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Majumder MA, Leek JT, Hansen KD, Razi A, McGuire AL. Large-scale genotype prediction from RNA sequence data necessitates a new ethical and policy framework. Nat Genet 2024:10.1038/s41588-024-01825-4. [PMID: 39039279 DOI: 10.1038/s41588-024-01825-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Affiliation(s)
- Mary A Majumder
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, USA.
| | - Jeffrey T Leek
- Biostatistics Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Kasper D Hansen
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Afrooz Razi
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Amy L McGuire
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, USA
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2
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Palmer EE, Cederroth H, Cederroth M, Delgado-Vega AM, Roberts N, Taylan F, Nordgren A, Botto LD. Equity in action: The Diagnostic Working Group of The Undiagnosed Diseases Network International. NPJ Genom Med 2024; 9:37. [PMID: 38965249 PMCID: PMC11224220 DOI: 10.1038/s41525-024-00422-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 05/29/2024] [Indexed: 07/06/2024] Open
Abstract
Rare diseases are recognized as a global public health priority. A timely and accurate diagnosis is a critical enabler for precise and personalized health care. However, barriers to rare disease diagnoses are especially steep for those from historically underserved communities, including low- and middle-income countries. The Undiagnosed Diseases Network International (UDNI) was launched in 2015 to help fill the knowledge gaps that impede diagnosis for rare diseases, and to foster the translation of research into medical practice, aided by active patient involvement. To better pursue these goals, in 2021 the UDNI established the Diagnostic Working Group of the UDNI (UDNI DWG) as a community of practice that would (a) accelerate diagnoses for more families; (b) support and share knowledge and skills by developing Undiagnosed Diseases Programs, particularly those in lower resource areas; and (c) promote discovery and expand global medical knowledge. This Perspectives article documents the initial establishment and iterative co-design of the UDNI DWG.
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Affiliation(s)
- Elizabeth Emma Palmer
- Discipline of Paediatrics and Child Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia.
- Centre for Clinical Genetics, Sydney Childrens' Hospitals Network, Sydney, NSW, Australia.
| | | | | | - Angelica Maria Delgado-Vega
- Department of Molecular Medicine and Surgery, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics and Genomics, Karolinska University Hospital, Stockholm, Sweden
| | - Natalie Roberts
- Discipline of Paediatrics and Child Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
| | - Fulya Taylan
- Department of Molecular Medicine and Surgery, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics and Genomics, Karolinska University Hospital, Stockholm, Sweden
| | - Ann Nordgren
- Department of Molecular Medicine and Surgery, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics and Genomics, Karolinska University Hospital, Stockholm, Sweden
- Institute of Biomedicine, Department of Laboratory Medicine, University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Lorenzo D Botto
- Division of Medical Genetics, Department of Pediatrics, University of Utah, Salt Lake City, Utah, USA
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3
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Dingemans AJM, Jansen S, van Reeuwijk J, de Leeuw N, Pfundt R, Schuurs-Hoeijmakers J, van Bon BW, Marcelis C, Ockeloen CW, Willemsen M, van der Sluijs PJ, Santen GWE, Kooy RF, Vulto-van Silfhout AT, Kleefstra T, Koolen DA, Vissers LELM, de Vries BBA. Prevalence of comorbidities in individuals with neurodevelopmental disorders from the aggregated phenomics data of 51,227 pediatric individuals. Nat Med 2024; 30:1994-2003. [PMID: 38745008 DOI: 10.1038/s41591-024-03005-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 04/16/2024] [Indexed: 05/16/2024]
Abstract
The prevalence of comorbidities in individuals with neurodevelopmental disorders (NDDs) is not well understood, yet these are important for accurate diagnosis and prognosis in routine care and for characterizing the clinical spectrum of NDD syndromes. We thus developed PhenomAD-NDD, an aggregated database containing the comorbid phenotypic data of 51,227 individuals with NDD, all harmonized into Human Phenotype Ontology (HPO), with in total 3,054 unique HPO terms. We demonstrate that almost all congenital anomalies are more prevalent in the NDD population than in the general population, and the NDD baseline prevalence allows for an approximation of the enrichment of symptoms. For example, such analyses of 33 genetic NDDs show that 32% of enriched phenotypes are currently not reported in the clinical synopsis in the Online Mendelian Inheritance in Man (OMIM). PhenomAD-NDD is open to all via a visualization online tool and allows us to determine the enrichment of symptoms in NDD.
