1
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Zhang X, Song B, Carlino MJ, Li G, Ferchen K, Chen M, Thompson EN, Kain BN, Schnell D, Thakkar K, Kouril M, Jin K, Hay SB, Sen S, Bernardicius D, Ma S, Bennett SN, Croteau J, Salvatori O, Lye MH, Gillen AE, Jordan CT, Singh H, Krause DS, Salomonis N, Grimes HL. An immunophenotype-coupled transcriptomic atlas of human hematopoietic progenitors. Nat Immunol 2024; 25:703-715. [PMID: 38514887 PMCID: PMC11003869 DOI: 10.1038/s41590-024-01782-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 02/07/2024] [Indexed: 03/23/2024]
Abstract
Analysis of the human hematopoietic progenitor compartment is being transformed by single-cell multimodal approaches. Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) enables coupled surface protein and transcriptome profiling, thereby revealing genomic programs underlying progenitor states. To perform CITE-seq systematically on primary human bone marrow cells, we used titrations with 266 CITE-seq antibodies (antibody-derived tags) and machine learning to optimize a panel of 132 antibodies. Multimodal analysis resolved >80 stem, progenitor, immune, stromal and transitional cells defined by distinctive surface markers and transcriptomes. This dataset enables flow cytometry solutions for in silico-predicted cell states and identifies dozens of cell surface markers consistently detected across donors spanning race and sex. Finally, aligning annotations from this atlas, we nominate normal marrow equivalents for acute myeloid leukemia stem cell populations that differ in clinical response. This atlas serves as an advanced digital resource for hematopoietic progenitor analyses in human health and disease.
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Affiliation(s)
- Xuan Zhang
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Baobao Song
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Immunology Graduate Program, University of Cincinnati, Cincinnati, OH, USA
| | - Maximillian J Carlino
- Yale Stem Cell Center, Yale School of Medicine, New Haven, CT, USA
- Department of Laboratory Medicine, Yale University, New Haven, CT, USA
| | - Guangyuan Li
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Kyle Ferchen
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Mi Chen
- Yale Stem Cell Center, Yale School of Medicine, New Haven, CT, USA
- Department of Laboratory Medicine, Yale University, New Haven, CT, USA
| | - Evrett N Thompson
- Yale Stem Cell Center, Yale School of Medicine, New Haven, CT, USA
- Department of Laboratory Medicine, Yale University, New Haven, CT, USA
- Department of Cell Biology, Yale School of Medicine, New Haven, CT, USA
| | - Bailee N Kain
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Dan Schnell
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Kairavee Thakkar
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Michal Kouril
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Kang Jin
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Stuart B Hay
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Sidharth Sen
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - David Bernardicius
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Siyuan Ma
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Sierra N Bennett
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | | | | | | | - Austin E Gillen
- Division of Hematology, University of Colorado School of Medicine, Aurora, CO, USA
- Rocky Mountain Regional VA Medical Center, Aurora, CO, USA
| | - Craig T Jordan
- Division of Hematology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Harinder Singh
- Departments of Immunology and Computational and Systems Biology, Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Diane S Krause
- Yale Stem Cell Center, Yale School of Medicine, New Haven, CT, USA
- Department of Laboratory Medicine, Yale University, New Haven, CT, USA
- Department of Cell Biology, Yale School of Medicine, New Haven, CT, USA
| | - Nathan Salomonis
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
- Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA.
| | - H Leighton Grimes
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
- Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA.
- Division of Experimental Hematology and Cancer Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
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2
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Gaddis N, Fortriede J, Guo M, Bardes EE, Kouril M, Tabar S, Burns K, Ardini-Poleske ME, Loos S, Schnell D, Jin K, Iyer B, Du Y, Huo BX, Bhattacharjee A, Korte J, Munshi R, Smith V, Herbst A, Kitzmiller JA, Clair GC, Carson JP, Adkins J, Morrisey EE, Pryhuber GS, Misra R, Whitsett JA, Sun X, Heathorn T, Paten B, Prasath VBS, Xu Y, Tickle T, Aronow BJ, Salomonis N. LungMAP Portal Ecosystem: Systems-level Exploration of the Lung. Am J Respir Cell Mol Biol 2024; 70:129-139. [PMID: 36413377 PMCID: PMC10848697 DOI: 10.1165/rcmb.2022-0165oc] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 11/21/2022] [Indexed: 11/23/2022] Open
Abstract
An improved understanding of the human lung necessitates advanced systems models informed by an ever-increasing repertoire of molecular omics, cellular imaging, and pathological datasets. To centralize and standardize information across broad lung research efforts, we expanded the LungMAP.net website into a new gateway portal. This portal connects a broad spectrum of research networks, bulk and single-cell multiomics data, and a diverse collection of image data that span mammalian lung development and disease. The data are standardized across species and technologies using harmonized data and metadata models that leverage recent advances, including those from the Human Cell Atlas, diverse ontologies, and the LungMAP CellCards initiative. To cultivate future discoveries, we have aggregated a diverse collection of single-cell atlases for multiple species (human, rhesus, and mouse) to enable consistent queries across technologies, cohorts, age, disease, and drug treatment. These atlases are provided as independent and integrated queryable datasets, with an emphasis on dynamic visualization, figure generation, reanalysis, cell-type curation, and automated reference-based classification of user-provided single-cell genomics datasets (Azimuth). As this resource grows, we intend to increase the breadth of available interactive interfaces, supported data types, data portals and datasets from LungMAP, and external research efforts.
