NIH CFDE Playbook Workflow Partnership
OverviewSpace: Independent Teams
SEEK ID: https://workflowhub.eu/projects/201
Public web page: https://playbook-workflow-builder.cloud/
Organisms: No Organisms specified
WorkflowHub PALs: No PALs for this Team
Team created: 12th Oct 2023
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Teams: NIH CFDE Playbook Workflow Partnership
Organizations: Mount Sinai
https://orcid.org/0000-0003-3471-7416
A space managed by WorkflowHub administrators for teams that don't want/need to manage their own space.
Teams: IBISBA Workflows, NMR Workflow, UNLOCK, NanoGalaxy, Galaxy Climate, PNDB, IMBforge, COVID-19 PubSeq: Public SARS-CoV-2 Sequence Resource, LBI-RUD, Nick-test-team, usegalaxy-eu, Italy-Covid-data-Portal, UX trial team, Integrated and Urban Plant Pathology Laboratory, SARS-CoV-2 Data Hubs, lmjxteam2, virAnnot pipeline, Ay Lab, iPC: individualizedPaediatricCure, Harkany Lab, MOLGENIS, EJPRD WP13 case-studies workflows, Common Workflow Language (CWL) community, Testing, SeBiMER, IAA-CSIC, MAB - ATGC, Probabilistic graphical models, GenX, Snakemake-Workflows, ODA, IPK BIT, CO2MICS Lab, FAME, CHU Limoges - UF9481 Bioinformatique / CNR Herpesvirus, Quadram Institute Bioscience - Bioinformatics, HecatombDevelopment, Institute of Human Genetics, Testing RO Crates, Test Team, Applied Computational Biology at IEG/HMGU, INFRAFRONTIER workflows, OME, TransBioNet, OpenEBench, Bioinformatics and Biostatistics (BIO2 ) Core, VIB Bioinformatics Core, CRC Cohort, ICAN, MustafaVoh, Single Cell Unit, CO-Graph, emo-bon, TestEMBL-EBIOntology, CINECA, Toxicology community, Pitagora-Network, Workflows Australia, Medizinisches Proteom-Center, Medical Bioinformatics, AGRF BIO, EU-Openscreen, X-omics, ELIXIR Belgium, URGI, Size Inc, GA-VirReport Team, The Boucher Lab, Air Quality Prediction, pyiron, CAPSID, Edinburgh Genomics, Defragmentation TS, NBIS, Phytoplankton Analysis, Seq4AMR, Workflow registry test, Read2Map, SKM3, ParslRNA-Seq: an efficient and scalable RNAseq analysis workflow for studies of differentiated gene expression, de.NBI Cloud, Meta-NanoSim, ILVO Plant Health, EMERGEN-BIOINFO, KircherLab, Apis-wings, BCCM_ULC, Dessimoz Lab, TRON gGmbH, GEMS at MLZ, Computational Science at HZDR, Big data in biomedicine, TRE-FX, MISTIC, Guigó lab, Statistical genetics, Delineating Regions-of-interest for Mass Spectrometry Imaging by Multimodally Corroborated Spatial Segmentation, OLCF-WES, Bioinformatics Unit @ CRG, Bioinformatics Innovation Lab, BSC-CES, ELIXIR Proteomics, Black Ochre Data Labs, Zavolan Lab, Metabolomics-Reproducibility, Team Cardio, NGFF Tools, Bioinformatics workflows for life science, Workflows for geographic science, Pacific-deep-sea-sponges-microbiome, CSFG, SNAKE, Katdetectr, INFRAFRONTIER GmbH, PerMedCoE, Euro-BioImaging, EOSC-Life WP3 OC Team, cross RI project, ANSES-Ploufragan, SANBI Pathogen Bioinformatics, Biodata Analysis Group, DeSci Labs, Erasmus MC - Viroscience Bioinformatics, ARA-dev, Mendel Centre for Plant Genomics and Proteomics, Metagenomic tools, WorkflowEng, Polygenic Score Catalog, bpm, scNTImpute, Systems Biotechnology laboratory, Cimorgh IT solutions, MLme: Machine Learning Made Easy, Hurwitz Lab, Dioscuri TDA, Scipion CNB, System Biotechnology laboratory, yPublish - Bioinfo tools, NIH CFDE Playbook Workflow Partnership, MMV-Lab, EMBL-CBA, EBP-Nor, Evaluation of Swin Transformer and knowledge transfer for denoising of super-resolution structured illumination microscopy data, Bioinformatics Laboratory for Genomics and Biodiversity (LBGB), multi-analysis dFC, CholGen, RNA group, Plant Genomes Pipelines in Galaxy, Pathogen Genomic Laboratory, Chemical Data Lab, JiangLab, Pangenome database project, HP2NET - Framework for construction of phylogenetic networks on High Performance Computing (HPC) environment, Center for Open Bioimage Analysis, Generalized Open-Source Workflows for Atomistic Molecular Dynamics Simulations of Viral Helicases, Historical DNA genome skimming, QCDIS, Peter Menzel's Team, NHM Clark group, ESRF Workflow System (Ewoks), Kalbe