findings. Overall, this new concept is likely to change the annotation of proteins, in particular for proteins from smaller families or proteins with intrinsically disordered regions.

PAFway: pairwise associations between functional annotations in biological networks and pathways

Motivation

Large gene networks can be dense and difficult to interpret in a biologically meaningful way.

Results

Here, we introduce PAFway, which estimates pairwise associations between functional annotations in biological networks and pathways. It answers the biological question: do genes that have a specific function tend to regulate genes that have a different specific function? The results can be visualized as a heatmap or a network of biological functions. We apply this package to reveal associations between functional annotations in an Arabidopsis thaliana gene network.

Availability and implementation

PAFway is submitted to CRAN. Currently available here: https://github.com/ezer/PAFway.

Supplementary information

Supplementary data are available at Bioinformatics online.

SAlign-a structure aware method for global PPI network alignment

BACKGROUND

High throughput experiments have generated a significantly large amount of protein interaction data, which is being used to study protein networks. Studying complete protein networks can reveal more insight about healthy/disease states than studying proteins in isolation. Similarly, a comparative study of protein-protein interaction (PPI) networks of different species reveals important insights which may help in disease analysis and drug design. The study of PPI network alignment can also helps in understanding the different biological systems of different species. It can also be used in transfer of knowledge across different species. Different aligners have been introduced in the last decade but developing an accurate and scalable global alignment algorithm that can ensures the biological significance alignment is still challenging.

RESULTS

This paper presents a novel global pairwise network alignment algorithm, SAlign, which uses topological and biological information in the alignment process. The proposed algorithm incorporates sequence and structural information for computing biological scores, whereas previous algorithms only use sequence information. The alignment based on the proposed technique shows that the combined effect of structure and sequence results in significantly better pairwise alignments. We have compared SAlign with state-of-art algorithms on the basis of semantic similarity of alignment and the number of aligned nodes on multiple PPI network pairs. The results of SAlign on the network pairs which have high percentage of proteins with available structure are 3-63% semantically better than all existing techniques. Furthermore, it also aligns 5-14% more nodes of these network pairs as compared to existing aligners. The results of SAlign on other PPI network pairs are comparable or better than all existing techniques. We also introduce [Formula: see text], a Monte Carlo based alignment algorithm, that produces multiple network alignments with similar semantic similarity. This helps the user to pick biologically meaningful alignments.

CONCLUSION

The proposed algorithm has the ability to find the alignments that are more biologically significant/relevant as compared to the alignments of existing aligners. Furthermore, the proposed method is able to generate alternate alignments that help in studying different genes/proteins of the specie.

Automatic Gene Function Prediction in the 2020's

The current rate at which new DNA and protein sequences are being generated is too fast to experimentally discover the functions of those sequences, emphasizing the need for accurate Automatic Function Prediction (AFP) methods. AFP has been an active and growing research field for decades and has made considerable progress in that time. However, it is certainly not solved. In this paper, we describe challenges that the AFP field still has to overcome in the future to increase its applicability. The challenges we consider are how to: (1) include condition-specific functional annotation, (2) predict functions for non-model species, (3) include new informative data sources, (4) deal with the biases of Gene Ontology (GO) annotations, and (5) maximally exploit the GO to obtain performance gains. We also provide recommendations for addressing those challenges, by adapting (1) the way we represent proteins and genes, (2) the way we represent gene functions, and (3) the algorithms that perform the prediction from gene to function. Together, we show that AFP is still a vibrant research area that can benefit from continuing advances in machine learning with which AFP in the 2020s can again take a large step forward reinforcing the power of computational biology.

Breed Differences in Dog Cognition Associated with Brain-Expressed Genes and Neurological Functions

Given their remarkable phenotypic diversity, dogs present a unique opportunity for investigating the genetic bases of cognitive and behavioral traits. Our previous work demonstrated that genetic relatedness among breeds accounts for a substantial portion of variation in dog cognition. Here, we investigated the genetic architecture of breed differences in cognition, seeking to identify genes that contribute to variation in cognitive phenotypes. To do so, we combined cognitive data from the citizen science project Dognition.com with published breed-average genetic polymorphism data, resulting in a dataset of 1654 individuals with cognitive phenotypes representing 49 breeds. We conducted a breed-average genome-wide association study to identify specific polymorphisms associated with breed differences in inhibitory control, communication, memory, and physical reasoning. We found five single nucleotide polymorphisms (SNPs) that reached genome-wide significance after Bonferroni correction, located in EML1, OR52E2, HS3ST5, a U6 spliceosomal RNA, and a long noncoding RNA. When we combined results across multiple SNPs within the same gene, we identified 188 genes implicated in breed differences in cognition. This gene set included more genes than expected by chance that were (1) differentially expressed in brain tissue and (2) involved in nervous system functions including peripheral nervous system development, Wnt signaling, presynapse assembly, and synaptic vesicle exocytosis. These results advance our understanding of the genetic underpinnings of complex cognitive phenotypes and identify specific genetic variants for further research.