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Affiliation(s)
- Alexander J M Dingemans
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Sandra Jansen
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jeroen van Reeuwijk
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Nicole de Leeuw
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Rolph Pfundt
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Janneke Schuurs-Hoeijmakers
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Bregje W van Bon
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Carlo Marcelis
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Charlotte W Ockeloen
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Marjolein Willemsen
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | | | - Gijs W E Santen
- Department of Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - R Frank Kooy
- Department of Medical Genetics, University of Antwerp, Antwerp, Belgium
| | - Anneke T Vulto-van Silfhout
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Tjitske Kleefstra
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - David A Koolen
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Lisenka E L M Vissers
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Bert B A de Vries
- Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands.
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4
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Jin YW, Hu P, Liu Q. NNICE: a deep quantile neural network algorithm for expression deconvolution. Sci Rep 2024; 14:14040. [PMID: 38890415 PMCID: PMC11189483 DOI: 10.1038/s41598-024-65053-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 06/17/2024] [Indexed: 06/20/2024] Open
Abstract
The composition of cell-type is a key indicator of health. Advancements in bulk gene expression data curation, single cell RNA-sequencing technologies, and computational deconvolution approaches offer a new perspective to learn about the composition of different cell types in a quick and affordable way. In this study, we developed a quantile regression and deep learning-based method called Neural Network Immune Contexture Estimator (NNICE) to estimate the cell type abundance and its uncertainty by automatically deconvolving bulk RNA-seq data. The proposed NNICE model was able to successfully recover ground-truth cell type fraction values given unseen bulk mixture gene expression profiles from the same dataset it was trained on. Compared with baseline methods, NNICE achieved better performance on deconvolve both pseudo-bulk gene expressions (Pearson correlation R = 0.9) and real bulk gene expression data (Pearson correlation R = 0.9) across all cell types. In conclusion, NNICE combines statistic inference with deep learning to provide accurate and interpretable cell type deconvolution from bulk gene expression.
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Affiliation(s)
- Yong Won Jin
- Department of Biochemistry & Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, R3E 0J9, Canada
| | - Pingzhao Hu
- Department of Biochemistry & Medical Genetics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, R3E 0J9, Canada
- Department of Biochemistry, Schulich School of Medicine & Dentistry, Western University, London, ON, N6A 5C1, Canada
| | - Qian Liu
- Department of Applied Computer Science, University of Winnipeg, Winnipeg, MB, R3B 2E9, Canada.
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5
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Zhang J, Liu Y, Thabane L, Li J, Bai X, Li L, Lip GYH, Sun X, Xia M, Van Spall HGC, Li G. Journal requirement for data sharing statements in clinical trials: a cross-sectional study. J Clin Epidemiol 2024; 172:111405. [PMID: 38838963 DOI: 10.1016/j.jclinepi.2024.111405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 05/24/2024] [Accepted: 05/27/2024] [Indexed: 06/07/2024]
Abstract
OBJECTIVES Data sharing statements are considered routine in clinical trial reporting and represent a step toward data transparency. The International Committee of Medical Journal Editors (ICMJE) required clinical trials to publish data sharing statements. We aimed to assess the requirement for data sharing statements of individual participant data by biomedical journals and explore associations between journal characteristics and journal requirements for data sharing statements. STUDY DESIGN AND SETTING In this cross-sectional study, we included all biomedical journals that published clinical trials from January 1, 2019, to December 31, 2022, and that were indexed by the Journal Citation Reports. The study outcome was the journal requirement for data sharing statements. Multivariable logistic regression analysis was used to assess the relationship between journal characteristics and requirement for data sharing statements. RESULTS Of the 3229 biomedical journals included in the analysis, 2345 (72.6%) required authors to include data sharing statements. Journals published in the UK (OR, 3.19 [95% CI, 2.43-4.22]) and endorsing the Consolidated Standards of Reporting Trials (OR, 3.30 [95% CI, 2.78-3.92]) had greater odds of requiring data sharing statements. Journals that were open access, non-English language, in the Journal Citation Reports group of clinical medicine, and on the ICMJE list had lower odds of requiring data sharing statements, with ORs ranging from 0.18 to 0.81. CONCLUSION Despite ICMJE recommendations, more than 27% of the biomedical journals that published clinical trials did not require clinical trials to include data sharing statements, highlighting room for improved transparency.