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Affiliation(s)
- Nathan Gaddis
- RTI International, Research Triangle Park, North Carolina
| | - Joshua Fortriede
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
| | - Minzhe Guo
- Division of Pulmonary Biology, The Perinatal Institute, and
| | - Eric E. Bardes
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
| | - Michal Kouril
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
| | - Scott Tabar
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
| | - Kevin Burns
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
| | | | - Stephanie Loos
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
| | - Daniel Schnell
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
| | - Kang Jin
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
| | - Balaji Iyer
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
| | - Yina Du
- Division of Pulmonary Biology, The Perinatal Institute, and
| | - Bing-Xing Huo
- Data Sciences Platform, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Anukana Bhattacharjee
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
| | - Jeff Korte
- Data Sciences Platform, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Ruchi Munshi
- Data Sciences Platform, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Victoria Smith
- Data Sciences Platform, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Andrew Herbst
- Data Sciences Platform, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | | | - Geremy C. Clair
- Biological Science Division, Pacific Northwest National Laboratory, Richland, Washington
| | - James P. Carson
- Texas Advanced Computing Center, University of Texas at Austin, Austin, Texas
| | - Joshua Adkins
- Biological Science Division, Pacific Northwest National Laboratory, Richland, Washington
| | - Edward E. Morrisey
- Department of Medicine and
- Penn-CHOP Lung Biology Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Gloria S. Pryhuber
- Department of Pediatrics, University of Rochester Medical Center, Rochester, New York
| | - Ravi Misra
- Department of Pediatrics, University of Rochester Medical Center, Rochester, New York
| | - Jeffrey A. Whitsett
- Division of Pulmonary Biology, The Perinatal Institute, and
- Department of Pediatrics, University of Cincinnati School of Medicine, Cincinnati, Ohio
| | - Xin Sun
- Department of Pediatrics and
- Department of Biological Sciences, University of California, San Diego, San Diego, California; and
| | - Trevor Heathorn
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, California
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, California
| | - V. B. Surya Prasath
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
- Department of Pediatrics, University of Cincinnati School of Medicine, Cincinnati, Ohio
| | - Yan Xu
- Division of Pulmonary Biology, The Perinatal Institute, and
- Department of Pediatrics, University of Cincinnati School of Medicine, Cincinnati, Ohio
| | - Tim Tickle
- Data Sciences Platform, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Bruce J. Aronow
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
- Department of Pediatrics, University of Cincinnati School of Medicine, Cincinnati, Ohio
| | - Nathan Salomonis
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
- Department of Pediatrics, University of Cincinnati School of Medicine, Cincinnati, Ohio
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3
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Guo M, Morley MP, Jiang C, Wu Y, Li G, Du Y, Zhao S, Wagner A, Cakar AC, Kouril M, Jin K, Gaddis N, Kitzmiller JA, Stewart K, Basil MC, Lin SM, Ying Y, Babu A, Wikenheiser-Brokamp KA, Mun KS, Naren AP, Clair G, Adkins JN, Pryhuber GS, Misra RS, Aronow BJ, Tickle TL, Salomonis N, Sun X, Morrisey EE, Whitsett JA, Xu Y. Guided construction of single cell reference for human and mouse lung. Nat Commun 2023; 14:4566. [PMID: 37516747 PMCID: PMC10387117 DOI: 10.1038/s41467-023-40173-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 07/13/2023] [Indexed: 07/31/2023] Open
Abstract
Accurate cell type identification is a key and rate-limiting step in single-cell data analysis. Single-cell references with comprehensive cell types, reproducible and functionally validated cell identities, and common nomenclatures are much needed by the research community for automated cell type annotation, data integration, and data sharing. Here, we develop a computational pipeline utilizing the LungMAP CellCards as a dictionary to consolidate single-cell transcriptomic datasets of 104 human lungs and 17 mouse lung samples to construct LungMAP single-cell reference (CellRef) for both normal human and mouse lungs. CellRefs define 48 human and 40 mouse lung cell types catalogued from diverse anatomic locations and developmental time points. We demonstrate the accuracy and stability of LungMAP CellRefs and their utility for automated cell type annotation of both normal and diseased lungs using multiple independent methods and testing data. We develop user-friendly web interfaces for easy access and maximal utilization of the LungMAP CellRefs.
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Affiliation(s)
- Minzhe Guo
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA.
- Department of Pediatrics, University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH, 45267, USA.
| | - Michael P Morley
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn-CHOP Lung Biology Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Cell and Developmental Biology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Cheng Jiang
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - Yixin Wu
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - Guangyuan Li
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - Yina Du
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - Shuyang Zhao
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - Andrew Wagner
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - Adnan Cihan Cakar
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - Michal Kouril
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - Kang Jin
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | | | - Joseph A Kitzmiller
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - Kathleen Stewart
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn-CHOP Lung Biology Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Maria C Basil
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn-CHOP Lung Biology Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Susan M Lin
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn-CHOP Lung Biology Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Yun Ying
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn-CHOP Lung Biology Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Apoorva Babu
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn-CHOP Lung Biology Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Kathryn A Wikenheiser-Brokamp
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
- Division of Pathology and Laboratory Medicine, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
- Department of Pathology & Laboratory Medicine, University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH, 45267, USA
| | - Kyu Shik Mun
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
- Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Anjaparavanda P Naren
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Geremy Clair
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Joshua N Adkins
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Gloria S Pryhuber
- Department of Pediatrics Division of Neonatology, University of Rochester Medical Center, Rochester, NY, 14642, USA
| | - Ravi S Misra
- Department of Pediatrics Division of Neonatology, University of Rochester Medical Center, Rochester, NY, 14642, USA
| | - Bruce J Aronow
- Department of Pediatrics, University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH, 45267, USA
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - Timothy L Tickle
- Data Sciences Platform, The Broad Institute, Cambridge, MA, 02142, USA
| | - Nathan Salomonis
- Department of Pediatrics, University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH, 45267, USA
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - Xin Sun
- Department of Pediatrics, University of California at San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, USA
- Department of Biological Sciences, University of California at San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - Edward E Morrisey
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Penn-CHOP Lung Biology Institute, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Cell and Developmental Biology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jeffrey A Whitsett
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH, 45267, USA
| | - Yan Xu
- The Perinatal Institute and Section of Neonatology, Perinatal and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA.
- Department of Pediatrics, University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH, 45267, USA.
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA.
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4
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Pilarczyk M, Fazel-Najafabadi M, Kouril M, Shamsaei B, Vasiliauskas J, Niu W, Mahi N, Zhang L, Clark NA, Ren Y, White S, Karim R, Xu H, Biesiada J, Bennett MF, Davidson SE, Reichard JF, Roberts K, Stathias V, Koleti A, Vidovic D, Clarke DJB, Schürer SC, Ma'ayan A, Meller J, Medvedovic M. Connecting omics signatures and revealing biological mechanisms with iLINCS. Nat Commun 2022; 13:4678. [PMID: 35945222 PMCID: PMC9362980 DOI: 10.1038/s41467-022-32205-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 07/21/2022] [Indexed: 11/21/2022] Open
Abstract
There are only a few platforms that integrate multiple omics data types, bioinformatics tools, and interfaces for integrative analyses and visualization that do not require programming skills. Here we present iLINCS ( http://ilincs.org ), an integrative web-based platform for analysis of omics data and signatures of cellular perturbations. The platform facilitates mining and re-analysis of the large collection of omics datasets (>34,000), pre-computed signatures (>200,000), and their connections, as well as the analysis of user-submitted omics signatures of diseases and cellular perturbations. iLINCS analysis workflows integrate vast omics data resources and a range of analytics and interactive visualization tools into a comprehensive platform for analysis of omics signatures. iLINCS user-friendly interfaces enable execution of sophisticated analyses of omics signatures, mechanism of action analysis, and signature-driven drug repositioning. We illustrate the utility of iLINCS with three use cases involving analysis of cancer proteogenomic signatures, COVID 19 transcriptomic signatures and mTOR signaling.
