SignaLink3: a multi-layered resource to uncover tissue-specific signaling networks.

Signaling networks represent the molecular mechanisms controlling a cell's response to various internal or external stimuli. Most currently available signaling databases contain only a part of the complex network of intertwining pathways, leaving out key interactions or processes. Hence, we have developed SignaLink3 (http://signalink.org/), a value-added knowledge-base that provides manually curated data on signaling pathways and integrated data from several types of databases (interaction, regulation, localisation, disease, etc.) for humans, and three major animal model organisms. SignaLink3 contains over 400 000 newly added human protein-protein interactions resulting in a total of 700 000 interactions for Homo sapiens, making it one of the largest integrated signaling network resources. Next to H. sapiens, SignaLink3 is the only current signaling network resource to provide regulatory information for the model species Caenorhabditis elegans and Danio rerio, and the largest resource for Drosophila melanogaster. Compared to previous versions, we have integrated gene expression data as well as subcellular localization of the interactors, therefore uniquely allowing tissue-, or compartment-specific pathway interaction analysis to create more accurate models. Data is freely available for download in widely used formats, including CSV, PSI-MI TAB or SQL.

HSF1Base: A Comprehensive Database of HSF1 (Heat Shock Factor 1) Target Genes.

HSF1 (heat shock factor 1) is an evolutionarily conserved master transcriptional regulator of the heat shock response (HSR) in eukaryotic cells. In response to high temperatures, HSF1 upregulates genes encoding molecular chaperones, also called heat shock proteins, which assist the refolding or degradation of damaged intracellular proteins. Accumulating evidence reveals however that HSF1 participates in several other physiological and pathological processes such as differentiation, immune response, and multidrug resistance, as well as in ageing, neurodegenerative demise, and cancer. To address how HSF1 controls these processes one should systematically analyze its target genes. Here we present a novel database called HSF1Base (hsf1base.org) that contains a nearly comprehensive list of HSF1 target genes identified so far. The list was obtained by manually curating publications on individual HSF1 targets and analyzing relevant high throughput transcriptomic and chromatin immunoprecipitation data derived from the literature and the Yeastract database. To support the biological relevance of HSF1 targets identified by high throughput methods, we performed an enrichment analysis of (potential) HSF1 targets across different tissues/cell types and organisms. We found that general HSF1 functions (targets are expressed in all tissues/cell types) are mostly related to cellular proteostasis. Furthermore, HSF1 targets that are conserved across various animal taxa operate mostly in cellular stress pathways (e.g., autophagy), chromatin remodeling, ribosome biogenesis, and ageing. Together, these data highlight diverse roles for HSF1, expanding far beyond the HSR.

ComPPI: a cellular compartment-specific database for protein-protein interaction network analysis.

Here we present ComPPI, a cellular compartment-specific database of proteins and their interactions enabling an extensive, compartmentalized protein-protein interaction network analysis (URL: http://ComPPI.LinkGroup.hu). ComPPI enables the user to filter biologically unlikely interactions, where the two interacting proteins have no common subcellular localizations and to predict novel properties, such as compartment-specific biological functions. ComPPI is an integrated database covering four species (S. cerevisiae, C. elegans, D. melanogaster and H. sapiens). The compilation of nine protein-protein interaction and eight subcellular localization data sets had four curation steps including a manually built, comprehensive hierarchical structure of >1600 subcellular localizations. ComPPI provides confidence scores for protein subcellular localizations and protein-protein interactions. ComPPI has user-friendly search options for individual proteins giving their subcellular localization, their interactions and the likelihood of their interactions considering the subcellular localization of their interacting partners. Download options of search results, whole-proteomes, organelle-specific interactomes and subcellular localization data are available on its website. Due to its novel features, ComPPI is useful for the analysis of experimental results in biochemistry and molecular biology, as well as for proteome-wide studies in bioinformatics and network science helping cellular biology, medicine and drug design.

AutophagyNet: high-resolution data source for the analysis of autophagy and its regulation.