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Affiliation(s)
- Jingyi Zhang
- Center for Clinical Epidemiology and Methodology (CCEM), The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, China
| | - Yingxin Liu
- Center for Clinical Epidemiology and Methodology (CCEM), The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, China
| | - Lehana Thabane
- Department of Health Research Methods, Evidence, and Impact (HEI), McMaster University, Hamilton, ON, Canada; Father Sean O'Sullivan Research Centre, St Joseph's Healthcare Hamilton, Hamilton, ON, Canada; Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa
| | - Jianfeng Li
- Department of Epidemiology and Health Statistics, School of Public Health, Guangdong Medical University, Dongguan, China
| | - Xuerui Bai
- Department of Epidemiology, School of Medicine, Jinan University, Guangzhou, China
| | - Likang Li
- Center for Clinical Epidemiology and Methodology (CCEM), The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, China
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Sciences at University of Liverpool, Liverpool John Moores University, Liverpool Heart & Chest Hospital, Liverpool, United Kingdom; Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Xin Sun
- Institute of Integrated Traditional Chinese and Western Medicine and Chinese Evidence-Based Medicine Center and Cochrane China Center, West China Hospital, Sichuan University, Chengdu, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, Sichuan, China; Sichuan Center of Technology Innovation for Real World Data, Chengdu, Sichuan, China
| | - Min Xia
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, and Department of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Harriette G C Van Spall
- Department of Health Research Methods, Evidence, and Impact (HEI), McMaster University, Hamilton, ON, Canada; Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Guowei Li
- Center for Clinical Epidemiology and Methodology (CCEM), The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, China; Father Sean O'Sullivan Research Centre, St Joseph's Healthcare Hamilton, Hamilton, ON, Canada.
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6
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Kleverov M, Zenkova D, Kamenev V, Sablina M, Artyomov MN, Sergushichev AA. Phantasus, a web application for visual and interactive gene expression analysis. eLife 2024; 13:e85722. [PMID: 38742735 PMCID: PMC11147506 DOI: 10.7554/elife.85722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 05/13/2024] [Indexed: 05/16/2024] Open
Abstract
Transcriptomic profiling became a standard approach to quantify a cell state, which led to the accumulation of huge amount of public gene expression datasets. However, both reuse of these datasets or analysis of newly generated ones requires significant technical expertise. Here, we present Phantasus: a user-friendly web application for interactive gene expression analysis which provides a streamlined access to more than 96,000 public gene expression datasets, as well as allows analysis of user-uploaded datasets. Phantasus integrates an intuitive and highly interactive JavaScript-based heatmap interface with an ability to run sophisticated R-based analysis methods. Overall Phantasus allows users to go all the way from loading, normalizing, and filtering data to doing differential gene expression and downstream analysis. Phantasus can be accessed online at https://alserglab.wustl.edu/phantasus or can be installed locally from Bioconductor (https://bioconductor.org/packages/phantasus). Phantasus source code is available at https://github.com/ctlab/phantasus under an MIT license.