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Affiliation(s)
- Marcin Pilarczyk
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Mehdi Fazel-Najafabadi
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Michal Kouril
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
| | - Behrouz Shamsaei
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Juozas Vasiliauskas
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Wen Niu
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Naim Mahi
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Lixia Zhang
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Nicholas A Clark
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Yan Ren
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Shana White
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Rashid Karim
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, 45220, USA
| | - Huan Xu
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Jacek Biesiada
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
| | - Mark F Bennett
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Sarah E Davidson
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
| | - John F Reichard
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
| | - Kurt Roberts
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
| | - Vasileios Stathias
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine and Center for Computational Science, University of Miami, Miami, FL 33136, USA
| | - Amar Koleti
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine and Center for Computational Science, University of Miami, Miami, FL 33136, USA
| | - Dusica Vidovic
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine and Center for Computational Science, University of Miami, Miami, FL 33136, USA
| | - Daniel J B Clarke
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Stephan C Schürer
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine and Center for Computational Science, University of Miami, Miami, FL 33136, USA
| | - Avi Ma'ayan
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Jarek Meller
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA
- LINCS Data Coordination and Integration Center (DCIC), New York, USA
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, 45220, USA
| | - Mario Medvedovic
- Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH, 45220, USA.
- LINCS Data Coordination and Integration Center (DCIC), Cincinnati, USA.
- LINCS Data Coordination and Integration Center (DCIC), New York, USA.
- LINCS Data Coordination and Integration Center (DCIC), Miami, USA.
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5
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Shamsaei B, Chojnacki S, Pilarczyk M, Najafabadi M, Niu W, Chen C, Ross K, Matlock A, Muhlich J, Chutipongtanate S, Zheng J, Turner J, Vidović D, Jaffe J, MacCoss M, Wu C, Pillai A, Ma'ayan A, Schürer S, Kouril M, Medvedovic M, Meller J. piNET: a versatile web platform for downstream analysis and visualization of proteomics data. Nucleic Acids Res 2020; 48:W85-W93. [PMID: 32469073 PMCID: PMC7319557 DOI: 10.1093/nar/gkaa436] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 04/29/2020] [Accepted: 05/27/2020] [Indexed: 02/03/2023] Open
Abstract
Rapid progress in proteomics and large-scale profiling of biological systems at the protein level necessitates the continued development of efficient computational tools for the analysis and interpretation of proteomics data. Here, we present the piNET server that facilitates integrated annotation, analysis and visualization of quantitative proteomics data, with emphasis on PTM networks and integration with the LINCS library of chemical and genetic perturbation signatures in order to provide further mechanistic and functional insights. The primary input for the server consists of a set of peptides or proteins, optionally with PTM sites, and their corresponding abundance values. Several interconnected workflows can be used to generate: (i) interactive graphs and tables providing comprehensive annotation and mapping between peptides and proteins with PTM sites; (ii) high resolution and interactive visualization for enzyme-substrate networks, including kinases and their phospho-peptide targets; (iii) mapping and visualization of LINCS signature connectivity for chemical inhibitors or genetic knockdown of enzymes upstream of their target PTM sites. piNET has been built using a modular Spring-Boot JAVA platform as a fast, versatile and easy to use tool. The Apache Lucene indexing is used for fast mapping of peptides into UniProt entries for the human, mouse and other commonly used model organism proteomes. PTM-centric network analyses combine PhosphoSitePlus, iPTMnet and SIGNOR databases of validated enzyme-substrate relationships, for kinase networks augmented by DeepPhos predictions and sequence-based mapping of PhosphoSitePlus consensus motifs. Concordant LINCS signatures are mapped using iLINCS. For each workflow, a RESTful API counterpart can be used to generate the results programmatically in the json format. The server is available at http://pinet-server.org, and it is free and open to all users without login requirement.
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Affiliation(s)
- Behrouz Shamsaei
- Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, USA
| | - Szymon Chojnacki
- Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, USA
| | - Marcin Pilarczyk
- Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, USA
| | - Mehdi Najafabadi
- Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, USA
| | - Wen Niu
- Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, USA
| | - Chuming Chen
- Center for Bioinformatics & Computational Biology; University of Delaware, USA
| | - Karen Ross
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, USA
| | - Andrea Matlock
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, USA
| | - Jeremy Muhlich
- Department of Systems Biology, Harvard Medical School, USA
| | - Somchai Chutipongtanate
- Department of Cancer Biology, University of Cincinnati College of Medicine, USA.,Department of Pediatrics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Thailand
| | - Jie Zheng
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, USA
| | - John Turner
- Department of Pharmacology, Miller School of Medicine, Sylvester Comprehensive Cancer Center, Center for Computational Science, University of Miami, Miami, USA
| | - Dušica Vidović
- Department of Pharmacology, Miller School of Medicine, Sylvester Comprehensive Cancer Center, Center for Computational Science, University of Miami, Miami, USA
| | - Jake Jaffe
- Broad Institute of MIT and Harvard & Inzen Therapeutics, USA
| | - Michael MacCoss
- Department of Genome Sciences, University of Washington, USA
| | - Cathy Wu
- Center for Bioinformatics & Computational Biology; University of Delaware, USA.,Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, USA
| | - Ajay Pillai
- Human Genome Research Institute, National Institutes of Health, Bethesda, USA
| | - Avi Ma'ayan
- Mount Sinai Center for Bioinformatics, Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, USA
| | - Stephan Schürer
- Department of Pharmacology, Miller School of Medicine, Sylvester Comprehensive Cancer Center, Center for Computational Science, University of Miami, Miami, USA
| | - Michal Kouril
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, USA
| | - Mario Medvedovic
- Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, USA.,Department of Biomedical Informatics, University of Cincinnati College of Medicine, USA
| | - Jarek Meller
- Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, USA.,Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, USA.,Department of Electrical Engineering and Computer Science, University of Cincinnati, USA
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6
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Wissel BD, Van Camp PJ, Kouril M, Weis C, Glauser TA, White PS, Kohane IS, Dexheimer JW. An interactive online dashboard for tracking COVID-19 in U.S. counties, cities, and states in real time. J Am Med Inform Assoc 2020; 27:1121-1125. [PMID: 32333753 PMCID: PMC7188179 DOI: 10.1093/jamia/ocaa071] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 04/16/2020] [Accepted: 04/20/2020] [Indexed: 11/17/2022] Open
Abstract
OBJECTIVE The study sought to create an online resource that informs the public of coronavirus disease 2019 (COVID-19) outbreaks in their area. MATERIALS AND METHODS This R Shiny application aggregates data from multiple resources that track COVID-19 and visualizes them through an interactive, online dashboard. RESULTS The Web resource, called the COVID-19 Watcher, can be accessed online (https://covid19watcher.research.cchmc.org/). It displays COVID-19 data from every county and 188 metropolitan areas in the United States. Features include rankings of the worst-affected areas and auto-generating plots that depict temporal changes in testing capacity, cases, and deaths. DISCUSSION The Centers for Disease Control and Prevention does not publish COVID-19 data for local municipalities, so it is critical that academic resources fill this void so the public can stay informed. The data used have limitations and likely underestimate the scale of the outbreak. CONCLUSIONS The COVID-19 Watcher can provide the public with real-time updates of outbreaks in their area.
