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  • 1
    Online Resource
    Online Resource
    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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  • 2
    Online Resource
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    Drugmonizome and Drugmonizome-ML: integration and abstraction of small molecule attributes for drug enrichment analysis and machine learning
    Oxford University Press (OUP) ; 2021
    In:  Database Vol. 2021 ( 2021-03-31)
    Details
    In: Database, Oxford University Press (OUP), Vol. 2021 ( 2021-03-31)
    Abstract: Understanding the underlying molecular and structural similarities between seemingly heterogeneous sets of drugs can aid in identifying drug repurposing opportunities and assist in the discovery of novel properties of preclinical small molecules. A wealth of information about drug and small molecule structure, targets, indications and side effects; induced gene expression signatures; and other attributes are publicly available through web-based tools, databases and repositories. By processing, abstracting and aggregating information from these resources into drug set libraries, knowledge about novel properties of drugs and small molecules can be systematically imputed with machine learning. In addition, drug set libraries can be used as the underlying database for drug set enrichment analysis. Here, we present Drugmonizome, a database with a search engine for querying annotated sets of drugs and small molecules for performing drug set enrichment analysis. Utilizing the data within Drugmonizome, we also developed Drugmonizome-ML. Drugmonizome-ML enables users to construct customized machine learning pipelines using the drug set libraries from Drugmonizome. To demonstrate the utility of Drugmonizome, drug sets from 12 independent SARS-CoV-2 in vitro screens were subjected to consensus enrichment analysis. Despite the low overlap among these 12 independent in vitro screens, we identified common biological processes critical for blocking viral replication. To demonstrate Drugmonizome-ML, we constructed a machine learning pipeline to predict whether approved and preclinical drugs may induce peripheral neuropathy as a potential side effect. Overall, the Drugmonizome and Drugmonizome-ML resources provide rich and diverse knowledge about drugs and small molecules for direct systems pharmacology applications. Database URL: https://maayanlab.cloud/drugmonizome/.
    Type of Medium: Online Resource
    ISSN: 1758-0463
    URL: Article
    DOI: 10.1093/database/baab017
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2496706-3
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  • 3
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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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  • 4
    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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  • 5
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    Supplemental Information 1: Supplemental Figures.
    PeerJ ; 2023
    In:  PeerJ Vol. 11 ( 2023-02-27), p. e14927-
    Details
    In: PeerJ, PeerJ, Vol. 11 ( 2023-02-27), p. e14927-
    Abstract: Gene-gene co-expression correlations measured by mRNA-sequencing (RNA-seq) can be used to predict gene annotations based on the co-variance structure within these data. In our prior work, we showed that uniformly aligned RNA-seq co-expression data from thousands of diverse studies is highly predictive of both gene annotations and protein-protein interactions. However, the performance of the predictions varies depending on whether the gene annotations and interactions are cell type and tissue specific or agnostic. Tissue and cell type-specific gene-gene co-expression data can be useful for making more accurate predictions because many genes perform their functions in unique ways in different cellular contexts. However, identifying the optimal tissues and cell types to partition the global gene-gene co-expression matrix is challenging. Results Here we introduce and validate an approach called PRediction of gene Insights from Stratified Mammalian gene co-EXPression (PrismEXP) for improved gene annotation predictions based on RNA-seq gene-gene co-expression data. Using uniformly aligned data from ARCHS4, we apply PrismEXP to predict a wide variety of gene annotations including pathway membership, Gene Ontology terms, as well as human and mouse phenotypes. Predictions made with PrismEXP outperform predictions made with the global cross-tissue co-expression correlation matrix approach on all tested domains, and training using one annotation domain can be used to predict annotations in other domains. Conclusions By demonstrating the utility of PrismEXP predictions in multiple use cases we show how PrismEXP can be used to enhance unsupervised machine learning methods to better understand the roles of understudied genes and proteins. To make PrismEXP accessible, it is provided via a user-friendly web interface, a Python package, and an Appyter. AVAILABILITY. The PrismEXP web-based application, with pre-computed PrismEXP predictions, is available from: https://maayanlab.cloud/prismexp ; PrismEXP is also available as an Appyter: https://appyters.maayanlab.cloud/PrismEXP/ ; and as Python package: https://github.com/maayanlab/prismexp .
    Type of Medium: Online Resource
    ISSN: 2167-8359
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.7717/peerj.14927
    DOI: 10.7717/peerj.14927/fig-1
    DOI: 10.7717/peerj.14927/fig-2
    DOI: 10.7717/peerj.14927/fig-3
    DOI: 10.7717/peerj.14927/fig-4
    DOI: 10.7717/peerj.14927/fig-5
    DOI: 10.7717/peerj.14927/table-1
    DOI: 10.7717/peerj.14927/supp-1
    Language: English
    Publisher: PeerJ
    Publication Date: 2023
    detail.hit.zdb_id: 2703241-3
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  • 6
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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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  • 7
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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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