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Affiliation(s)
- Maksim Kleverov
- ITMO University, Computer Technologies LaboratorySaint PetersburgRussian Federation
- Washington University in St. Louis School of Medicine, Department of Pathology and ImmunologySt LouisUnited States
| | - Daria Zenkova
- ITMO University, Computer Technologies LaboratorySaint PetersburgRussian Federation
| | - Vladislav Kamenev
- ITMO University, Computer Technologies LaboratorySaint PetersburgRussian Federation
| | - Margarita Sablina
- ITMO University, Computer Technologies LaboratorySaint PetersburgRussian Federation
| | - Maxim N Artyomov
- Washington University in St. Louis School of Medicine, Department of Pathology and ImmunologySt LouisUnited States
| | - Alexey A Sergushichev
- ITMO University, Computer Technologies LaboratorySaint PetersburgRussian Federation
- Washington University in St. Louis School of Medicine, Department of Pathology and ImmunologySt LouisUnited States
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7
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Lu B, Chen X, Xavier Castellanos F, Thompson PM, Zuo XN, Zang YF, Yan CG. The power of many brains: Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration. Sci Bull (Beijing) 2024; 69:1536-1555. [PMID: 38519398 DOI: 10.1016/j.scib.2024.03.006] [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: 08/17/2023] [Revised: 12/12/2023] [Accepted: 02/27/2024] [Indexed: 03/24/2024]
Abstract
Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection of subtle abnormalities and robust associations, and fostering new research methods. Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment. Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies. We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders. However, challenges such as data harmonization across different sites, privacy protection, and effective data sharing must be addressed. With proper governance and open science practices, we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis, treatment selection, and outcome prediction, contributing to optimal brain health.
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Affiliation(s)
- Bin Lu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Xiao Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China
| | - Francisco Xavier Castellanos
- Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, New York 10016, USA; Nathan Kline Institute for Psychiatric Research, Orangeburg 10962, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles 90033, USA
| | - Xi-Nian Zuo
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; National Basic Science Data Center, Beijing 100190, China
| | - Yu-Feng Zang
- Centre for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou 310004, China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou 310030, China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairment, Hangzhou 311121, China
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China; International Big-Data Center for Depression Research, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.
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8
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Alper P, Dĕd V, Herzinger S, Grouès V, Peter S, Lebioda J, Ebermann L, Popleteeva M, Barry ND, Welter D, Ghosh S, Becker R, Schneider R, Gu W, Trefois C, Satagopam V. DS-PACK: Tool assembly for the end-to-end support of controlled access human data sharing. Sci Data 2024; 11:501. [PMID: 38750048 PMCID: PMC11096168 DOI: 10.1038/s41597-024-03326-9] [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: 10/03/2023] [Accepted: 04/29/2024] [Indexed: 05/18/2024] Open
Abstract
The EU General Data Protection Regulation (GDPR) requirements have prompted a shift from centralised controlled access genome-phenome archives to federated models for sharing sensitive human data. In a data-sharing federation, a central node facilitates data discovery; meanwhile, distributed nodes are responsible for handling data access requests, concluding agreements with data users and providing secure access to the data. Research institutions that want to become part of such federations often lack the resources to set up the required controlled access processes. The DS-PACK tool assembly is a reusable, open-source middleware solution that semi-automates controlled access processes end-to-end, from data submission to access. Data protection principles are engraved into all components of the DS-PACK assembly. DS-PACK centralises access control management and distributes access control enforcement with support for data access via cloud-based applications. DS-PACK is in production use at the ELIXIR Luxembourg data hosting platform, combined with an operational model including legal facilitation and data stewardship.
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Affiliation(s)
- Pinar Alper
- Luxembourg National Data Service, PNED GIE, Esch-sur-Alzette, L-4362, Luxembourg.