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Affiliation(s)
- Benjamin D Wissel
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Biomedical Informatics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - P J Van Camp
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Biomedical Informatics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Michal Kouril
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Biomedical Informatics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Chad Weis
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA
| | - Tracy A Glauser
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
- Division of Neurology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA
| | - Peter S White
- Department of Biomedical Informatics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Judith W Dexheimer
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Biomedical Informatics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
- Division of Emergency Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA
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7
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Thorman AW, Reigle J, Chutipongtanate S, Shamsaei B, Pilarczyk M, Fazel-Najafabadi M, Adamczak R, Kouril M, Medvedovic M, Meller J. Connectivity Enhanced Structure Activity Relationship (ceSAR): A Novel Approach to Increase Accuracy and Speed of Virtual Drug Discovery. FASEB J 2020. [DOI: 10.1096/fasebj.2020.34.s1.05660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
| | | | | | | | | | | | | | | | | | - Jarek Meller
- University of Cincinnati
- Cincinnati Children's Hospital and Medical Center
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8
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Abstract
The vast amount of RNA-seq data deposited in Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA) is still a grossly underutilized resource for biomedical research. To remove technical roadblocks for reusing these data, we have developed a web-application GREIN (GEO RNA-seq Experiments Interactive Navigator) which provides user-friendly interfaces to manipulate and analyze GEO RNA-seq data. GREIN is powered by the back-end computational pipeline for uniform processing of RNA-seq data and the large number (>6,500) of already processed datasets. The front-end user interfaces provide a wealth of user-analytics options including sub-setting and downloading processed data, interactive visualization, statistical power analyses, construction of differential gene expression signatures and their comprehensive functional characterization, and connectivity analysis with LINCS L1000 data. The combination of the massive amount of back-end data and front-end analytics options driven by user-friendly interfaces makes GREIN a unique open-source resource for re-using GEO RNA-seq data. GREIN is accessible at: https://shiny.ilincs.org/grein , the source code at: https://github.com/uc-bd2k/grein , and the Docker container at: https://hub.docker.com/r/ucbd2k/grein .
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Affiliation(s)
- Naim Al Mahi
- Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati, 3223 Eden Avenue, Cincinnati, OH, 45220, USA
| | - Mehdi Fazel Najafabadi
- Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati, 3223 Eden Avenue, Cincinnati, OH, 45220, USA
| | - Marcin Pilarczyk
- Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati, 3223 Eden Avenue, Cincinnati, OH, 45220, USA
| | - Michal Kouril
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Mario Medvedovic
- Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati, 3223 Eden Avenue, Cincinnati, OH, 45220, USA.
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9
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Keenan AB, Jenkins SL, Jagodnik KM, Koplev S, He E, Torre D, Wang Z, Dohlman AB, Silverstein MC, Lachmann A, Kuleshov MV, Ma'ayan A, Stathias V, Terryn R, Cooper D, Forlin M, Koleti A, Vidovic D, Chung C, Schürer SC, Vasiliauskas J, Pilarczyk M, Shamsaei B, Fazel M, Ren Y, Niu W, Clark NA, White S, Mahi N, Zhang L, Kouril M, Reichard JF, Sivaganesan S, Medvedovic M, Meller J, Koch RJ, Birtwistle MR, Iyengar R, Sobie EA, Azeloglu EU, Kaye J, Osterloh J, Haston K, Kalra J, Finkbiener S, Li J, Milani P, Adam M, Escalante-Chong R, Sachs K, Lenail A, Ramamoorthy D, Fraenkel E, Daigle G, Hussain U, Coye A, Rothstein J, Sareen D, Ornelas L, Banuelos M, Mandefro B, Ho R, Svendsen CN, Lim RG, Stocksdale J, Casale MS, Thompson TG, Wu J, Thompson LM, Dardov V, Venkatraman V, Matlock A, Van Eyk JE, Jaffe JD, Papanastasiou M, Subramanian A, Golub TR, Erickson SD, Fallahi-Sichani M, Hafner M, Gray NS, Lin JR, Mills CE, Muhlich JL, Niepel M, Shamu CE, Williams EH, Wrobel D, Sorger PK, Heiser LM, Gray JW, Korkola JE, Mills GB, LaBarge M, Feiler HS, Dane MA, Bucher E, Nederlof M, Sudar D, Gross S, Kilburn DF, Smith R, Devlin K, Margolis R, Derr L, Lee A, Pillai A. The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations. Cell Syst 2018; 6:13-24. [PMID: 29199020 PMCID: PMC5799026 DOI: 10.1016/j.cels.2017.11.001] [Citation(s) in RCA: 241] [Impact Index Per Article: 40.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Revised: 09/13/2017] [Accepted: 11/01/2017] [Indexed: 12/19/2022]
Abstract
The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program that catalogs how human cells globally respond to chemical, genetic, and disease perturbations. Resources generated by LINCS include experimental and computational methods, visualization tools, molecular and imaging data, and signatures. By assembling an integrated picture of the range of responses of human cells exposed to many perturbations, the LINCS program aims to better understand human disease and to advance the development of new therapies. Perturbations under study include drugs, genetic perturbations, tissue micro-environments, antibodies, and disease-causing mutations. Responses to perturbations are measured by transcript profiling, mass spectrometry, cell imaging, and biochemical methods, among other assays. The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders. This Perspective describes LINCS technologies, datasets, tools, and approaches to data accessibility and reusability.