The molecular landscape of ETMR at diagnosis and relapse

Embryonal tumours with multilayered rosettes (ETMRs) are aggressive paediatric embryonal brain tumours with a universally poor prognosis1. Here we collected 193 primary ETMRs and 23 matched relapse samples to investigate the genomic landscape of this distinct tumour type. We found that patients with tumours in which the proposed driver C19MC2-4 was not amplified frequently had germline mutations in DICER1 or other microRNA-related aberrations such as somatic amplification of miR-17-92 (also known as MIR17HG). Whole-genome sequencing revealed that tumours had an overall low recurrence of single-nucleotide variants (SNVs), but showed prevalent genomic instability caused by widespread occurrence of R-loop structures. We show that R-loop-associated chromosomal instability can be induced by the loss of DICER1 function. Comparison of primary tumours and matched relapse samples showed a strong conservation of structural variants, but low conservation of SNVs. Moreover, many newly acquired SNVs are associated with a mutational signature related to cisplatin treatment. Finally, we show that targeting R-loops with topoisomerase and PARP inhibitors might be an effective treatment strategy for this deadly disease.

Highly heritable and functionally relevant breed differences in dog behaviour

Variation across dog breeds presents a unique opportunity to investigate the evolution and biological basis of complex behavioural traits. We integrated behavioural data from more than 14 000 dogs from 101 breeds with breed-averaged genotypic data (n = 5697 dogs) from over 100 000 loci in the dog genome. We found high levels of among-breed heritability for 14 behavioural traits (the proportion of trait variance attributable to genetic similarity among breeds). We next identified 131 single nucleotide polymorphisms associated with breed differences in behaviour, which were found in genes that are highly expressed in the brain and enriched for neurobiological functions and developmental processes, suggesting that they may be functionally associated with behavioural differences. Our results shed light on the heritability and genetic architecture of complex behavioural traits and identify dogs as a powerful model in which to address these questions.

Computational identification of protein-protein interactions in model plant proteomes

Protein-protein interactions (PPIs) play essential roles in many biological processes. A PPI network provides crucial information on how biological pathways are structured and coordinated from individual protein functions. In the past two decades, large-scale PPI networks of a handful of organisms were determined by experimental techniques. However, these experimental methods are time-consuming, expensive, and are not easy to perform on new target organisms. Large-scale PPI data is particularly sparse in plant organisms. Here, we developed a computational approach for detecting PPIs trained and tested on known PPIs of Arabidopsis thaliana and applied to three plants, Arabidopsis thaliana, Glycine max (soybean), and Zea mays (maize) to discover new PPIs on a genome-scale. Our method considers a variety of features including protein sequences, gene co-expression, functional association, and phylogenetic profiles. This is the first work where a PPI prediction method was developed for is the first PPI prediction method applied on benchmark datasets of Arabidopsis. The method showed a high prediction accuracy of over 90% and very high precision of close to 1.0. We predicted 50,220 PPIs in Arabidopsis thaliana, 13,175,414 PPIs in corn, and 13,527,834 PPIs in soybean. Newly predicted PPIs were classified into three confidence levels according to the availability of existing supporting evidence and discussed. Predicted PPIs in the three plant genomes are made available for future reference.

GOnet: a tool for interactive Gene Ontology analysis

BACKGROUND

Biological interpretation of gene/protein lists resulting from -omics experiments can be a complex task. A common approach consists of reviewing Gene Ontology (GO) annotations for entries in such lists and searching for enrichment patterns. Unfortunately, there is a gap between machine-readable output of GO software and its human-interpretable form. This gap can be bridged by allowing users to simultaneously visualize and interact with term-term and gene-term relationships.

RESULTS

We created the open-source GOnet web-application (available at http://tools.dice-database.org/GOnet/ ), which takes a list of gene or protein entries from human or mouse data and performs GO term annotation analysis (mapping of provided entries to GO subsets) or GO term enrichment analysis (scanning for GO categories overrepresented in the input list). The application is capable of producing parsable data formats and importantly, interactive visualizations of the GO analysis results. The interactive results allow exploration of genes and GO terms as a graph that depicts the natural hierarchy of the terms and retains relationships between terms and genes/proteins. As a result, GOnet provides insight into the functional interconnection of the submitted entries.

CONCLUSIONS

The application can be used for GO analysis of any biological data sources resulting in gene/protein lists. It can be helpful for experimentalists as well as computational biologists working on biological interpretation of -omics data resulting in such lists.

A document level neural model integrated domain knowledge for chemical-induced disease relations

BACKGROUND

The effective combination of texts and knowledge may improve performances of natural language processing tasks. For the recognition of chemical-induced disease (CID) relations which may span sentence boundaries in an article, although existing CID systems explored the utilization for knowledge bases, the effects of different knowledge on the identification of a special CID haven't been distinguished by these systems. Moreover, systems based on neural network only constructed sentence or mention level models.

