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  • 1
    Online Resource
    Online Resource
    Getting Started with LINCS Datasets and Tools
    Wiley ; 2022
    In:  Current Protocols Vol. 2, No. 7 ( 2022-07)
    Details
    In: Current Protocols, Wiley, Vol. 2, No. 7 ( 2022-07)
    Abstract: The Library of Integrated Network‐based Cellular Signatures (LINCS) was an NIH Common Fund program that aimed to expand our knowledge about human cellular responses to chemical, genetic, and microenvironment perturbations. Responses to perturbations were measured by transcriptomics, proteomics, cellular imaging, and other high content assays. The second phase of the LINCS program, which lasted 7 years, involved the engagement of six data and signature generation centers (DSGCs) and one data coordination and integration center (DCIC). The DSGCs and the DCIC developed several digital resources, including tools, databases, and workflows that aim to facilitate the use of the LINCS data and integrate this data with other publicly available data. The digital resources developed by the DSGCs and the DCIC can be used to gain new biological and pharmacological insights that can lead to the development of novel therapeutics. This protocol provides step‐by‐step instructions for processing the LINCS data into signatures, and utilizing the digital resources developed by the LINCS consortia for hypothesis generation and knowledge discovery. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1 : Navigating L1000 tools and data in CLUE.io Basic Protocol 2 : Computing signatures from the L1000 data with the CD method Basic Protocol 3 : Analyzing lists of differentially expressed genes and querying them against the L1000 data with BioJupies and the Bulk RNA‐seq Appyter Basic Protocol 4 : Utilizing the L1000FWD resource for drug discovery Basic Protocol 5 : KINOMEscan and the KINOMEscan Appyter Basic Protocol 6 : LINCS P100 and GCP Proteomics Assays Basic Protocol 7 : The LINCS Joint Project (LJP) Basic Protocol 8 : The LINCS Data Portals and SigCom LINCS Basic Protocol 9 : Creating and analyzing signatures with iLINCS
    Type of Medium: Online Resource
    ISSN: 2691-1299 , 2691-1299
    URL: Issue
    URL: Article
    DOI: 10.1002/cpz1.v2.7
    DOI: 10.1002/cpz1.487
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 3059383-9
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  • 2
    Online Resource
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    lncHUB2: aggregated and inferred knowledge about human and mouse lncRNAs
    Oxford University Press (OUP) ; 2023
    In:  Database Vol. 2023 ( 2023-03-04)
    Details
    In: Database, Oxford University Press (OUP), Vol. 2023 ( 2023-03-04)
    Abstract: Long non-coding ribonucleic acids (lncRNAs) account for the largest group of non-coding RNAs. However, knowledge about their function and regulation is limited. lncHUB2 is a web server database that provides known and inferred knowledge about the function of 18 705 human and 11 274 mouse lncRNAs. lncHUB2 produces reports that contain the secondary structure fold of the lncRNA, related publications, the most correlated coding genes, the most correlated lncRNAs, a network that visualizes the most correlated genes, predicted mouse phenotypes, predicted membership in biological processes and pathways, predicted upstream transcription factor regulators, and predicted disease associations. In addition, the reports include subcellular localization information; expression across tissues, cell types, and cell lines, and predicted small molecules and CRISPR knockout (CRISPR-KO) genes prioritized based on their likelihood to up- or downregulate the expression of the lncRNA. Overall, lncHUB2 is a database with rich information about human and mouse lncRNAs and as such it can facilitate hypothesis generation for many future studies. The lncHUB2 database is available at https://maayanlab.cloud/lncHUB2. Database URL: https://maayanlab.cloud/lncHUB2
    Type of Medium: Online Resource
    ISSN: 1758-0463
    URL: Article
    DOI: 10.1093/database/baad009
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 2496706-3
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  • 3
    Online Resource
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    Appyters: Turning Jupyter Notebooks into data-driven web apps
    Elsevier BV ; 2021
    In:  Patterns Vol. 2, No. 3 ( 2021-03), p. 100213-
    Details
    In: Patterns, Elsevier BV, Vol. 2, No. 3 ( 2021-03), p. 100213-
    Type of Medium: Online Resource
    ISSN: 2666-3899
    URL: Article
    DOI: 10.1016/j.patter.2021.100213
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2021
    detail.hit.zdb_id: 3019416-7
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  • 4
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    Interoperable RNA-Seq analysis in the cloud
    Elsevier BV ; 2020
    In:  Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms Vol. 1863, No. 6 ( 2020-06), p. 194521-
    Details
    In: Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms, Elsevier BV, Vol. 1863, No. 6 ( 2020-06), p. 194521-
    Type of Medium: Online Resource
    ISSN: 1874-9399
    URL: Article
    DOI: 10.1016/j.bbagrm.2020.194521
    RVK:
    WA 15000
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2020
    detail.hit.zdb_id: 2406725-8
    SSG: 12
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  • 5
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    DrugShot: querying biomedical search terms to retrieve prioritized lists of small molecules
    Springer Science and Business Media LLC ; 2022
    In:  BMC Bioinformatics Vol. 23, No. 1 ( 2022-12)
    Details
    In: BMC Bioinformatics, Springer Science and Business Media LLC, Vol. 23, No. 1 ( 2022-12)