- ELIXIR Luxembourg, Belvaux, Luxembourg.
| | - Vilém Dĕd
- ELIXIR Luxembourg, Belvaux, Luxembourg
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, L-4367, Luxembourg
| | - Sascha Herzinger
- ELIXIR Luxembourg, Belvaux, Luxembourg
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, L-4367, Luxembourg
| | - Valentin Grouès
- ELIXIR Luxembourg, Belvaux, Luxembourg
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, L-4367, Luxembourg
| | - Sarah Peter
- ELIXIR Luxembourg, Belvaux, Luxembourg
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, L-4367, Luxembourg
| | - Jacek Lebioda
- Luxembourg National Data Service, PNED GIE, Esch-sur-Alzette, L-4362, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
| | - Linda Ebermann
- Luxembourg National Data Service, PNED GIE, Esch-sur-Alzette, L-4362, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
| | - Marina Popleteeva
- ELIXIR Luxembourg, Belvaux, Luxembourg
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, L-4367, Luxembourg
| | - Nene Djenaba Barry
- Luxembourg National Data Service, PNED GIE, Esch-sur-Alzette, L-4362, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
| | - Danielle Welter
- Luxembourg National Data Service, PNED GIE, Esch-sur-Alzette, L-4362, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
| | - Soumyabrata Ghosh
- ELIXIR Luxembourg, Belvaux, Luxembourg
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, L-4367, Luxembourg
| | - Regina Becker
- Luxembourg National Data Service, PNED GIE, Esch-sur-Alzette, L-4362, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
| | - Reinhard Schneider
- ELIXIR Luxembourg, Belvaux, Luxembourg
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, L-4367, Luxembourg
| | - Wei Gu
- Luxembourg National Data Service, PNED GIE, Esch-sur-Alzette, L-4362, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
| | - Christophe Trefois
- Luxembourg National Data Service, PNED GIE, Esch-sur-Alzette, L-4362, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
| | - Venkata Satagopam
- ELIXIR Luxembourg, Belvaux, Luxembourg.
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, L-4367, Luxembourg.
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9
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Tadi AA, Alhadidi D, Rueda L. PPPCT: Privacy-Preserving framework for Parallel Clustering Transcriptomics data. Comput Biol Med 2024; 173:108351. [PMID: 38520921 DOI: 10.1016/j.compbiomed.2024.108351] [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: 08/28/2023] [Revised: 03/18/2024] [Accepted: 03/18/2024] [Indexed: 03/25/2024]
Abstract
Single-cell transcriptomics data provides crucial insights into patients' health, yet poses significant privacy concerns. Genomic data privacy attacks can have deep implications, encompassing not only the patients' health information but also extending widely to compromise their families'. Moreover, the permanence of leaked data exacerbates the challenges, making retraction an impossibility. While extensive efforts have been directed towards clustering single-cell transcriptomics data, addressing critical challenges, especially in the realm of privacy, remains pivotal. This paper introduces an efficient, fast, privacy-preserving approach for clustering single-cell RNA-sequencing (scRNA-seq) datasets. The key contributions include ensuring data privacy, achieving high-quality clustering, accommodating the high dimensionality inherent in the datasets, and maintaining reasonable computation time for big-scale datasets. Our proposed approach utilizes the map-reduce scheme to parallelize clustering, addressing intensive calculation challenges. Intel Software Guard eXtension (SGX) processors are used to ensure the security of sensitive code and data during processing. Additionally, the approach incorporates a logarithm transformation as a preprocessing step, employs non-negative matrix factorization for dimensionality reduction, and utilizes parallel k-means for clustering. The approach fully leverages the computing capabilities of all processing resources within a secure private cloud environment. Experimental results demonstrate the efficacy of our approach in preserving patient privacy while surpassing state-of-the-art methods in both clustering quality and computation time. Our method consistently achieves a minimum of 7% higher Adjusted Rand Index (ARI) than existing approaches, contingent on dataset size. Additionally, due to parallel computations and dimensionality reduction, our approach exhibits efficiency, converging to very good results in less than 10 seconds for a scRNA-seq dataset with 5000 genes and 6000 cells when prioritizing privacy and under two seconds without privacy considerations. Availability and implementation Code and datasets availability: https://github.com/University-of-Windsor/PPPCT.