Macroautophagy/autophagy is a highly-conserved catabolic procss eliminating dysfunctional cellular components and invading pathogens. Autophagy malfunction contributes to disorders such as cancer, neurodegenerative and inflammatory diseases. Understanding autophagy regulation in health and disease has been the focus of the last decades. We previously provided an integrated database for autophagy research, the Autophagy Regulatory Network (ARN). For the last eight years, this resource has been used by thousands of users. Here, we present a new and upgraded resource, AutophagyNet. It builds on the previous database but contains major improvements to address user feedback and novel needs due to the advancement in omics data availability. AutophagyNet contains updated interaction curation and integration of over 280,000 experimentally verified interactions between core autophagy proteins and their protein, transcriptional and post-transcriptional regulators as well as their potential upstream pathway connections. AutophagyNet provides annotations for each core protein about their role: 1) in different types of autophagy (mitophagy, xenophagy, etc.); 2) in distinct stages of autophagy (initiation, expansion, termination, etc.); 3) with subcellular and tissue-specific localization. These annotations can be used to filter the dataset, providing customizable download options tailored to the user's needs. The resource is available in various file formats (e.g. CSV, BioPAX and PSI-MI), and data can be analyzed and visualized directly in Cytoscape. The multi-layered regulation of autophagy can be analyzed by combining AutophagyNet with tissue- or cell type-specific (multi-)omics datasets (e.g. transcriptomic or proteomic data). The resource is publicly accessible at http://autophagynet.org.Abbreviations: ARN: Autophagy Regulatory Network; ATG: autophagy related; BCR: B cell receptor pathway; BECN1: beclin 1; GABARAP: GABA type A receptor-associated protein; IIP: innate immune pathway; LIR: LC3-interacting region; lncRNA: long non-coding RNA; MAP1LC3B: microtubule associated protein 1 light chain 3 beta; miRNA: microRNA; NHR: nuclear hormone receptor; PTM: post-translational modification; RTK: receptor tyrosine kinase; TCR: T cell receptor; TLR: toll like receptor.

TFLink: an integrated gateway to access transcription factor-target gene interactions for multiple species.

Analysis of transcriptional regulatory interactions and their comparisons across multiple species are crucial for progress in various fields in biology, from functional genomics to the evolution of signal transduction pathways. However, despite the rapidly growing body of data on regulatory interactions in several eukaryotes, no databases exist to provide curated high-quality information on transcription factor-target gene interactions for multiple species. Here, we address this gap by introducing the TFLink gateway, which uniquely provides experimentally explored and highly accurate information on transcription factor-target gene interactions (∼12 million), nucleotide sequences and genomic locations of transcription factor binding sites (∼9 million) for human and six model organisms: mouse, rat, zebrafish, fruit fly, worm and yeast by integrating 10 resources. TFLink provides user-friendly access to data on transcription factor-target gene interactions, interactive network visualizations and transcription factor binding sites, with cross-links to several other databases. Besides containing accurate information on transcription factors, with a clear labelling of the type/volume of the experiments (small-scale or high-throughput), the source database and the original publications, TFLink also provides a wealth of standardized regulatory data available for download in multiple formats. The database offers easy access to high-quality data for wet-lab researchers, supplies data for gene set enrichment analyses and facilitates systems biology and comparative gene regulation studies. Database URL https://tflink.net/.

Multilayered Networks of SalmoNet2 Enable Strain Comparisons of the Salmonella Genus on a Molecular Level.

Serovars of the genus Salmonella primarily evolved as gastrointestinal pathogens in a wide range of hosts. Some serotypes later evolved further, adopting a more invasive lifestyle in a narrower host range associated with systemic infections. A system-level knowledge of these pathogens could identify the complex adaptations associated with the evolution of serovars with distinct pathogenicity, host range, and risk to human health. This promises to aid the design of interventions and serve as a knowledge base in the Salmonella research community. Here, we present SalmoNet2, a major update to SalmoNet1, the first multilayered interaction resource for Salmonella strains, containing protein-protein, transcriptional regulatory, and enzyme-enzyme interactions. The new version extends the number of Salmonella networks from 11 to 20. We now include a strain from the second species in the Salmonella genus, a strain from the Salmonella enterica subspecies arizonae and additional strains of importance from the subspecies enterica, including S. Typhimurium strain D23580, an epidemic multidrug-resistant strain associated with invasive nontyphoidal salmonellosis (iNTS). The database now uses strain specific metabolic models instead of a generalized model to highlight differences between strains. The update has increased the coverage of high-quality protein-protein interactions, and enhanced interoperability with other computational resources by adopting standardized formats. The resource website has been updated with tutorials to help researchers analyze their Salmonella data using molecular interaction networks from SalmoNet2. SalmoNet2 is accessible at http://salmonet.org/. IMPORTANCE Multilayered network databases collate interaction information from multiple sources, and are powerful both as a knowledge base and subject of analysis. Here, we present SalmoNet2, an integrated network resource containing protein-protein, transcriptional regulatory, and metabolic interactions for 20 Salmonella strains. Key improvements to the update include expanding the number of strains, strain-specific metabolic networks, an increase in high-quality protein-protein interactions, community standard computational formats to help interoperability, and online tutorials to help users analyze their data using SalmoNet2.