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Affiliation(s)
- Alexandra B Keenan
- BD2K-LINCS DCIC, Mount Sinai Center for Bioinformatics, Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sherry L Jenkins
- BD2K-LINCS DCIC, Mount Sinai Center for Bioinformatics, Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kathleen M Jagodnik
- BD2K-LINCS DCIC, Mount Sinai Center for Bioinformatics, Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Simon Koplev
- BD2K-LINCS DCIC, Mount Sinai Center for Bioinformatics, Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Edward He
- BD2K-LINCS DCIC, Mount Sinai Center for Bioinformatics, Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Denis Torre
- BD2K-LINCS DCIC, Mount Sinai Center for Bioinformatics, Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Zichen Wang
- BD2K-LINCS DCIC, Mount Sinai Center for Bioinformatics, Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Anders B Dohlman
- BD2K-LINCS DCIC, Mount Sinai Center for Bioinformatics, Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Moshe C Silverstein
- BD2K-LINCS DCIC, Mount Sinai Center for Bioinformatics, Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Alexander Lachmann
- BD2K-LINCS DCIC, Mount Sinai Center for Bioinformatics, Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Maxim V Kuleshov
- BD2K-LINCS DCIC, Mount Sinai Center for Bioinformatics, Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Avi Ma'ayan
- BD2K-LINCS DCIC, Mount Sinai Center for Bioinformatics, Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Vasileios Stathias
- BD2K-LINCS DCIC, Department of Molecular and Cellular Pharmacology, University of Miami, Miami, FL 33146, USA
| | - Raymond Terryn
- BD2K-LINCS DCIC, Department of Molecular and Cellular Pharmacology, University of Miami, Miami, FL 33146, USA
| | - Daniel Cooper
- BD2K-LINCS DCIC, Department of Molecular and Cellular Pharmacology, University of Miami, Miami, FL 33146, USA
| | - Michele Forlin
- BD2K-LINCS DCIC, Department of Molecular and Cellular Pharmacology, University of Miami, Miami, FL 33146, USA
| | - Amar Koleti
- BD2K-LINCS DCIC, Department of Molecular and Cellular Pharmacology, University of Miami, Miami, FL 33146, USA
| | - Dusica Vidovic
- BD2K-LINCS DCIC, Department of Molecular and Cellular Pharmacology, University of Miami, Miami, FL 33146, USA
| | - Caty Chung
- BD2K-LINCS DCIC, Department of Molecular and Cellular Pharmacology, University of Miami, Miami, FL 33146, USA
| | - Stephan C Schürer
- BD2K-LINCS DCIC, Department of Molecular and Cellular Pharmacology, University of Miami, Miami, FL 33146, USA
| | - Jouzas Vasiliauskas
- BD2K-LINCS DCIC, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45220, USA
| | - Marcin Pilarczyk
- BD2K-LINCS DCIC, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45220, USA
| | - Behrouz Shamsaei
- BD2K-LINCS DCIC, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45220, USA
| | - Mehdi Fazel
- BD2K-LINCS DCIC, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45220, USA
| | - Yan Ren
- BD2K-LINCS DCIC, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45220, USA
| | - Wen Niu
- BD2K-LINCS DCIC, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45220, USA
| | - Nicholas A Clark
- BD2K-LINCS DCIC, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45220, USA
| | - Shana White
- BD2K-LINCS DCIC, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45220, USA
| | - Naim Mahi
- BD2K-LINCS DCIC, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45220, USA
| | - Lixia Zhang
- BD2K-LINCS DCIC, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45220, USA
| | - Michal Kouril
- BD2K-LINCS DCIC, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45220, USA
| | - John F Reichard
- BD2K-LINCS DCIC, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45220, USA
| | - Siva Sivaganesan
- BD2K-LINCS DCIC, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45220, USA
| | - Mario Medvedovic
- BD2K-LINCS DCIC, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45220, USA
| | - Jaroslaw Meller
- BD2K-LINCS DCIC, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45220, USA
| | - Rick J Koch
- DToxS, Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Marc R Birtwistle
- DToxS, Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ravi Iyengar
- DToxS, Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Eric A Sobie
- DToxS, Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Evren U Azeloglu
- DToxS, Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Julia Kaye
- NeuroLINCS, Gladstone Institute of Neurological Disease and the Departments of Neurology and Physiology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Jeannette Osterloh
- NeuroLINCS, Gladstone Institute of Neurological Disease and the Departments of Neurology and Physiology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Kelly Haston
- NeuroLINCS, Gladstone Institute of Neurological Disease and the Departments of Neurology and Physiology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Jaslin Kalra
- NeuroLINCS, Gladstone Institute of Neurological Disease and the Departments of Neurology and Physiology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Steve Finkbiener
- NeuroLINCS, Gladstone Institute of Neurological Disease and the Departments of Neurology and Physiology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Jonathan Li
- NeuroLINCS, Department of Biological Engineering, MIT, Cambridge, MA 02142, USA
| | - Pamela Milani
- NeuroLINCS, Department of Biological Engineering, MIT, Cambridge, MA 02142, USA
| | - Miriam Adam
- NeuroLINCS, Department of Biological Engineering, MIT, Cambridge, MA 02142, USA
| | | | - Karen Sachs
- NeuroLINCS, Department of Biological Engineering, MIT, Cambridge, MA 02142, USA
| | - Alex Lenail
- NeuroLINCS, Department of Biological Engineering, MIT, Cambridge, MA 02142, USA
| | - Divya Ramamoorthy
- NeuroLINCS, Department of Biological Engineering, MIT, Cambridge, MA 02142, USA
| | - Ernest Fraenkel
- NeuroLINCS, Department of Biological Engineering, MIT, Cambridge, MA 02142, USA
| | - Gavin Daigle
- NeuroLINCS, Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Uzma Hussain
- NeuroLINCS, Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Alyssa Coye
- NeuroLINCS, Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Jeffrey Rothstein
- NeuroLINCS, Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Dhruv Sareen
- NeuroLINCS, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Loren Ornelas