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Affiliation(s)
- Ali Abbasi Tadi
- University of Windsor, 401 Sunset Ave, Windsor, N9B 3P4, Ontario, Canada.
| | - Dima Alhadidi
- University of Windsor, 401 Sunset Ave, Windsor, N9B 3P4, Ontario, Canada
| | - Luis Rueda
- University of Windsor, 401 Sunset Ave, Windsor, N9B 3P4, Ontario, Canada
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10
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Lawson J, Rahimzadeh V, Baek J, Dove ES. Achieving Procedural Parity in Managing Access to Genomic and Related Health Data: A Global Survey of Data Access Committee Members. Biopreserv Biobank 2024; 22:123-129. [PMID: 37192473 DOI: 10.1089/bio.2022.0205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2023] Open
Abstract
Data access committees (DACs) are critical players in the data sharing ecosystem. DACs review requests for access to data held in one or more repositories and where specific constraints determine how the data may be used and by whom. Our team surveyed DAC members affiliated with genomic data repositories worldwide to understand standard processes and procedures, operational metrics, bottlenecks, and efficiencies, as well as their perspectives on possible improvements to quality review. We found that DAC operations and systemic issues were common across repositories globally. In general, DAC members endeavored to achieve an appropriate balance of review efficiency, quality, and compliance. Our results suggest a similarly proportionate path forward that helps DACs pursue mutual improvements to efficiency and compliance without sacrificing review quality.
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Affiliation(s)
- Jonathan Lawson
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Vasiliki Rahimzadeh
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, Texas, USA
| | - Jinyoung Baek
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Edward S Dove
- School of Law, University of Edinburgh, Edinburgh, United Kingdom
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11
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Abondio P, Bruno F, Passarino G, Montesanto A, Luiselli D. Pangenomics: A new era in the field of neurodegenerative diseases. Ageing Res Rev 2024; 94:102180. [PMID: 38163518 DOI: 10.1016/j.arr.2023.102180] [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: 09/07/2023] [Revised: 12/14/2023] [Accepted: 12/28/2023] [Indexed: 01/03/2024]
Abstract
A pangenome is composed of all the genetic variability of a group of individuals, and its application to the study of neurodegenerative diseases may provide valuable insights into the underlying aspects of genetic heterogenetiy for these complex ailments, including gene expression, epigenetics, and translation mechanisms. Furthermore, a reference pangenome allows for the identification of previously undetected structural commonalities and differences among individuals, which may help in the diagnosis of a disease, support the prediction of what will happen over time (prognosis) and aid in developing novel treatments in the perspective of personalized medicine. Therefore, in the present review, the application of the pangenome concept to the study of neurodegenerative diseases will be discussed and analyzed for its potential to enable an improvement in diagnosis and prognosis for these illnesses, leading to the development of tailored treatments for individual patients from the knowledge of the genomic composition of a whole population.
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Affiliation(s)
- Paolo Abondio
- Laboratory of Ancient DNA, Department of Cultural Heritage, University of Bologna, Via degli Ariani 1, 48121 Ravenna, Italy.