Bioinformatics, Nextflow4Metabolomics, GBCS, CEMCOF, Jackson Laboratory NGS-Ops, Schwartz Lab, BRAIN - Biomedical Research on Adult Intracranial Neoplasms, Cancer Therapeutics and Drug Safety, Deepdefense, Mid-Ohio Regional Planning Commission, MGSSB, Institute for Human Genetics and Genomic Medicine Aachen, FengTaoSMU, EGA, Plant-Food-Research-Open, KrauthammerLab, Geo Workflows, grassland pDT, FunGIALab, CRIM - Computer Research Institute of Montréal, Medvedeva Lab, Metagenlab, FAIR-EASE, Protein-protein and protein-nucleic acid binding site prediction research, Culhane Lab, IDUN - Drug Delivery and Sensing, Edge Computing DAG Task Scheduling Research Group, Stratum corneum nanotexture feature detection using deep learning and spatial analysis: a non-invasive tool for skin barrier assessment, COPO, Taudière group, ErasmusMC Clinical Bioinformatics, interTwin, fluid flow modeling, EnrichDO, WorkflowResearch, Application Security - Test Crypt4GH solutions, RenLabBioinformatics, Yongxin's team, PiFlow, HLee_SeoGroup, UFZ - Image Data Management and Processing Workflows, Korean Bioinformaticians, Into the deep, XChem, CPM, SocialGene, Research Data Management ICE-2, ObjectRecognition, LiDAR
Web page: Not specified
The workflow starts with selecting EH38E2924876 as the search term. Genomic position of provided unique regulatory element identifier was retrieved from CFDE Linked Data Hub[1]. A list of variants in the region of the regulatory element was retrieved from CFDE Linked Data Hub[1]. Variant/variant set associated allele specific epigenomic signatures were retrieved from CFDE LDH[5] based on Roadmap and ENTEx data[6], [4]. GTEx eQTL and sQTL evidence for the given variant(s) were retrieved from CFDE ...
Type: Playbook Workflow Builder Workflow
Creator: Playbook Partnership NIH CFDE
Submitter: Daniel Clarke
A file containing GEO Aging Signatures was first uploaded. The file containing GEO Aging Signatures was loaded as a gene signature. A file containing GTEx Aging Signatures was first uploaded. The file containing GTEx Aging Signatures was loaded as a gene signature. Significant genes were extracted from the GEO Aging Signatures. Significant genes were extracted from the GTEx Aging Signatures. Reversers and mimickers from over 1 million signatures were identified using SigCom LINCS[1]. Resolved ...
Type: Playbook Workflow Builder Workflow
Creator: Playbook Partnership NIH CFDE
Submitter: Daniel Clarke
A file was first uploaded. The file was parsed as a gene count matrix. Significantly over-expressed genes when compared to tissue expression in GTEx[1] were identified. RNA-seq-like LINCS L1000 Signatures[3] which mimick or reverse the the expression of IMP3 were visualized. Drugs which down-regulate the expression of IMP3 were identified from the RNA-seq-like LINCS L1000 Chemical Perturbagens[3]. Genes which down-regulate the expression of IMP3 were identified from the RNA-seq-like LINCS L1000 ...
Type: Playbook Workflow Builder Workflow
Creator: Playbook Partnership NIH CFDE
Submitter: Daniel Clarke
The workflow starts with selecting RPE as the search term. For the given gene ID (SYMBOL), StringDB PPI was extracted using their API[1]. For the Given StringDB PPI, the list of nodes (Gene Set) is generated. For the Given StringDB PPI, the list of nodes (GeneSet) is generated. Reversers and mimickers from over 1 million signatures were identified using SigCom LINCS[2]. The gene set was submitted to Enrichr[4]. The gene set was then searched in the Metabolomics Workbench[5] to identify relevant ...
Type: Playbook Workflow Builder Workflow
Creator: Playbook Partnership NIH CFDE
Submitter: Daniel Clarke
The workflow starts with a gene set created from Example gene set. CTD is applied which diffuses through all nodes in STRING[1] to identify nodes that are "guilty by association" and highly connected to the initial gene set of interest[2][3]. A list of Highly Connected Genes was obtained from the CTD output. A list of Guilty By Association Genes was obtained from the CTD output.
- Szklarczyk, D. et al. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic ...