- NeuroLINCS, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Maria Banuelos
- NeuroLINCS, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Berhan Mandefro
- NeuroLINCS, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Ritchie Ho
- NeuroLINCS, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Clive N Svendsen
- NeuroLINCS, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Ryan G Lim
- NeuroLINCS, Departments of Psychiatry and Human Behavior and Neurobiology and Behavior, University of California Irvine, Irvine, CA 92697, USA
| | - Jennifer Stocksdale
- NeuroLINCS, Departments of Psychiatry and Human Behavior and Neurobiology and Behavior, University of California Irvine, Irvine, CA 92697, USA
| | - Malcolm S Casale
- NeuroLINCS, Departments of Psychiatry and Human Behavior and Neurobiology and Behavior, University of California Irvine, Irvine, CA 92697, USA
| | - Terri G Thompson
- NeuroLINCS, Departments of Psychiatry and Human Behavior and Neurobiology and Behavior, University of California Irvine, Irvine, CA 92697, USA
| | - Jie Wu
- NeuroLINCS, Departments of Psychiatry and Human Behavior and Neurobiology and Behavior, University of California Irvine, Irvine, CA 92697, USA
| | - Leslie M Thompson
- NeuroLINCS, Departments of Psychiatry and Human Behavior and Neurobiology and Behavior, University of California Irvine, Irvine, CA 92697, USA
| | - Victoria Dardov
- NeuroLINCS, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | | | - Andrea Matlock
- NeuroLINCS, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | | | - Jacob D Jaffe
- LINCS PCCSE, The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | | | - Aravind Subramanian
- LINCS Center for Transcriptomics, The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Todd R Golub
- LINCS Center for Transcriptomics, The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Dana-Farber Cancer Institute, Boston, MA 02215, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Sean D Erickson
- HMS LINCS Center, Harvard Medical School, Boston, MA 02115, USA
| | | | - Marc Hafner
- HMS LINCS Center, Harvard Medical School, Boston, MA 02115, USA
| | | | - Jia-Ren Lin
- HMS LINCS Center, Harvard Medical School, Boston, MA 02115, USA
| | - Caitlin E Mills
- HMS LINCS Center, Harvard Medical School, Boston, MA 02115, USA
| | | | - Mario Niepel
- HMS LINCS Center, Harvard Medical School, Boston, MA 02115, USA
| | | | | | - David Wrobel
- HMS LINCS Center, Harvard Medical School, Boston, MA 02115, USA
| | - Peter K Sorger
- HMS LINCS Center, Harvard Medical School, Boston, MA 02115, USA
| | - Laura M Heiser
- MEP-LINCS Center, Oregon Health & Science University, Portland, OR 97239, USA
| | - Joe W Gray
- MEP-LINCS Center, Oregon Health & Science University, Portland, OR 97239, USA
| | - James E Korkola
- MEP-LINCS Center, Oregon Health & Science University, Portland, OR 97239, USA
| | - Gordon B Mills
- MEP-LINCS Center, Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Mark LaBarge
- MEP-LINCS Center, Department of Population Sciences, Beckman Research Institute at City of Hope, Duarte, CA 91011, USA; MEP-LINCS Center, Center for Cancer Biomarkers Research, University of Bergen, Bergen 5009, Norway
| | - Heidi S Feiler
- MEP-LINCS Center, Oregon Health & Science University, Portland, OR 97239, USA
| | - Mark A Dane
- MEP-LINCS Center, Oregon Health & Science University, Portland, OR 97239, USA
| | - Elmar Bucher
- MEP-LINCS Center, Oregon Health & Science University, Portland, OR 97239, USA
| | - Michel Nederlof
- MEP-LINCS Center, Oregon Health & Science University, Portland, OR 97239, USA; MEP-LINCS Center, Quantitative Imaging Systems LLC, Portland, OR 97239, USA
| | - Damir Sudar
- MEP-LINCS Center, Oregon Health & Science University, Portland, OR 97239, USA; MEP-LINCS Center, Quantitative Imaging Systems LLC, Portland, OR 97239, USA
| | - Sean Gross
- MEP-LINCS Center, Oregon Health & Science University, Portland, OR 97239, USA
| | - David F Kilburn
- MEP-LINCS Center, Oregon Health & Science University, Portland, OR 97239, USA
| | - Rebecca Smith
- MEP-LINCS Center, Oregon Health & Science University, Portland, OR 97239, USA
| | - Kaylyn Devlin
- MEP-LINCS Center, Oregon Health & Science University, Portland, OR 97239, USA
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10
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Kirkendall ES, Kouril M, Dexheimer JW, Courter JD, Hagedorn P, Szczesniak R, Li D, Damania R, Minich T, Spooner SA. Automated identification of antibiotic overdoses and adverse drug events via analysis of prescribing alerts and medication administration records. J Am Med Inform Assoc 2017; 24:295-302. [PMID: 27507653 DOI: 10.1093/jamia/ocw086] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2015] [Accepted: 04/30/2016] [Indexed: 01/22/2023] Open
Abstract
Objectives Electronic trigger detection tools hold promise to reduce Adverse drug event (ADEs) through efficiencies of scale and real-time reporting. We hypothesized that such a tool could automatically detect medication dosing errors as well as manage and evaluate dosing rule modifications. Materials and Methods We created an order and alert analysis system that identified antibiotic medication orders and evaluated user response to dosing alerts. Orders associated with overridden alerts were examined for evidence of administration and the delivered dose was compared to pharmacy-derived dosing rules to confirm true overdoses. True overdose cases were reviewed for association with known ADEs. Results Of 55 546 orders reviewed, 539 were true overdose orders, which lead to 1965 known overdose administrations. Documentation of loose stools and diarrhea was significantly increased following drug administration in the overdose group. Dosing rule thresholds were altered to reflect clinically accurate dosing. These rule changes decreased overall alert burden and improved the salience of alerts. Discussion Electronic algorithm-based detection systems can identify antibiotic overdoses that are clinically relevant and are associated with known ADEs. The system also serves as a platform for evaluating the effects of modifying electronic dosing rules. These modifications lead to decreased alert burden and improvements in response to decision support alerts. Conclusion The success of this test case suggests that gains are possible in reducing medication errors and improving patient safety with automated algorithm-based detection systems. Follow-up studies will determine if the positive effects of the system persist and if these changes lead to improved safety outcomes.