A systems genomics approach to uncover patient-specific pathogenic pathways and proteins in ulcerative colitis.

We describe a precision medicine workflow, the integrated single nucleotide polymorphism network platform (iSNP), designed to determine the mechanisms by which SNPs affect cellular regulatory networks, and how SNP co-occurrences contribute to disease pathogenesis in ulcerative colitis (UC). Using SNP profiles of 378 UC patients we map the regulatory effects of the SNPs to a human signalling network containing protein-protein, miRNA-mRNA and transcription factor binding interactions. With unsupervised clustering algorithms we group these patient-specific networks into four distinct clusters driven by PRKCB, HLA, SNAI1/CEBPB/PTPN1 and VEGFA/XPO5/POLH hubs. The pathway analysis identifies calcium homeostasis, wound healing and cell motility as key processes in UC pathogenesis. Using transcriptomic data from an independent patient cohort, with three complementary validation approaches focusing on the SNP-affected genes, the patient specific modules and affected functions, we confirm the regulatory impact of non-coding SNPs. iSNP identified regulatory effects for disease-associated non-coding SNPs, and by predicting the patient-specific pathogenic processes, we propose a systems-level way to stratify patients.

Uniformly curated signaling pathways reveal tissue-specific cross-talks and support drug target discovery.

MOTIVATION: Signaling pathways control a large variety of cellular processes. However, currently, even within the same database signaling pathways are often curated at different levels of detail. This makes comparative and cross-talk analyses difficult. RESULTS: We present SignaLink, a database containing eight major signaling pathways from Caenorhabditis elegans, Drosophila melanogaster and humans. Based on 170 review and approximately 800 research articles, we have compiled pathways with semi-automatic searches and uniform, well-documented curation rules. We found that in humans any two of the eight pathways can cross-talk. We quantified the possible tissue- and cancer-specific activity of cross-talks and found pathway-specific expression profiles. In addition, we identified 327 proteins relevant for drug target discovery. CONCLUSIONS: We provide a novel resource for comparative and cross-talk analyses of signaling pathways. The identified multi-pathway and tissue-specific cross-talks contribute to the understanding of the signaling complexity in health and disease, and underscore its importance in network-based drug target selection. AVAILABILITY: http://SignaLink.org.

Sherlock: an open-source data platform to store, analyze and integrate Big Data for computational biologists.

In the era of Big Data, data collection underpins biological research more than ever before. In many cases, this can be as time-consuming as the analysis itself. It requires downloading multiple public databases with various data structures, and in general, spending days preparing the data before answering any biological questions. Here, we introduce Sherlock, an open-source, cloud-based big data platform ( https://earlham-sherlock.github.io/) to solve this problem. Sherlock provides a gap-filling way for computational biologists to store, convert, query, share and generate biology data while ultimately streamlining bioinformatics data management. The Sherlock platform offers a simple interface to leverage big data technologies, such as Docker and PrestoDB. Sherlock is designed to enable users to analyze, process, query and extract information from extremely complex and large data sets. Furthermore, Sherlock can handle different structured data (interaction, localization, or genomic sequence) from several sources and convert them to a common optimized storage format, for example, the Optimized Row Columnar (ORC). This format facilitates Sherlock's ability to quickly and efficiently execute distributed analytical queries on extremely large data files and share datasets between teams. The Sherlock platform is freely available on GitHub, and contains specific loader scripts for structured data sources of genomics, interaction and expression databases. With these loader scripts, users can easily and quickly create and work with specific file formats, such as JavaScript Object Notation (JSON) or ORC. For computational biology and large-scale bioinformatics projects, Sherlock provides an open-source platform empowering data management, analytics, integration and collaboration through modern big data technologies.

SignaLink: Multilayered Regulatory Networks.

Biological networks are graphs used to represent the inner workings of a biological system. Networks describe the relationships of the elements of biological systems using edges and nodes. However, the resulting representation of the system can sometimes be too simplistic to usefully model reality. By combining several different interaction types within one larger multilayered biological network, tools such as SignaLink provide a more nuanced view than those relying on single-layer networks (where edges only describe one kind of interaction). Multilayered networks display connections between multiple networks (i.e., protein-protein interactions and their transcriptional and posttranscriptional regulators), each one of them describing a specific set of connections. Multilayered networks also allow us to depict cross talk between cellular systems, which is a more realistic way of describing molecular interactions. They can be used to collate networks from different sources into one multilayered structure, which makes them useful as an analytic tool as well.