RESULTS

In this work, we proposed an effective document level neural model integrated domain knowledge to extract CID relations from biomedical articles. Basic semantic information of an article with respect to a special CID candidate pair was learned from the document level sub-network module. Furthermore, knowledge attention depending on the representation of the article was proposed to distinguish the influences of different knowledge on the special CID pair and then the final representation of knowledge was formed by aggregating weighed knowledge. Finally, the integrated representations of texts and knowledge were passed to a softmax classifier to perform the CID recognition. Experimental results on the chemical-disease relation corpus proposed by BioCreative V show that our proposed system integrated knowledge achieves a good overall performance compared with other state-of-the-art systems.

CONCLUSIONS

Experimental analyses demonstrate that the introduced attention mechanism on domain knowledge plays a significant role in distinguishing influences of different knowledge on the judgment for a special CID relation.

Computational Methods for Predicting Protein-Protein Interactions Using Various Protein Features

Understanding protein-protein interactions (PPIs) in a cell is essential for learning protein functions, pathways, and mechanism of diseases. PPIs are also important targets for developing drugs. Experimental methods, both small-scale and large-scale, have identified PPIs in several model organisms. However, results cover only a part of PPIs of organisms; moreover, there are many organisms whose PPIs have not yet been investigated. To complement experimental methods, many computational methods have been developed that predict PPIs from various characteristics of proteins. Here we provide an overview of literature reports to classify computational PPI prediction methods that consider different features of proteins, including protein sequence, genomes, protein structure, function, PPI network topology, and those which integrate multiple methods. © 2018 by John Wiley & Sons, Inc.

DextMP: deep dive into text for predicting moonlighting proteins

Motivation

Moonlighting proteins (MPs) are an important class of proteins that perform more than one independent cellular function. MPs are gaining more attention in recent years as they are found to play important roles in various systems including disease developments. MPs also have a significant impact in computational function prediction and annotation in databases. Currently MPs are not labeled as such in biological databases even in cases where multiple distinct functions are known for the proteins. In this work, we propose a novel method named DextMP, which predicts whether a protein is a MP or not based on its textual features extracted from scientific literature and the UniProt database.

Results

DextMP extracts three categories of textual information for a protein: titles, abstracts from literature, and function description in UniProt. Three language models were applied and compared: a state-of-the-art deep unsupervised learning algorithm along with two other language models of different types, Term Frequency-Inverse Document Frequency in the bag-of-words and Latent Dirichlet Allocation in the topic modeling category. Cross-validation results on a dataset of known MPs and non-MPs showed that DextMP successfully predicted MPs with over 91% accuracy with significant improvement over existing MP prediction methods. Lastly, we ran DextMP with the best performing language models and text-based feature combinations on three genomes, human, yeast and Xenopus laevis, and found that about 2.5–35% of the proteomes are potential MPs.

Availability and Implementation

Code available at http://kiharalab.org/DextMP.

GO FEAT: a rapid web-based functional annotation tool for genomic and transcriptomic data

Downstream analysis of genomic and transcriptomic sequence data is often executed by functional annotation that can be performed by various bioinformatics tools and biological databases. However, a full fast integrated tool is not available for such analysis. Besides, the current available software is not able to produce analytic lists of annotations and graphs to help users in evaluating the output results. Therefore, we present the Gene Ontology Functional Enrichment Annotation Tool (GO FEAT), a free web platform for functional annotation and enrichment of genomic and transcriptomic data based on sequence homology search. The analysis can be customized and visualized as per users’ needs and specifications. GO FEAT is freely available at http://computationalbiology.ufpa.br/gofeat/ and its source code is hosted at https://github.com/fabriciopa/gofeat.

Computing and Visualizing Gene Function Similarity and Coherence with NaviGO

Gene ontology (GO) is a controlled vocabulary of gene functions across all species, which is widely used for functional analyses of individual genes and large-scale proteomic studies. NaviGO is a webserver for visualizing and quantifying the relationship and similarity of GO annotations. Here, we walk through functionality of the NaviGO webserver ( http://kiharalab.org/web/navigo/ ) using an example input and explain what can be learned from analysis results. NaviGO has four main functions, accessed from each page of the webserver: "GO Parents," "GO Set", "GO Enrichment", and "Protein Set." For a given list of GO terms, the "GO Parents" tab visualizes the hierarchical relationship of GO terms, and the "GO Set" tab calculates six functional similarity and association scores and presents results in a network and a multidimensional scaling plot. For a set of proteins and their associated GO terms, the "GO Enrichment" tab calculates protein GO functional enrichment, while the "Protein Set" tab calculates functional association between proteins. The NaviGO source code can be also downloaded and used locally or integrated into other software pipelines.