https://3dgnome.mini.pw.edu.pl/
3D genome organization plays a critical role in its functioning. Alterations of this organization caused by structural variants (SVs) may lead to changes in gene transcription or even to disease. The recent advent of Chromosome Conformation Capture (3C) based techniques such as ChIA-PET and Hi-C allows us to investigate genome spatial organization. The rising volume of sequencing data in turn enables highly accurate identification of structural variation. Combination of those two sources of information can reveal mechanisms of genome regulation.
The current available in-situ CTCF and RNAPII ChIA-PET chromatin contacts obtained from the GM12878 cell line mapped to the GRCh38 genome assembly and extended the 1000 Genomes SVs dataset (3202 samples on GRCh38).
This web service provides ensembles of 3D models of genomic structures for all 3,202 samples from the 1000 Genomes Project phase 3 SV release. Enter an ID of a sample and choose a genomic region of interest at the Run analysis page to obtain a 3D model of the region for this particular individual. The models are built at the resolution of individual chromatin loops and visualize alterations emerging in genomic structures after introduction of SVs.
You can also upload to the service your own interaction data in bedpe (paired-end BED) format together with bed or vcf file containing SVs to visualize structures formed by the submitted long-range contacts and altered by the provided SVs. The CTCF/RNAPII arcs are visualized using the IGV tool with additional genes and SVs annotations. For 3D model visualization, we use a viewer: NGL, where we provide colouring by gene and enhancer location. The models are downloadable in mmcif and xyz format.
- The webserver was described in Nucleic Acids Research :
3D-GNOME 2.0: a three-dimensional genome modeling engine for predicting structural variation-driven alterations of chromatin spatial structure in the human genome - SV-based chromatin modifying algorithm was described in Genome Biology:
Spatial chromatin architecture alteration by structural variations in human genomes at population scale
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OnTarget
 OnTarget is a webserver that streamlines the MiniPromoter design process. Users only need to specify a gene of interest or custom genomic coordinates on which to focus the identification of promoters and enhancers, and can also provide relevant cell-type-specific genomic evidence (e.g. accessible chromatin regions, histone modifications, etc.). OnTarget combines the provided data with internal data to identify candidate promoters and enhancers and design MiniPromoters.
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gProfiler
https://biit.cs.ut.ee/gprofiler
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g:GOSt performs functional enrichment analysis, also known as over-representation analysis (ORA) or gene set enrichment analysis, on input gene list. It maps genes to known functional information sources and detects statistically significantly enriched terms. We regularly retrieve data from Ensembl database and fungi, plants or metazoa specific versions of Ensembl Genomes, and parasite specific data from WormBase ParaSite.
In addition to Gene Ontology, we include pathways from KEGG Reactome and WikiPathways; miRNA targets from miRTarBase and regulatory motif matches from TRANSFAC; tissue specificity from Human Protein Atlas; protein complexes from CORUM and human disease phenotypes from Human Phenotype Ontology. g:GOSt supports close to 500 organisms and accepts hundreds of identifier types.
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https://caid.idpcentral.org/submit
Intrinsic disorder (ID) in proteins is well-established in structural biology, with increasing evidence for its involvement in essential biological processes. As measuring dynamic ID behavior experimentally on a large scale remains difficult, scores of published ID predictors have tried to fill this gap. Unfortunately, their heterogeneity makes it difficult to compare performance, confounding biologists wanting to make an informed choice. To address this issue, the Critical Assessment of protein Intrinsic Disorder (CAID) benchmarks predictors for ID and binding regions as a community blind-test in a standardized computing environment. Here we present the CAID Prediction Portal, a web server executing all CAID methods on user-defined sequences. The server generates standardized output and facilitates comparison between methods, producing a consensus prediction highlighting high-confidence ID regions. The website contains extensive documentation explaining the meaning of different CAID statistics and providing a brief description of all methods. Predictor output is visualized in an interactive feature viewer and made available for download in a single table, with the option to recover previous sessions via a private dashboard. The CAID Prediction Portal is a valuable resource for researchers interested in studying ID in proteins.
Welcome to the EvryRNA Platform
https://evryrna.ibisc.univ-evry.fr/evryrna/
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EvryRNA platform is a web server providing various algorithms and bioinformatics tools developed in the laboratory IBISC of UEVE/Genopole, and dedicated to the prediction and the analysis of non-coding RNAs (ncRNAs). These RNAs are regulators of gene expression control and genome stability. They are involved in different biological processes, and some of them, including microRNAs, are known to be involved in many diseases such as cancer and neurodegenerative diseases. Their study provides insight into how living organisms function, including differentiation and cell proliferation, but also to consider new therapeutic approaches for genetic diseases and cancer.