| | - Francesco Bruno
- Academy of Cognitive Behavioral Sciences of Calabria (ASCoC), Lamezia Terme, Italy; Regional Neurogenetic Centre (CRN), Department of Primary Care, Azienda Sanitaria Provinciale Di Catanzaro, Viale A. Perugini, 88046 Lamezia Terme, CZ, Italy; Association for Neurogenetic Research (ARN), Lamezia Terme, CZ, Italy
| | - Giuseppe Passarino
- Department of Biology, Ecology and Earth Sciences, University of Calabria, Rende 87036, Italy
| | - Alberto Montesanto
- Department of Biology, Ecology and Earth Sciences, University of Calabria, Rende 87036, Italy
| | - Donata Luiselli
- Laboratory of Ancient DNA, Department of Cultural Heritage, University of Bologna, Via degli Ariani 1, 48121 Ravenna, Italy
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12
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Yang Z, Guan F, Bronk L, Zhao L. Multi-omics approaches for biomarker discovery in predicting the response of esophageal cancer to neoadjuvant therapy: A multidimensional perspective. Pharmacol Ther 2024; 254:108591. [PMID: 38286161 DOI: 10.1016/j.pharmthera.2024.108591] [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: 08/23/2023] [Revised: 12/02/2023] [Accepted: 01/04/2024] [Indexed: 01/31/2024]
Abstract
Neoadjuvant chemoradiotherapy (NCRT) followed by surgery has been established as the standard treatment strategy for operable locally advanced esophageal cancer (EC). However, achieving pathologic complete response (pCR) or near pCR to NCRT is significantly associated with a considerable improvement in survival outcomes, while pCR patients may help organ preservation for patients by active surveillance to avoid planned surgery. Thus, there is an urgent need for improved biomarkers to predict EC chemoradiation response in research and clinical settings. Advances in multiple high-throughput technologies such as next-generation sequencing have facilitated the discovery of novel predictive biomarkers, specifically based on multi-omics data, including genomic/transcriptomic sequencings and proteomic/metabolomic mass spectra. The application of multi-omics data has shown the benefits in improving the understanding of underlying mechanisms of NCRT sensitivity/resistance in EC. Particularly, the prominent development of artificial intelligence (AI) has introduced a new direction in cancer research. The integration of multi-omics data has significantly advanced our knowledge of the disease and enabled the identification of valuable biomarkers for predicting treatment response from diverse dimension levels, especially with rapid advances in biotechnological and AI methodologies. Herein, we summarize the current status of research on the use of multi-omics technologies in predicting NCRT response for EC patients. Current limitations, challenges, and future perspectives of these multi-omics platforms will be addressed to assist in experimental designs and clinical use for further integrated analysis.
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Affiliation(s)
- Zhi Yang
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, 15 West Changle Road, Xi'an, China
| | - Fada Guan
- Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, CT 06510, United States of America
| | - Lawrence Bronk
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States of America
| | - Lina Zhao
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, 15 West Changle Road, Xi'an, China.
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13
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Hamilton DG, Page MJ, Everitt S, Fraser H, Fidler F. Cancer researchers' experiences with and perceptions of research data sharing: Results of a cross-sectional survey. Account Res 2024:1-28. [PMID: 38299475 DOI: 10.1080/08989621.2024.2308606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 01/18/2024] [Indexed: 02/02/2024]
Abstract
BACKGROUND Despite wide recognition of the benefits of sharing research data, public availability rates have not increased substantially in oncology or medicine more broadly over the last decade. METHODS We surveyed 285 cancer researchers to determine their prior experience with sharing data and views on known drivers and inhibitors. RESULTS We found that 45% of respondents had shared some data from their most recent empirical publication, with respondents who typically studied non-human research participants, or routinely worked with human genomic data, more likely to share than those who did not. A third of respondents added that they had previously shared data privately, with 74% indicating that doing so had also led to authorship opportunities or future collaborations for them. Journal and funder policies were reported to be the biggest general drivers toward sharing, whereas commercial interests, agreements with industrial sponsors and institutional policies were the biggest prohibitors. We show that researchers' decisions about whether to share data are also likely to be influenced by participants' desires. CONCLUSIONS Our survey suggests that increased promotion and support by research institutions, alongside greater championing of data sharing by journals and funders, may motivate more researchers in oncology to share their data.