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Affiliation(s)
- Eric S Kirkendall
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.,Department of Information Services, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.,Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.,James M. Anderson Center for Health Systems Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Michal Kouril
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Judith W Dexheimer
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.,Division of Emergency Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Joshua D Courter
- Division of Pharmacy, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Philip Hagedorn
- Department of Information Services, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.,Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Rhonda Szczesniak
- Division of Biostatistics & Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.,Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Dan Li
- Department of Mathematical Sciences, University of Cincinnati, Cincinnati, Ohio, USA
| | - Rahul Damania
- Northeast Ohio Medical University College of Medicine, Rootstown, Ohio, USA
| | - Thomas Minich
- Department of Information Services, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.,Division of Pharmacy, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - S Andrew Spooner
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.,Department of Information Services, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.,Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
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11
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Clark NA, Hafner M, Kouril M, Williams EH, Muhlich JL, Pilarczyk M, Niepel M, Sorger PK, Medvedovic M. GRcalculator: an online tool for calculating and mining dose-response data. BMC Cancer 2017; 17:698. [PMID: 29065900 PMCID: PMC5655815 DOI: 10.1186/s12885-017-3689-3] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2017] [Accepted: 10/16/2017] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Quantifying the response of cell lines to drugs or other perturbagens is the cornerstone of pre-clinical drug development and pharmacogenomics as well as a means to study factors that contribute to sensitivity and resistance. In dividing cells, traditional metrics derived from dose-response curves such as IC 50 , AUC, and E max , are confounded by the number of cell divisions taking place during the assay, which varies widely for biological and experimental reasons. Hafner et al. (Nat Meth 13:521-627, 2016) recently proposed an alternative way to quantify drug response, normalized growth rate (GR) inhibition, that is robust to such confounders. Adoption of the GR method is expected to improve the reproducibility of dose-response assays and the reliability of pharmacogenomic associations (Hafner et al. 500-502, 2017). RESULTS We describe here an interactive website ( www.grcalculator.org ) for calculation, analysis, and visualization of dose-response data using the GR approach and for comparison of GR and traditional metrics. Data can be user-supplied or derived from published datasets. The web tools are implemented in the form of three integrated Shiny applications (grcalculator, grbrowser, and grtutorial) deployed through a Shiny server. Intuitive graphical user interfaces (GUIs) allow for interactive analysis and visualization of data. The Shiny applications make use of two R packages (shinyLi and GRmetrics) specifically developed for this purpose. The GRmetrics R package is also available via Bioconductor and can be used for offline data analysis and visualization. Source code for the Shiny applications and associated packages (shinyLi and GRmetrics) can be accessed at www.github.com/uc-bd2k/grcalculator and www.github.com/datarail/gr_metrics . CONCLUSIONS GRcalculator is a powerful, user-friendly, and free tool to facilitate analysis of dose-response data. It generates publication-ready figures and provides a unified platform for investigators to analyze dose-response data across diverse cell types and perturbagens (including drugs, biological ligands, RNAi, etc.). GRcalculator also provides access to data collected by the NIH LINCS Program ( http://www.lincsproject.org /) and other public domain datasets. The GRmetrics Bioconductor package provides computationally trained users with a platform for offline analysis of dose-response data and facilitates inclusion of GR metrics calculations within existing R analysis pipelines. These tools are therefore well suited to users in academia as well as industry.
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Affiliation(s)
- Nicholas A. Clark
- LINCS-BD2K DCIC, Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45221 USA
| | - Marc Hafner
- HMS LINCS Center, Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115 USA
| | - Michal Kouril
- Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229 USA
| | - Elizabeth H. Williams
- HMS LINCS Center, Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115 USA
| | - Jeremy L. Muhlich
- HMS LINCS Center, Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115 USA
| | - Marcin Pilarczyk
- LINCS-BD2K DCIC, Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45221 USA
| | - Mario Niepel
- HMS LINCS Center, Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115 USA
| | - Peter K. Sorger
- HMS LINCS Center, Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115 USA
| | - Mario Medvedovic
- LINCS-BD2K DCIC, Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45221 USA
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Dexheimer JW, Kirkendall ES, Kouril M, Hagedorn PA, Minich T, Duan LL, Mahdi M, Szczesniak R, Spooner SA. The Effects of Medication Alerts on Prescriber Response in a Pediatric Hospital. Appl Clin Inform 2017; 8:491-501. [PMID: 28487930 PMCID: PMC6241745 DOI: 10.4338/aci-2016-10-ra-0168] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Accepted: 02/28/2017] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE More than 70% of hospitals in the United States have electronic health records (EHRs). Clinical decision support (CDS) presents clinicians with electronic alerts during the course of patient care; however, alert fatigue can influence a provider's response to any EHR alert. The primary goal was to evaluate the effects of alert burden on user response to the alerts. METHODS We performed a retrospective study of medication alerts over a 24-month period (1/2013-12/2014) in a large pediatric academic medical center. The institutional review board approved this study. The primary outcome measure was alert salience, a measure of whether or not the prescriber took any corrective action on the order that generated an alert. We estimated the ideal number of alerts to maximize salience. Salience rates were examined for providers at each training level, by day of week, and time of day through logistic regressions. RESULTS While salience never exceeded 38%, 49 alerts/day were associated with maximal salience in our dataset. The time of day an order was placed was associated with alert salience (maximal salience 2am). The day of the week was also associated with alert salience (maximal salience on Wednesday). Provider role did not have an impact on salience. CONCLUSION Alert burden plays a role in influencing provider response to medication alerts. An increased number of alerts a provider saw during a one-day period did not directly lead to decreased response to alerts. Given the multiple factors influencing the response to alerts, efforts focused solely on burden are not likely to be effective.
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Affiliation(s)
- Judith W Dexheimer
- Judith Dexheimer, PhD, Division of Emergency Medicine, Cincinnati Children's Hospital Medical Center, MLC 2008, 3333 Burnet Avenue, Cincinnati, OH 45229-3039, , Phone: 513-803-2962, Fax: 513-803-2581
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13
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Affiliation(s)
- Philip A Hagedorn
- Department of Information Services, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio2Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Eric S Kirkendall
- Department of Information Services, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio2Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio3Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio4James M. Anderson Center for Health Systems Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Michal Kouril
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Judith W Dexheimer
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio5Division of Emergency Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Joshua Courter
- Division of Pharmacy, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Thomas Minich
- Division of Pharmacy, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - S Andrew Spooner
- Department of Information Services, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio2Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio3Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
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14
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Deleger L, Lingren T, Ni Y, Kaiser M, Stoutenborough L, Marsolo K, Kouril M, Molnar K, Solti I. Preparing an annotated gold standard corpus to share with extramural investigators for de-identification research. J Biomed Inform 2014; 50:173-183. [PMID: 24556292 DOI: 10.1016/j.jbi.2014.01.014] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2013] [Revised: 01/28/2014] [Accepted: 01/30/2014] [Indexed: 10/25/2022]
Abstract
OBJECTIVE The current study aims to fill the gap in available healthcare de-identification resources by creating a new sharable dataset with realistic Protected Health Information (PHI) without reducing the value of the data for de-identification research. By releasing the annotated gold standard corpus with Data Use Agreement we would like to encourage other Computational Linguists to experiment with our data and develop new machine learning models for de-identification. This paper describes: (1) the modifications required by the Institutional Review Board before sharing the de-identification gold standard corpus; (2) our efforts to keep the PHI as realistic as possible; (3) and the tests to show the effectiveness of these efforts in preserving the value of the modified data set for machine learning model development. MATERIALS AND METHODS In a previous study we built an original de-identification gold standard corpus annotated with true Protected Health Information (PHI) from 3503 randomly selected clinical notes for the 22 most frequent clinical note types of our institution. In the current study we modified the original gold standard corpus to make it suitable for external sharing by replacing HIPAA-specified PHI with newly generated realistic PHI. Finally, we evaluated the research value of this new dataset by comparing the performance of an existing published in-house de-identification system, when trained on the new de-identification gold standard corpus, with the performance of the same system, when trained on the original corpus. We assessed the potential benefits of using the new de-identification gold standard corpus to identify PHI in the i2b2 and PhysioNet datasets that were released by other groups for de-identification research. We also measured the effectiveness of the i2b2 and PhysioNet de-identification gold standard corpora in identifying PHI in our original clinical notes. RESULTS Performance of the de-identification system using the new gold standard corpus as a training set was very close to training on the original corpus (92.56 vs. 93.48 overall F-measures). Best i2b2/PhysioNet/CCHMC cross-training performances were obtained when training on the new shared CCHMC gold standard corpus, although performances were still lower than corpus-specific trainings. DISCUSSION AND CONCLUSION We successfully modified a de-identification dataset for external sharing while preserving the de-identification research value of the modified gold standard corpus with limited drop in machine learning de-identification performance.