    Abstract: PubMed contains millions of abstracts that co-mention terms that describe drugs with other biomedical terms such as genes or diseases. Unique opportunities exist for leveraging these co-mentions by integrating them with other drug-drug similarity resources such as the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 signatures to develop novel hypotheses. Results DrugShot is a web-based server application and an Appyter that enables users to enter any biomedical search term into a simple input form to receive ranked lists of drugs and other small molecules based on their relevance to the search term. To produce ranked lists of small molecules, DrugShot cross-references returned PubMed identifiers (PMIDs) with DrugRIF or AutoRIF, which are curated resources of drug-PMID associations, to produce an associated small molecule list where each small molecule is ranked according to total co-mentions with the search term from shared PubMed IDs. Additionally, using two types of drug-drug similarity matrices, lists of small molecules are predicted to be associated with the search term. Such predictions are based on literature co-mentions and signature similarity from LINCS L1000 drug-induced gene expression profiles. Conclusions DrugShot prioritizes drugs and small molecules associated with biomedical search terms. In addition to listing known associations, DrugShot predicts additional drugs and small molecules related to any search term. Hence, DrugShot can be used to prioritize drugs and preclinical compounds for drug repurposing and suggest indications and adverse events for preclinical compounds. DrugShot is freely and openly available at: https://maayanlab.cloud/drugshot and https://appyters.maayanlab.cloud/#/DrugShot .
    Type of Medium: Online Resource
    ISSN: 1471-2105
    URL: Article
    DOI: 10.1186/s12859-022-04590-5
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2041484-5
    SSG: 12
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  • 6
    Online Resource
    Online Resource
    blitzGSEA: efficient computation of gene set enrichment analysis through gamma distribution approximation
    Oxford University Press (OUP) ; 2022
    In:  Bioinformatics Vol. 38, No. 8 ( 2022-04-12), p. 2356-2357
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 38, No. 8 ( 2022-04-12), p. 2356-2357
    Abstract: The identification of pathways and biological processes from differential gene expression is central for interpretation of data collected by transcriptomics assays. Gene set enrichment analysis (GSEA) is the most commonly used algorithm to calculate the significance of the relevancy of an annotated gene set with a differential expression signature. To compute significance, GSEA implements permutation tests which are slow and inaccurate for comparing many differential expression signatures to thousands of annotated gene sets. Results Here, we present blitzGSEA, an algorithm that is based on the same running sum statistic as GSEA, but instead of performing permutations, blitzGSEA approximates the enrichment score probabilities based on Gamma distributions. blitzGSEA achieves significant improvement in performance compared with prior GSEA implementations, while approximating small P-values more accurately. Availability and implementation The data, a python package, together with all source code, and a detailed user guide are available from GitHub at: https://github.com/MaayanLab/blitzgsea. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btac076
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 7
    Online Resource
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    D2H2: diabetes data and hypothesis hub
    Oxford University Press (OUP) ; 2023
    In:  Bioinformatics Advances Vol. 3, No. 1 ( 2023-01-05)
    Details
    In: Bioinformatics Advances, Oxford University Press (OUP), Vol. 3, No. 1 ( 2023-01-05)
    Abstract: There is a rapid growth in the production of omics datasets collected by the diabetes research community. However, such published data are underutilized for knowledge discovery. To make bioinformatics tools and published omics datasets from the diabetes field more accessible to biomedical researchers, we developed the Diabetes Data and Hypothesis Hub (D2H2). Results D2H2 contains hundreds of high-quality curated transcriptomics datasets relevant to diabetes, accessible via a user-friendly web-based portal. The collected and processed datasets are curated from the Gene Expression Omnibus (GEO). Each curated study has a dedicated page that provides data visualization, differential gene expression analysis, and single-gene queries. To enable the investigation of these curated datasets and to provide easy access to bioinformatics tools that serve gene and gene set-related knowledge, we developed the D2H2 chatbot. Utilizing GPT, we prompt users to enter free text about their data analysis needs. Parsing the user prompt, together with specifying information about all D2H2 available tools and workflows, we answer user queries by invoking the most relevant tools via the tools’ API. D2H2 also has a hypotheses generation module where gene sets are randomly selected from the bulk RNA-seq precomputed signatures. We then find highly overlapping gene sets extracted from publications listed in PubMed Central with abstract dissimilarity. With the help of GPT, we speculate about a possible explanation of the high overlap between the gene sets. Overall, D2H2 is a platform that provides a suite of bioinformatics tools and curated transcriptomics datasets for hypothesis generation. Availability and implementation D2H2 is available at: https://d2h2.maayanlab.cloud/ and the source code is available from GitHub at https://github.com/MaayanLab/D2H2-site under the CC BY-NC 4.0 license.
