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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] [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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Lin K, Li L, Dai Y, Wang H, Teng S, Bao X, Lu ZJ, Wang D. A comprehensive evaluation of connectivity methods for L1000 data. Brief Bioinform 2019; 21:2194-2205. [DOI: 10.1093/bib/bbz129] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 08/26/2019] [Accepted: 09/14/2019] [Indexed: 01/08/2023] Open
Abstract
Abstract
The methodologies for evaluating similarities between gene expression profiles of different perturbagens are the key to understanding mechanisms of actions (MoAs) of unknown compounds and finding new indications for existing drugs. L1000-based next-generation Connectivity Map (CMap) data is more than a thousand-fold scale-up of the CMap pilot dataset. Although several systematic evaluations have been performed individually to assess the accuracy of the methodologies for the CMap pilot study, the performance of these methodologies needs to be re-evaluated for the L1000 data. Here, using the drug–drug similarities from the Drug Repurposing Hub database as a benchmark standard, we evaluated six popular published methods for the prediction performance of drug–drug relationships based on the partial area under the receiver operating characteristic (ROC) curve at false positive rates of 0.001, 0.005 and 0.01 (AUC0.001, AUC0.005 and AUC0.01). The similarity evaluating algorithm called ZhangScore was generally superior to other methods and exhibited the highest accuracy at the gene signature sizes ranging from 10 to 200. Further, we tested these methods with an experimentally derived gene signature related to estrogen in breast cancer cells, and the results confirmed that ZhangScore was more accurate than other methods. Moreover, based on scoring results of ZhangScore for the gene signature of TOP2A knockdown, in addition to well-known TOP2A inhibitors, we identified a number of potential inhibitors and at least two of them were the subject of previous investigation. Our studies provide potential guidelines for researchers to choose the suitable connectivity method. The six connectivity methods used in this report have been implemented in R package (https://github.com/Jasonlinchina/RCSM).
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Affiliation(s)
- Kequan Lin
- School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Lu Li
- School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Yifei Dai
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Huili Wang
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Shuaishuai Teng
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Xilinqiqige Bao
- International Mongolian Hospital of Inner Mongolia, Hohhot 010065, China
| | - Zhi John Lu
- School of Life Sciences, Tsinghua University, Beijing 100084, China
- Center of Synthetic & Systems Biology, Tsinghua University, Beijing 100084, China
- MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China
| | - Dong Wang
- School of Medicine, Tsinghua University, Beijing 100084, China
- Center of Synthetic & Systems Biology, Tsinghua University, Beijing 100084, China
- National Collaborative Innovation Center for Biotherapy, Tsinghua University, Beijing 100084, China
- School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
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Qin W, Wang X, Zhao H, Lu H. A Novel Joint Gene Set Analysis Framework Improves Identification of Enriched Pathways in Cross Disease Transcriptomic Analysis. Front Genet 2019; 10:293. [PMID: 31031796 PMCID: PMC6473067 DOI: 10.3389/fgene.2019.00293] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 03/19/2019] [Indexed: 12/25/2022] Open
Abstract
Motivation: Gene set enrichment analysis is a widely accepted expression analysis tool which aims at detecting coordinated expression change within a pre-defined gene sets rather than individual genes. The benefit of gene set analysis over individual differentially expressed (DE) gene analysis includes more reproducible and interpretable results and detecting small but consistent change among gene set which could not be detected by DE gene analysis. There have been many successful gene set analysis applications in human diseases. However, when the sample size of a disease study is small and no other public data sets of the same disease are available, it will lead to lack of power to detect pathways of importance to the disease. Results: We have developed a novel joint gene set analysis statistical framework which aims at improving the power of identifying enriched gene sets through integrating multiple similar disease data sets. Through comprehensive simulation studies, we demonstrated that our proposed frameworks obtained much better AUC scores than single data set analysis and another meta-analysis method in identification of enriched pathways. When applied to two real data sets, the proposed framework could retain the enriched gene sets identified by single data set analysis and exclusively obtained up to 200% more disease-related gene sets demonstrating the improved identification power through information shared between similar diseases. We expect that the proposed framework would enable researchers to better explore public data sets when the sample size of their study is limited.
