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Sarkar S, Anyaso-Samuel S, Qiu P, Datta S. Multiblock partial least squares and rank aggregation: Applications to detection of bacteriophages associated with antimicrobial resistance in the presence of potential confounding factors. Stat Med 2024; 43:2527-2546. [PMID: 38618705 DOI: 10.1002/sim.10058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 01/31/2024] [Accepted: 02/27/2024] [Indexed: 04/16/2024]
Abstract
Urban environments, characterized by bustling mass transit systems and high population density, host a complex web of microorganisms that impact microbial interactions. These urban microbiomes, influenced by diverse demographics and constant human movement, are vital for understanding microbial dynamics. We explore urban metagenomics, utilizing an extensive dataset from the Metagenomics & Metadesign of Subways & Urban Biomes (MetaSUB) consortium, and investigate antimicrobial resistance (AMR) patterns. In this pioneering research, we delve into the role of bacteriophages, or "phages"-viruses that prey on bacteria and can facilitate the exchange of antibiotic resistance genes (ARGs) through mechanisms like horizontal gene transfer (HGT). Despite their potential significance, existing literature lacks a consensus on their significance in ARG dissemination. We argue that they are an important consideration. We uncover that environmental variables, such as those on climate, demographics, and landscape, can obscure phage-resistome relationships. We adjust for these potential confounders and clarify these relationships across specific and overall antibiotic classes with precision, identifying several key phages. Leveraging machine learning tools and validating findings through clinical literature, we uncover novel associations, adding valuable insights to our comprehension of AMR development.
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Affiliation(s)
- Shoumi Sarkar
- Department of Biostatistics, University of Florida, Gainesville, Florida
| | | | - Peihua Qiu
- Department of Biostatistics, University of Florida, Gainesville, Florida
| | - Somnath Datta
- Department of Biostatistics, University of Florida, Gainesville, Florida
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2
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Łabaj PP, Dopazo J, Xiao W, Kreil DP. Editorial: Critical assessment of massive data analysis (CAMDA) annual conference 2021. Front Genet 2023; 14:1154398. [PMID: 36873943 PMCID: PMC9978925 DOI: 10.3389/fgene.2023.1154398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 02/06/2023] [Indexed: 02/18/2023] Open
Affiliation(s)
- Paweł P Łabaj
- Małopolska Centre of Biotechnology, Jagiellonian University, Kraków, Lesser Poland, Poland
| | - Joaquin Dopazo
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, Sevilla, Spain.,Institute of Biomedicine of Seville, IBiS, University Hospital Virgen del Rocío/CSIC/University of Sevilla, Sevilla, Spain
| | - Wenzhong Xiao
- Genome Technology Center, School of Medicine, Stanford University, Palo Alto, CA, United States.,Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - David P Kreil
- Department of Biotechnology, Boku University Vienna, Vienna, Austria
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Kańduła MM, Aldoshin AD, Singh S, Kolaczyk ED, Kreil D. ViLoN-a multi-layer network approach to data integration demonstrated for patient stratification. Nucleic Acids Res 2022; 51:e6. [PMID: 36395816 PMCID: PMC9841426 DOI: 10.1093/nar/gkac988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 10/11/2022] [Accepted: 11/02/2022] [Indexed: 11/19/2022] Open
Abstract
With more and more data being collected, modern network representations exploit the complementary nature of different data sources as well as similarities across patients. We here introduce the Variation of information fused Layers of Networks algorithm (ViLoN), a novel network-based approach for the integration of multiple molecular profiles. As a key innovation, it directly incorporates prior functional knowledge (KEGG, GO). In the constructed network of patients, patients are represented by networks of pathways, comprising genes that are linked by common functions and joint regulation in the disease. Patient stratification remains a key challenge both in the clinic and for research on disease mechanisms and treatments. We thus validated ViLoN for patient stratification on multiple data type combinations (gene expression, methylation, copy number), showing substantial improvements and consistently competitive performance for all. Notably, the incorporation of prior functional knowledge was critical for good results in the smaller cohorts (rectum adenocarcinoma: 90, esophageal carcinoma: 180), where alternative methods failed.