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Affiliation(s)
- Louise Deleger
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Todd Lingren
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Yizhao Ni
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Megan Kaiser
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Laura Stoutenborough
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Keith Marsolo
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Michal Kouril
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Katalin Molnar
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Imre Solti
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
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15
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Kirkendall ES, Kouril M, Minich T, Spooner SA. Analysis of electronic medication orders with large overdoses: opportunities for mitigating dosing errors. Appl Clin Inform 2014; 5:25-45. [PMID: 24734122 DOI: 10.4338/aci-2013-08-ra-0057] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2013] [Accepted: 11/17/2013] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Users of electronic health record (EHR) systems frequently prescribe doses outside recommended dose ranges, and tend to ignore the alerts that result. Since some of these dosing errors are the result of system design flaws, analysis of large overdoses can lead to the discovery of needed system changes. OBJECTIVES To develop database techniques for detecting and extracting large overdose orders from our EHR. To identify and characterize users' responses to these large overdoses. To identify possible causes of large-overdose errors and to mitigate them. METHODS We constructed a data mart of medication-order and dosing-alert data from a quaternary pediatric hospital from June 2011 to May 2013. The data mart was used along with a test version of the EHR to explain how orders were processed and alerts were generated for large (>500%) and extreme (>10,000%) overdoses. User response was characterized by the dosing alert salience rate, which expresses the proportion of time users take corrective action. RESULTS We constructed an advanced analytic framework based on workflow analysis and order simulation, and evaluated all 5,402,504 medication orders placed within the 2 year timeframe as well as 2,232,492 dose alerts associated with some of the orders. 8% of orders generated a visible alert, with ¼ of these related to overdosing. Alerts presented to trainees had higher salience rates than those presented to senior colleagues. Salience rates were low, varying between 4-10%, and were lower with larger overdoses. Extreme overdoses fell into eight causal categories, each with a system design mitigation. CONCLUSIONS Novel analytic systems are required to accurately understand prescriber behavior and interactions with medication-dosing CDS. We described a novel analytic system that can detect apparent large overdoses (≥500%) and explain the sociotechnical factors that drove the error. Some of these large overdoses can be mitigated by system changes. EHR design should prospectively mitigate these errors.
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Affiliation(s)
| | - M Kouril
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center , Cincinnati, Ohio
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16
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Deleger L, Li Q, Lingren T, Kaiser M, Molnar K, Stoutenborough L, Kouril M, Marsolo K, Solti I. Building gold standard corpora for medical natural language processing tasks. AMIA Annu Symp Proc 2012; 2012:144-153. [PMID: 23304283 PMCID: PMC3540456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
We present the construction of three annotated corpora to serve as gold standards for medical natural language processing (NLP) tasks. Clinical notes from the medical record, clinical trial announcements, and FDA drug labels are annotated. We report high inter-annotator agreements (overall F-measures between 0.8467 and 0.9176) for the annotation of Personal Health Information (PHI) elements for a de-identification task and of medications, diseases/disorders, and signs/symptoms for information extraction (IE) task. The annotated corpora of clinical trials and FDA labels will be publicly released and to facilitate translational NLP tasks that require cross-corpora interoperability (e.g. clinical trial eligibility screening) their annotation schemas are aligned with a large scale, NIH-funded clinical text annotation project.
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Affiliation(s)
- Louise Deleger
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
| | - Qi Li
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
| | - Todd Lingren
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
| | - Megan Kaiser
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
| | - Katalin Molnar
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
| | - Laura Stoutenborough
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
| | - Michal Kouril
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
| | - Keith Marsolo
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
| | - Imre Solti
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
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17
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Abstract
Progress in functional genomics and structural studies on biological macromolecules are generating a growing number of potential targets for therapeutics, adding to the importance of computational approaches for small molecule docking and virtual screening of candidate compounds. In this review, recent improvements in several public domain packages that are widely used in the context of drug development, including DOCK, AutoDock, AutoDock Vina and Screening for Ligands by Induced-fit Docking Efficiently (SLIDE) are surveyed. The authors also survey methods for the analysis and visualisation of docking simulations, as an important step in the overall assessment of the results. In order to illustrate the performance and limitations of current docking programs, the authors used the National Center for Toxicological Research (NCTR) oestrogen receptor benchmark set of 232 oestrogenic compounds with experimentally measured strength of binding to oestrogen receptor alpha. The methods tested here yielded a correlation coefficient of up to 0.6 between the predicted and observed binding affinities for active compounds in this benchmark.
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Affiliation(s)
- Jacek Biesiada
- Biomedical Informatics, Children's Hospital Research Foundation, Cincinnati, OH 45229, USA
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18
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Abstract
The Prostate Cancer Prevention Trial is the first phase 3 prevention trial for prostate cancer in the United States. The implementation of a large, randomized trial has provided a wealth of information that will aid in future cancer chemopreventive studies in US men. The experience from the implementation of the Prostate Cancer Prevention Trial was reviewed. Lessons learned from the study include: (1) US men are willing to enroll in prevention trials; (2) participants in chemoprevention trials are well educated and healthy; (3) the successful cancer prevention trial is viewed by participants as a "men's health trial"; (4) data management and discipline coordination at participating institutions are critical; (5) study design change is commonly required owing to changes in clinical practice over the course of the trial; and (6) training of institutional staff is essential. With proper design, robust data management, and a flexible staff, large-scale randomized chemoprevention trials can be accomplished in the United States. With the extraordinary number of potential agents, it is expected that much will be accomplished with this strategy in the near future.
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Affiliation(s)
- I M Thompson
- Division of Urology, University of Texas Health Sciences Center at San Antonio, San Antonio, Texas, USA.
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