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Affiliation(s)
- Wenyi Qin
- Center for Biomedical Informatics, Shanghai Children's Hospital, Shanghai Jiaotong University, Shanghai, China
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States
- Department of Genetics, School of Medicine, Yale University, New Haven, CT, United States
| | - Xujun Wang
- Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, Shanghai Jiaotong University, Shanghai, China
| | - Hongyu Zhao
- Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, Shanghai Jiaotong University, Shanghai, China
- Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, United States
| | - Hui Lu
- Center for Biomedical Informatics, Shanghai Children's Hospital, Shanghai Jiaotong University, Shanghai, China
- Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States
- Department of Bioinformatics and Biostatistics, SJTU-Yale Joint Center for Biostatistics, Shanghai Jiaotong University, Shanghai, China
- Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, United States
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He YD, Karbowski CM, Werner J, Everds N, Di Palma C, Chen Y, Higgins-Garn M, Tran S, Afshari CA, Hamadeh HK. Common handling procedures conducted in preclinical safety studies result in minimal hepatic gene expression changes in Sprague-Dawley rats. PLoS One 2014; 9:e88750. [PMID: 24551150 PMCID: PMC3925150 DOI: 10.1371/journal.pone.0088750] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2013] [Accepted: 01/12/2014] [Indexed: 11/30/2022] Open
Abstract
Gene expression profiling is a tool to gain mechanistic understanding of adverse effects in response to compound exposure. However, little is known about how the common handling procedures of experimental animals during a preclinical study alter baseline gene expression. We report gene expression changes in the livers of female Sprague-Dawley rats following common handling procedures. Baseline gene expression changes identified in this study provide insight on how these changes may affect interpretation of gene expression profiles following compound exposure. Rats were divided into three groups. One group was not subjected to handling procedures and served as controls for both handled groups. Animals in the other two groups were weighed, subjected to restraint in Broome restrainers, and administered water via oral gavage daily for 1 or 4 days with tail vein blood collections at 1, 2, 4, and 8 hours postdose on days 1 and 4. Significantly altered genes were identified in livers of animals following 1 or 4 days of handling when compared to the unhandled animals. Gene changes in animals handled for 4 days were similar to those handled for 1 day, suggesting a lack of habituation. The altered genes were primarily immune function related genes. These findings, along with a correlating increase in corticosterone levels suggest that common handling procedures may cause a minor immune system perturbance.
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Affiliation(s)
- Yudong D He
- Comparative Biology and Safety Sciences, Amgen Inc., Seattle, Washington, United States of America
| | - Christine M Karbowski
- Comparative Biology and Safety Sciences, Amgen Inc., Thousand Oaks, California, United States of America
| | - Jon Werner
- Comparative Biology and Safety Sciences, Amgen Inc., Thousand Oaks, California, United States of America
| | - Nancy Everds
- Comparative Biology and Safety Sciences, Amgen Inc., Seattle, Washington, United States of America
| | - Chris Di Palma
- Comparative Biology and Safety Sciences, Amgen Inc., Seattle, Washington, United States of America
| | - Yuan Chen
- Comparative Biology and Safety Sciences, Amgen Inc., Thousand Oaks, California, United States of America
| | - Marnie Higgins-Garn
- Comparative Biology and Safety Sciences, Amgen Inc., Thousand Oaks, California, United States of America
| | - Sandra Tran
- Comparative Biology and Safety Sciences, Amgen Inc., Thousand Oaks, California, United States of America
| | - Cynthia A Afshari
- Comparative Biology and Safety Sciences, Amgen Inc., Thousand Oaks, California, United States of America
| | - Hisham K Hamadeh
- Comparative Biology and Safety Sciences, Amgen Inc., Thousand Oaks, California, United States of America
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