    Type of Medium: Online Resource
    ISSN: 2635-0041
    URL: Article
    DOI: 10.1093/bioadv/vbad178
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 3076075-6
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  • 8
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    SigCom LINCS: data and metadata search engine for a million gene expression signatures
    Oxford University Press (OUP) ; 2022
    In:  Nucleic Acids Research Vol. 50, No. W1 ( 2022-07-05), p. W697-W709
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 50, No. W1 ( 2022-07-05), p. W697-W709
    Abstract: Millions of transcriptome samples were generated by the Library of Integrated Network-based Cellular Signatures (LINCS) program. When these data are processed into searchable signatures along with signatures extracted from Genotype-Tissue Expression (GTEx) and Gene Expression Omnibus (GEO), connections between drugs, genes, pathways and diseases can be illuminated. SigCom LINCS is a webserver that serves over a million gene expression signatures processed, analyzed, and visualized from LINCS, GTEx, and GEO. SigCom LINCS is built with Signature Commons, a cloud-agnostic skeleton Data Commons with a focus on serving searchable signatures. SigCom LINCS provides a rapid signature similarity search for mimickers and reversers given sets of up and down genes, a gene set, a single gene, or any search term. Additionally, users of SigCom LINCS can perform a metadata search to find and analyze subsets of signatures and find information about genes and drugs. SigCom LINCS is findable, accessible, interoperable, and reusable (FAIR) with metadata linked to standard ontologies and vocabularies. In addition, all the data and signatures within SigCom LINCS are available via a well-documented API. In summary, SigCom LINCS, available at https://maayanlab.cloud/sigcom-lincs, is a rich webserver resource for accelerating drug and target discovery in systems pharmacology.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkac328
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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  • 9
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    Online Resource
    KEA3: improved kinase enrichment analysis via data integration
    Oxford University Press (OUP) ; 2021
    In:  Nucleic Acids Research Vol. 49, No. W1 ( 2021-07-02), p. W304-W316
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 49, No. W1 ( 2021-07-02), p. W304-W316
    Abstract: Phosphoproteomics and proteomics experiments capture a global snapshot of the cellular signaling network, but these methods do not directly measure kinase state. Kinase Enrichment Analysis 3 (KEA3) is a webserver application that infers overrepresentation of upstream kinases whose putative substrates are in a user-inputted list of proteins. KEA3 can be applied to analyze data from phosphoproteomics and proteomics studies to predict the upstream kinases responsible for observed differential phosphorylations. The KEA3 background database contains measured and predicted kinase-substrate interactions (KSI), kinase-protein interactions (KPI), and interactions supported by co-expression and co-occurrence data. To benchmark the performance of KEA3, we examined whether KEA3 can predict the perturbed kinase from single-kinase perturbation followed by gene expression experiments, and phosphoproteomics data collected from kinase-targeting small molecules. We show that integrating KSIs and KPIs across data sources to produce a composite ranking improves the recovery of the expected kinase. The KEA3 webserver is available at https://maayanlab.cloud/kea3.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkab359
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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  • 10
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    Gene Set Knowledge Discovery with Enrichr
    Wiley ; 2021
    In:  Current Protocols Vol. 1, No. 3 ( 2021-03)
    Details
    In: Current Protocols, Wiley, Vol. 1, No. 3 ( 2021-03)
    Abstract: Profiling samples from patients, tissues, and cells with genomics, transcriptomics, epigenomics, proteomics, and metabolomics ultimately produces lists of genes and proteins that need to be further analyzed and integrated in the context of known biology. Enrichr (Chen et al., 2013; Kuleshov et al., 2016) is a gene set search engine that enables the querying of hundreds of thousands of annotated gene sets. Enrichr uniquely integrates knowledge from many high‐profile projects to provide synthesized information about mammalian genes and gene sets. The platform provides various methods to compute gene set enrichment, and the results are visualized in several interactive ways. This protocol provides a summary of the key features of Enrichr, which include using Enrichr programmatically and embedding an Enrichr button on any website. © 2021 Wiley Periodicals LLC. Basic Protocol 1 : Analyzing lists of differentially expressed genes from transcriptomics, proteomics and phosphoproteomics, GWAS studies, or other experimental studies Basic Protocol 2 : Searching Enrichr by a single gene or key search term Basic Protocol 3 : Preparing raw or processed RNA‐seq data through BioJupies in preparation for Enrichr analysis Basic Protocol 4 : Analyzing gene sets for model organisms using modEnrichr Basic Protocol 5 : Using Enrichr in Geneshot Basic Protocol 6 : Using Enrichr in ARCHS4 Basic Protocol 7 : Using the enrichment analysis visualization Appyter to visualize Enrichr results Basic Protocol 8 : Using the Enrichr API Basic Protocol 9 : Adding an Enrichr button to a website
    Type of Medium: Online Resource
    ISSN: 2691-1299 , 2691-1299
    URL: Issue
    URL: Article
    DOI: 10.1002/cpz1.v1.3
    DOI: 10.1002/cpz1.90
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 3059383-9
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