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Affiliation(s)
- Maciej M Kańduła
- Institute of Molecular Biotechnology, Boku University Vienna, Austria,Janssen Pharmaceutica NV, Beerse, Belgium
| | | | - Swati Singh
- Institute of Molecular Biotechnology, Boku University Vienna, Austria,Department of Biological Sciences and Bioengineering, Indian Institute of Technology Kanpur, Kanpur, India
| | - Eric D Kolaczyk
- Correspondence may also be addressed to Eric D. Kolaczyk. Tel: +1 514 398 3805;
| | - David P Kreil
- To whom correspondence should be addressed. Tel: +43 1 47654 79009;
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Mangul S, Martin LS, Hill BL, Lam AKM, Distler MG, Zelikovsky A, Eskin E, Flint J. Systematic benchmarking of omics computational tools. Nat Commun 2019; 10:1393. [PMID: 30918265 PMCID: PMC6437167 DOI: 10.1038/s41467-019-09406-4] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Accepted: 03/06/2019] [Indexed: 01/11/2023] Open
Abstract
Computational omics methods packaged as software have become essential to modern biological research. The increasing dependence of scientists on these powerful software tools creates a need for systematic assessment of these methods, known as benchmarking. Adopting a standardized benchmarking practice could help researchers who use omics data to better leverage recent technological innovations. Our review summarizes benchmarking practices from 25 recent studies and discusses the challenges, advantages, and limitations of benchmarking across various domains of biology. We also propose principles that can make computational biology benchmarking studies more sustainable and reproducible, ultimately increasing the transparency of biomedical data and results. Benchmarking studies are important for comprehensively understanding and evaluating different computational omics methods. Here, the authors review practices from 25 recent studies and propose principles to improve the quality of benchmarking studies.
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Affiliation(s)
- Serghei Mangul
- Department of Computer Science, University of California Los Angeles, 580 Portola Plaza, Los Angeles, CA, 90095, USA. .,Institute for Quantitative and Computational Biosciences, University of California Los Angeles, 611 Charles E Young Drive East, Los Angeles, CA, 90095, USA.
| | - Lana S Martin
- Institute for Quantitative and Computational Biosciences, University of California Los Angeles, 611 Charles E Young Drive East, Los Angeles, CA, 90095, USA
| | - Brian L Hill
- Department of Computer Science, University of California Los Angeles, 580 Portola Plaza, Los Angeles, CA, 90095, USA
| | - Angela Ka-Mei Lam
- Department of Computer Science, University of California Los Angeles, 580 Portola Plaza, Los Angeles, CA, 90095, USA
| | - Margaret G Distler
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Alex Zelikovsky
- Department of Computer Science, Georgia State University, Atlanta, GA, 30303, USA.,The Laboratory of Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Eleazar Eskin
- Department of Computer Science, University of California Los Angeles, 580 Portola Plaza, Los Angeles, CA, 90095, USA.,Department of Human Genetics, University of California Los Angeles, 695 Charles E. Young, Los Angeles, CA, USA
| | - Jonathan Flint
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
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Friedberg I, Wass MN, Mooney SD, Radivojac P. Ten simple rules for a community computational challenge. PLoS Comput Biol 2015; 11:e1004150. [PMID: 25906249 PMCID: PMC4408123 DOI: 10.1371/journal.pcbi.1004150] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Affiliation(s)
- Iddo Friedberg
- Department of Microbiology, Miami University, Oxford, Ohio, United States of America
- Department of Computer Science and Software Engineering, Miami University, Oxford, Ohio, United States of America
- * E-mail:
| | - Mark N. Wass
- Centre for Molecular Processing, School of Biosciences, University of Kent, Canterbury, Kent, United Kingdom
| | - Sean D. Mooney
- The Buck Institute for Research on Aging, Novato, California, United States of America
| | - Predrag Radivojac
- Department of Computer Science and Informatics, Indiana University, Bloomington, Indiana, United States of America
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Pennie WD, Kimber I. Toxicogenomics; transcript profiling and potential application to chemical allergy. Toxicol In Vitro 2002; 16:319-26. [PMID: 12020605 DOI: 10.1016/s0887-2333(02)00007-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Novel transcript profiling technologies allow simultaneous measurement of the changes in expression of many hundreds or many thousands of genes. The availability of these methods has brought about revolutionary changes in many areas of investigative biology, where analyses of patterns of gene expression, rather than of individual genes, are being employed. The application of these technologies to toxicology (toxicogenomics) offers new opportunities for both mechanistic toxicity research and predictive toxicology. Here we provide an overview of the basic approaches used in this field. The development of a series of toxicology-specific microarrays in our own laboratory is discussed, together with an example of one area of mechanistic research, chemical allergy, where we believe judicious application of toxicogenomics will make an important contribution.
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Affiliation(s)
- W D Pennie
- Syngenta Central Toxicology Laboratory, Alderley Park, Macclesfield SK10 4TJ, UK
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