| ✓ All Systems Operational |
SERVICES / SERVERS
| HALLEY | ||
| Our Halley service is a remote server for KorcsmarosLab use only. | ||
| Uptime: | ||
| Actual status of the service: | Operational | |
| SLK3WEBSERVER | ||
| Our SLK3 Webserver is a remote server for KorcsmarosLab use only. Service(s), which is running from this server: SLK3 |
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| Uptime: | ||
| Actual status of the service: | Operational | |
| WEBHOSTING | ||
| Our webhosting service is provided by 3 interconnected virtual machines located at the Earlham Institute in Norwich, UK. Service(s), which is running from this server cluster: ARN | NRF2OME |
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| Uptime: | ||
| Actual status of the service: | Operational | |
| SHERLOCK CLUSTER | ||
| Our Sherlock cluster is provided by 3 interconnected virtual machines located at the Earlham Institute in Norwich, UK. Service(s), which is running from this server cluster: Sherlock |
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| Uptime: | ||
| Actual status of the service: | Operational | |
| SHERLOCK | ||
| Sherlock is an open source data platform, developed in the Korcsmaros Group (Earlham Institute, Norwich, UK) to store, analyze and integrate bioinformatics data. With Sherlock: 1) you can store all datasets in a redundant, organized cloud storage, 2) convert all datasets to common, optimized file formats, 3) execute analytical queries on top of data files, 4) share datasets among different teams / projects, 5) generate operational datasets for certain services or collaborators. Sherlock is using standard big data technologies to store the biological data and to analyze it. The main concept can be seen in the following figure (don’t be confused, every word on it will be explained later). Sherlock follows industrial best practices in its architecture. Usually the main idea behind these scalable batch processing architectures is the separation of data storage and analytics, as you can see below. This allows you to scale the analytical power and the storage size independently from each other and even dynamically, if you are deploying Sherlock in the cloud. Sherlock is freely available on github. | ||
| Uptime: | ||
| Actual status of the service: | Operational | |
| DR. WATSON | ||
| Our Dr. Watson service is a Data Lake, where we are storing our different datafiles for our Sherlock Big Data platform. This data lake is operated by Digital Ocean (S3). In our Data Lake, we have 4 different zones/layers, to store data: 1) the raw zone, where we are storing the raw data, what was downloaded from the different freely available databases. 2) the landing zone, where we store our data in a compact and common json format. 3) the master zone, where we store our data in a common and really good quality formatted file format, called ORC (Optimized Row Columnar). 4) the project zone, where we store specific data for our different projects. Right now, in our Data Lake we have different datatypes: interaction data, expression data, sequence data, annotation data. | ||
| Actual status of the service: | Operational | |
| Databases/datasources in our Data Lake |
| Expression data |
| Bgee database | actual version: 05/04/2021 |
| Interaction data |
| BioPlex database | actual version: 27/04/2020 |
| Dorothea database | actual version: 27/01/2022 |
| HINT database | actual version: 08/09/2021 |
| HuRi database | actual version: 16/11/2020 |
| InBioMap database | actual version: 24/05/2019 |
| IntAct database | actual version: 27/01/2022 |
| IRefIndex database | actual version: 08/09/2021 |
| Mentha database | actual version: 24/01/2022 |
| Omnipath database | actual version: 27/01/2022 |
| String database | actual version: 09/09/2021 |
| Other datatypes |
| DBSnp | actual version: 26/06/2019 |
| Gene Ontology | actual version: 27/01/2021 |
| GO Annotations | actual version: 27/01/2021 |
| Human Genome | actual version: GRCh38.p13 |
| Uberon Gene Ontology | actual version: 13/04/2021 |
| UniProt ID Mapping Table | actual version: 04/2021 |
WEBSERVICES
ARN  |