EvryRNA includes many bioinformatics software for RNA secondary structure prediction and identification and prediction of ncRNAs including small RNAs (microRNAs, piARNs, etc.) in large-scale genomic sequences. These software correspond to different algorithms based on pattern matching and sequence algorithmic approaches, and machine learning approaches. They have the particularity, compared to the state of art, speed, enabling large-scale analyses, in addition to the effectiveness of predictions.
Contact:[email protected]
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Reactome
Visualize and interact with Reactome biological pathways
Merges pathway identifier mapping,
over-representation, and expression analysis
Designed to find pathways and network patterns related to cancer and other types of diseases
Information to browse the database and use its principal tools for data analysis
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GEPI
https://gepi.coling.uni-jena.de/
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GePI (Gene and Protein Interactions) is developed at JULIE Lab, the department for computational linguistics at the University of Jena.
This application scans the biomedical literature from PubMed and the PubMed Central open access subset for molecular interactions involving genes or gene products in the document texts. The interactions are structured into strictly binary relation pairs. Through GePI’s query interface, interactions that involve the input genes or proteins can be searched. The results are visualized in several ways and can be download in Excel format.
Refer to the Help page for detailed information on the usage of GePI.
The main developer is Erik Faessler. Dr. Sascha Schäuble and Prof. Dr. Udo Hahn supervise the project.
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EMPIAR: the Electron Microscopy Public Image Archive
 https://www.ebi.ac.uk/empiar/
EMPIAR, the Electron Microscopy Public Image Archive, is a public resource for raw images underpinning 3D cryo-EM maps and tomograms (themselves archived in EMDB). EMPIAR also accommodates 3D datasets obtained with volume EM techniques and soft and hard X-ray tomography.Â
As of 2023-09-01, EMPIAR contains 1426 entries, taking up 3.23 PB of storage.
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GENCODE: reference annotation for the human and mouse genomes in 2023
The goal of the GENCODE project is to identify and classify all gene features in the human and mouse genomes with high accuracy based on biological evidence, and to release these annotations for the benefit of biomedical research and genome interpretation.
GeneFriends: gene co-expression databases and tools for humans and model organisms
GeneFriends is a bioinformatic tool used to:
Assign putative functions to poorly annotated genes/features
Identify and rank new candidate genes/features related to a disease or biological process from a seed list of genes/features
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https://netbio.bgu.ac.il/ProAct/
The Process Activity (ProAct) webserver estimates the preferential activity of biological processes in tissues, cells, and other contexts. Given a differential expression matrix inferred from bulk or single cell transcriptomics, the webserver computes the preferential activity of biological processes per context.
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http://rnainformatics.org.cn/RiboUORF/
Ribo-uORF serves as a comprehensive functional resource for uORF analysis based on ribosome profiling (Ribo-seq) data. Ribo-uORF currently supports six species including human, mouse, rat, zebrafish, fly, and worm. Ribo-uORF includes 501,554 actively translated uORFs and 107,914 upstream translation initiation sites (uTIS), identified from 1,495 Ribo-seq and 77 quantitative translation initiation sequencing (QTI-seq) datasets, respectively. Moreover, mRNAbrowser was developed to visualize uORFs, cis-regulatory elements, genetic variations, eQTLs, GWAS, RNA modifications, and RNA editing, etc. Ribo-uORF provides a very intuitive web interface for users to intuitively and conveniently browse, search, and visualize data. Meanwhile, uORFscan and UTR5var were developed in Ribo-uORF to precisely identify uORFs and annotate the influence of genetic mutations on uORFs using user-uploaded datasets. Taken together, Ribo-uORF will likely greatly facilitate studies of uORFs and roles in mRNA translation initiation and the posttranscriptional control of gene expression.
b2bTools: online predictions for protein biophysical features and their conservation
B2bTools provide integrated protein sequence-based predictions via https://bio2byte.be/b2btools/. The aim of predictions is to identify the biophysical behaviour or features of proteins that are not readily captured by structural biology and/or molecular dynamics approaches. Upload of a FASTA file or text input of a sequence provides integrated predictions from DynaMine backbone and side-chain dynamics, conformational propensities, and derived EFoldMine early folding, DisoMine disorder, and Agmata beta-sheet aggregation. These predictions, several of which were previously not available online, capture ’emergent’ properties of proteins, i.e. the inherent biophysical propensities encoded in their sequence, rather than context-dependent behaviour (e.g. final folded state). Online visualisation is available as interactive plots, with brief explanations and tutorial pages included.
DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal
Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb (https://drugcomb.org/) is a web-based portal for the deposition and analysis of drug combination screening datasets. Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as on the functionality of the web server to improve the analysis, visualization and interpretation of drug combination screens. Here, we report significant updates of DrugComb, including: (i) manual curation and harmonization of more comprehensive drug combination and monotherapy screening data, not only for cancers but also for other diseases such as malaria and COVID-19; (ii) enhanced algorithms for assessing the sensitivity and synergy of drug combinations; (iii) network modelling tools to visualize the mechanisms of action of drugs or drug combinations for a given cancer sample and (iv) state-of-the-art machine learning models to predict drug combination sensitivity and synergy. These improvements have been provided with more user-friendly graphical interface and faster database infrastructure, which make DrugComb the most comprehensive web-based resources for the study of drug sensitivities for multiple diseases.
GEPIA2021: integrating multiple deconvolution-based analysis into GEPIA
http://gepia2021.cancer-pku.cn/
GEPIA (Gene Expression Profiling Interactive Analysis) webserver facilitates the widely used analyses based on the bulk gene expression datasets in the TCGA and the GTEx projects, providing the biologists and clinicians with a handy tool to perform comprehensive and complex data mining tasks. Recently, the deconvolution tools have led to revolutionary trends to resolve bulk RNA datasets at cell type-level resolution, interrogating the characteristics of different cell types in cancer and controlled cohorts became an important strategy to investigate the biological questions. Thus, GEPIA2021, a standalone extension of GEPIA, allowing users to perform multiple interactive analysis based on the deconvolution results, including cell type-level proportion comparison, correlation analysis, differential expression, and survival analysis. With GEPIA2021, experimental biologists could easily explore the large TCGA and GTEx datasets and validate their hypotheses in an enhanced resolution. GEPIA2021 is publicly accessible at http://gepia2021.cancer-pku.cn/.
ProLint: a web-based framework for the automated data analysis and visualization of lipid-protein interactions
The functional activity of membrane proteins is carried out in a complex lipid environment. Increasingly, it is becoming clear that lipids are an important player in regulating or generally modulating their activity. A routinely used method to gain insight into this interplay between lipids and proteins are Molecular Dynamics (MD) simulations, since they allow us to study interactions at atomic or near-atomic detail as a function of time. A major bottleneck, however, is analyzing and visualizing lipid-protein interactions, which, in practice, is a time-demanding task. ProLint (www.prolint.ca), is a webserver that completely automates analysis of MD generated files and visualization of lipid-protein interactions. Analysis is modular allowing users to select their preferred method, and visualization is entirely interactive through custom built applications that enable a detailed qualitative and quantitative exploration of lipid-protein interactions. ProLint also includes a database of published MD results that have been processed through the ProLint workflow and can be visualized by anyone regardless of their level of experience with MD. The automated analysis, feature-rich visualization, database integration, and open-source distribution with an easy to install process, will allow ProLint to become a routine workflow in lipid-protein interaction studies.
CNVxplorer: a web tool to assist clinical interpretation of CNVs in rare disease patients
Copy Number Variants (CNVs) are an important cause of rare diseases. Array-based Comparative Genomic Hybridization tests yield a similar to 12% diagnostic rate, with similar to 8% of patients presenting CNVs of unknown significance. CNVs interpretation is particularly challenging on genomic regions outside of those overlapping with previously reported structural variants or disease-associated genes. Recent studies showed that a more comprehensive evaluation of CNV features, leveraging both coding and non-coding impacts, can significantly improve diagnostic rates. However, currently available CNV interpretation tools are mostly gene-centric or provide only non-interactive annotations difficult to assess in the clinical practice. Here, we present CNVxplorer, a web server suited for the functional assessment of CNVs in a clinical diagnostic setting. CNVxplorer mines a comprehensive set of clinical, genomic, and epigenomic features associated with CNVs. It provides sequence constraint metrics, impact on regulatory elements and topologically associating domains, as well as expression patterns. Analyses offered cover (a) agreement with patient phenotypes; (b) visualizations of associations among genes, regulatory elements and transcription factors; (c) enrichment on functional and pathway annotations and (d) co-occurrence of terms across PubMed publications related to the query CNVs. A flexible evaluation workflow allows dynamic re-interrogation in clinical sessions. CNVxplorer is publicly available at http://cnvxplorer.com.