Type: Playbook Workflow Builder Workflow
Creator: Playbook Partnership NIH CFDE
Submitter: Daniel Clarke
The workflow starts with selecting chr2:g.39417578C>G as the search term. The closest gene to the variant was found using MyVariant.info[1]. Gene expression in tumors for CDKL4 were queried from the Open Pediatric Cancer Atlas API[3]. Median expression of CDKL4 was obtained from the GTEx Portal[4] using the portal's API. To visualize the level of expression across tumor gene expression, a bar plot was created Fig..
- Lelong, S. et al. BioThings SDK: a toolkit for building high-performance ...
Type: Playbook Workflow Builder Workflow
Creator: Playbook Partnership NIH CFDE
Submitter: Daniel Clarke
The workflow starts with selecting KLF6 as the search term. RNA-seq-like LINCS L1000 Signatures[1] which mimick or reverse the the expression of KLF6 were visualized. Median expression of KLF6 was obtained from the GTEx Portal[6] using the portal's API. To visualize the scored tissues, a vertical bar plot was created Fig..
- Evangelista, J. E. et al. SigCom LINCS: data and metadata search engine for a million gene expression signatures. Nucleic Acids Research vol. 50 W697–W709 (2022). ...
Type: Playbook Workflow Builder Workflow
Creator: Playbook Partnership NIH CFDE
Submitter: Daniel Clarke
The workflow starts with selecting chr10:g.3823823G>A as the search term. The closest gene to the variant was found using MyVariant.info[1]. RNA-seq-like LINCS L1000 Signatures[3] which mimick or reverse the the expression of KLF6 were visualized. Median expression of KLF6 was obtained from the GTEx Portal[8] using the portal's API. To visualize the scored tissues, a vertical bar plot was created Fig..
- Lelong, S. et al. BioThings SDK: a toolkit for building high-performance data APIs in ...
Type: Playbook Workflow Builder Workflow
Creator: Playbook Partnership NIH CFDE
Submitter: Daniel Clarke
The workflow starts with selecting Autophagy as the search term. Gene sets with set labels containing Autophagy were queried from Enrichr[1]. Identified matching terms from the MGI Mammalian Phenotype Level 4 2019[2] library were assembled into a collection of gene sets. A GMT was extracted from the Enrichr results for MGI_Mammalian_Phenotype_Level_4_2019. All the identified gene sets were combined using the union set operation. Reversers and mimickers from over 1 million signatures were identified ...
Type: Playbook Workflow Builder Workflow
Creator: Playbook Partnership NIH CFDE
Submitter: Daniel Clarke
The workflow starts with selecting KLF4 as the search term. Gene sets with set labels containing KLF4 were queried from Enrichr[1]. Identified matching terms from the ENCODE TF ChIP-seq 2015[2] library were assembled into a collection of gene sets. A GMT was extracted from the Enrichr results for ENCODE_TF_ChIP-seq_2015. Identified matching terms from the ChEA 2022[4] library were assembled into a collection of gene sets. A GMT was extracted from the Enrichr results for ChEA_2022. Identified ...
Type: Playbook Workflow Builder Workflow
Creator: Playbook Partnership NIH CFDE
Submitter: Daniel Clarke
The workflow starts with selecting atrial fibrillation as the search term. The workflow starts with selecting Ibrutinib as the search term. Gene sets with set labels containing atrial fibrillation were queried from Enrichr[1]. Identified matching terms from the MGI Mammalian Phenotype Level 4 2021[2] library were assembled into a collection of gene sets. A GMT was extracted from the Enrichr results for MGI_Mammalian_Phenotype_Level_4_2021. A consensus gene set was created by only retaining genes ...
Type: Playbook Workflow Builder Workflow
Creator: Playbook Partnership NIH CFDE
Submitter: Daniel Clarke
The workflow starts with selecting Inflammation as the search term. The workflow starts with selecting Penicillin as the search term. The workflow starts with selecting Cortisol as the search term. Gene sets with set labels containing Inflammation were queried from Enrichr[1]. Identified matching terms from the GWAS Catalog 2019[2] library were assembled into a collection of gene sets. A GMT was extracted from the Enrichr results for GWAS_Catalog_2019. All the identified gene sets were combined ...
Type: Playbook Workflow Builder Workflow
Creator: Playbook Partnership NIH CFDE
Submitter: Daniel Clarke
The input to this workflow is a data matrix of gene expression that was collected from a pediatric patient tumor patient from the KidsFirst Common Fund program [1]. The RNA-seq samples are the columns of the matrix, and the rows are the raw expression gene count for all human coding genes (Table 1). This data matrix is fed into TargetRanger [2] to screen for targets which are highly expressed in the tumor but lowly expressed across most healthy human tissues based on gene expression data collected ...