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| ARN (Autophagy Regulation Network) is an integrated resource to analyze regulatory network of autophagy proteins. It has a lot of features: 1) A manually curated dataset of autophagy components and their interactions. 2) Integrated resource with known protein regulators of autophagy. 3) Contains possible transcriptional and post-transcriptional regulators (ie., transcription factors and miRNA) of autophagy and its protein regulators. 4) Links all autophagy component and regulators to major signaling pathways. 5) Predict novel regulators and interactions. 6) Can be downloaded in a user-specified content and format. | ||
| How to cite ARN: | ||
| Autophagy Regulatory Network – a systems-level bioinformatics resource for studying autophagy components and their regulation (2015). Türei D, Földvári-Nagy L, Fazekas D, Módos D, Kubisch J, Kadlecsik T, Demeter A, Lenti K, Csermely P, Vellai T, Korcsmáros T. | Autophagy, 11(1):155-165. |
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| Actual status of the service: | Operational | |
| NRF2OME |
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| NRF2ome is an integrated regulatory network of Nuclear Factor (erythroid-derived 2)-like 2 (NRF2). It has many features as well: 1) A manually curated dataset of regulatory connections of NRF2 Integrated resource with known protein regulators of NRF2. 2) Contains possible transcriptional and post-transcriptional regulators (ie., transcription factors and miRNA) of NRF2 and its protein regulators. 3) Links NRF2 and regulators to major signaling pathways. 4) Predict novel regulators and interactions. 5) Can be downloaded in a user-specified content and format. | ||
| How to cite NRF2OME: | ||
| NRF2-ome, an integrated web resource to discover protein interaction and regulatory networks of NRF2. Türei D, Papp D, Fazekas D, Földvári-Nagy L, Módos D, Lenti K, Csermely P, Korcsmáros T. (2013) | FEBS Letters 586, 13, 1795–1802. |
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| -The NRF2-related interactome and regulome contain multifunctional proteins and fine-tuned autoregulatory loops. (2012) Papp D, Lenti K, Módos D, Fazekas D, Dúl Z, Türei D, Földvári-Nagy L, Nussinov R, Csermely P, Korcsmáros T. | Oxidative Medicine and Cellular Longevity, 2013:737591 |
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| Actual status of the service: | Operational | |
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| SLK3 |
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| SignaLink 3 is an integrated resource to analyze signaling pathway cross-talks, transcription factors, miRNAs and regulatory enzymes. Main features: 1) A signaling network resource with known and predicted information for human and model organisms. 2) Manually curated dataset of major signaling pathways - including curated data from resources such as ACSN, InnateDB, Reactome and Signor. 3) Extends pathways with integrated regulatory resources to contain pathway-specific transcription factors, miRNA, scaffolds and post translational modifying enzymes. 4) Proteins are classified by pathway position (core/non-core) and function (ligand, receptor, mediator, etc.). 5) Signaling interactions are directed and labeled with PubMed IDs of the publications of experimental evidence. 6) A multi-layered network structure allows the selection of user-specific details. 7) Allows filtering based on tissue or sub-cellular localization. 8) Supporting downloads in csv, biopax (level 3), psimi tab, sbml or cytoscape formats. | ||
| How to cite SLK3: | ||
| SignaLink 2.0 - A signaling pathway resource with multi-layered regulatory networks Fazekas D*, Koltai M*, Türei D*, Módos D, Pálfy M, Dúl Z, Zsákai L, Szalay-Bekő M, Lenti K, Farkas I J, Vellai T, Csermely P, Korcsmáros T (* equal contributions) | BMC Systems Biology 2013, 7:7. |
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| Uniformly curated signaling pathways reveal tissue-specific cross-talks and support drug target discovery. Korcsmáros T *, Farkas I J *, Szalay M S, Rovó P, Fazekas D, Spiro Z, Böde C, Lenti K, Vellai T, Csermely P (* equal contributions) | Bioinformatics 26:2042-2050 (2010). |
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| Actual status of the service: | Operational | |