snpXplorer: a web application to explore human SNP-associations and annotate SNP-sets
Genetic association studies are frequently used to study the genetic basis of numerous human phenotypes. However, the rapid interrogation of how well a certain genomic region associates across traits as well as the interpretation of genetic associations is often complex and requires the integration of multiple sources of annotation, which involves advanced bioinformatic skills. We developed snpXplorer, an easy-to-use web-server application for exploring Single Nucleotide Polymorphisms (SNP) association statistics and to functionally annotate sets of SNPs. snpXplorer can superimpose association statistics from multiple studies, and displays regional information including SNP associations, structural variations, recombination rates, eQTL, linkage disequilibrium patterns, genes and gene-expressions per tissue. By overlaying multiple GWAS studies, snpXplorer can be used to compare levels of association across different traits, which may help the interpretation of variant consequences. Given a list of SNPs, snpXplorer can also be used to perform variant-to-gene mapping and gene-set enrichment analysis to identify molecular pathways that are overrepresented in the list of input SNPs. snpXplorer is freely available at https://snpxplorer.net. Source code, documentation, example files and tutorial videos are available within the Help section of snpXplorer and at https://github.com/TesiNicco/snpXplorer.
TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data
Uncovering how transcription factors regulate their targets at DNA, RNA and protein levels over time is critical to define gene regulatory networks (GRNs) and assign mechanisms in normal and diseased states. RNA-seq is a standard method measuring gene regulation using an established set of analysis stages. However, none of the currently available pipeline methods for interpreting ordered genomic data (in time or space) use time-series models to assign cause and effect relationships within GRNs, are adaptive to diverse experimental designs, or enable user interpretation through a web-based platform. Furthermore, methods integrating ordered RNA-seq data with protein-DNA binding data to distinguish direct from indirect interactions are urgently needed. We present TIMEOR (Trajectory Inference and Mechanism Exploration with Omics data in R), the first web-based and adaptive time-series multi-omics pipeline method which infers the relationship between gene regulatory events across time. TIMEOR addresses the critical need for methods to determine causal regulatory mechanism networks by leveraging time-series RNA-seq, motif analysis, protein-DNA binding data, and protein-protein interaction networks. TIMEOR’s user-catered approach helps non-coders generate new hypotheses and validate known mechanisms. We used TIMEOR to identify a novel link between insulin stimulation and the circadian rhythm cycle. TIMEOR is available at https://github.com/ashleymaeconard/TIMEOR.git and http://timeor.brown.edu.
Arena3D(web): interactive 3D visualization of multilayered networks
Efficient integration and visualization of heterogeneous biomedical information in a single view is a key challenge. In this study, we present Arena3D(web), the first, fully interactive and dependency-free, web application which allows the visualization of multilayered graphs in 3D space. With Arena3D(web), users can integrate multiple networks in a single view along with their intra- and inter-layer connections. For clearer and more informative views, users can choose between a plethora of layout algorithms and apply them on a set of selected layers either individually or in combination. Users can align networks and highlight node topological features, whereas each layer as well as the whole scene can be translated, rotated and scaled in 3D space. User-selected edge colors can be used to highlight important paths, while node positioning, coloring and resizing can be adjusted on-the-fly. In its current version, Arena3D(web) supports weighted and unweighted undirected graphs and is written in R, Shiny and JavaScript. We demonstrate the functionality of Arena3D(web) using two different use-case scenarios; one regarding drug repurposing for SARS-CoV-2 and one related to GPCR signaling pathways implicated in melanoma. Arena3D(web) is available at http://bib.fleming.gr:3838/Arena3D or http://bib.fleming.gr/Arena3D.
ProteoSign v2: a faster and evolved user-friendly online tool for statistical analyses of differential proteomics
http://bioinformatics.med. uoc.gr/ProteoSign
Bottom-up proteomics analyses have been proved over the last years to be a powerful tool in the characterization of the proteome and are crucial for understanding cellular and organism behaviour. Through differential proteomic analysis researchers can shed light on groups of proteins or individual proteins that play key roles in certain, normal or pathological conditions. However, several tools for the analysis of such complex datasets are powerful, but hard-to-use with steep learning curves. In addition, some other tools are easy to use, but are weak in terms of analytical power. Previously, we have introduced ProteoSign, a powerful, yet user-friendly open-source online platform for protein differential expression/abundance analysis designed with the end-proteomics user in mind. Part of Proteosign’s power stems from the utilization of the well-established Linear Models For Microarray Data (LIMMA) methodology. Here, we present a substantial upgrade of this computational resource, called ProteoSign v2, where we introduce major improvements, also based on user feedback. The new version offers more plot options, supports additional experimental designs, analyzes updated input datasets and performs a gene enrichment analysis of the differentially expressed proteins. We also introduce the deployment of the Docker technology and significantly increase the speed of a full analysis. ProteoSign v2 is available at http://bioinformatics.med. uoc.gr/ProteoSign.