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Affiliation(s)
- Daniel G Hamilton
- MetaMelb Research Group, School of BioSciences, University of Melbourne, Melbourne, Australia
- Melbourne Medical School, Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Melbourne, Australia
| | - Matthew J Page
- Methods in Evidence Synthesis Unit, School of Public Health & Preventive Medicine, Monash University, Melbourne, Australia
| | - Sarah Everitt
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Hannah Fraser
- MetaMelb Research Group, School of BioSciences, University of Melbourne, Melbourne, Australia
| | - Fiona Fidler
- MetaMelb Research Group, School of BioSciences, University of Melbourne, Melbourne, Australia
- School of History & Philosophy of Sciences, University of Melbourne, Melbourne, Australia
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14
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Tan JK, Awuah WA, Roy S, Ferreira T, Ahluwalia A, Guggilapu S, Javed M, Asyura MMAZ, Adebusoye FT, Ramamoorthy K, Paoletti E, Abdul-Rahman T, Prykhodko O, Ovechkin D. Exploring the advances of single-cell RNA sequencing in thyroid cancer: a narrative review. Med Oncol 2023; 41:27. [PMID: 38129369 PMCID: PMC10739406 DOI: 10.1007/s12032-023-02260-x] [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: 08/04/2023] [Accepted: 11/16/2023] [Indexed: 12/23/2023]
Abstract
Thyroid cancer, a prevalent form of endocrine malignancy, has witnessed a substantial increase in occurrence in recent decades. To gain a comprehensive understanding of thyroid cancer at the single-cell level, this narrative review evaluates the applications of single-cell RNA sequencing (scRNA-seq) in thyroid cancer research. ScRNA-seq has revolutionised the identification and characterisation of distinct cell subpopulations, cell-to-cell communications, and receptor interactions, revealing unprecedented heterogeneity and shedding light on novel biomarkers for therapeutic discovery. These findings aid in the construction of predictive models on disease prognosis and therapeutic efficacy. Altogether, scRNA-seq has deepened our understanding of the tumour microenvironment immunologic insights, informing future studies in the development of effective personalised treatment for patients. Challenges and limitations of scRNA-seq, such as technical biases, financial barriers, and ethical concerns, are discussed. Advancements in computational methods, the advent of artificial intelligence (AI), machine learning (ML), and deep learning (DL), and the importance of single-cell data sharing and collaborative efforts are highlighted. Future directions of scRNA-seq in thyroid cancer research include investigating intra-tumoral heterogeneity, integrating with other omics technologies, exploring the non-coding RNA landscape, and studying rare subtypes. Overall, scRNA-seq has transformed thyroid cancer research and holds immense potential for advancing personalised therapies and improving patient outcomes. Efforts to make this technology more accessible and cost-effective will be crucial to ensuring its widespread utilisation in healthcare.
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Affiliation(s)
| | | | - Sakshi Roy
- School of Medicine, Queen's University Belfast, Belfast, UK
| | - Tomas Ferreira
- School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | | | - Saibaba Guggilapu
- Faculty of Medicine, Bangalore Medical College and Research Institute, Bengaluru, India
| | - Mahnoor Javed
- School of Medicine, The University of Nottingham, Nottingham, NG7 2UH, UK
| | | | | | | | - Emma Paoletti
- Faculty of Medicine, University of Manchester, Manchester, M13 9WJ, UK
| | | | - Olha Prykhodko
- Faculty of Medicine, Sumy State University, Sumy, Ukraine
| | - Denys Ovechkin
- Faculty of Medicine, Sumy State University, Sumy, Ukraine
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15
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Lorenzo D, Esquerda M, Bofarull M, Cusi V, Roig H, Bertran J, Carrera J, Torralba F, Cambra FJ, Vila M, Garriga M, Palau F. The reuse of genetic information in research and informed consent. Eur J Hum Genet 2023; 31:1393-1397. [PMID: 37699995 PMCID: PMC10689789 DOI: 10.1038/s41431-023-01457-y] [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: 12/05/2022] [Revised: 05/28/2023] [Accepted: 08/31/2023] [Indexed: 09/14/2023] Open
Abstract
Important advances in genetics research have been made in recent years. Such advances have facilitated the availability of huge amounts of genetic information that could potentially be reused beyond the original purpose for which such information was obtained. Any such reuse must meet certain ethical criteria to ensure that the dignity, integrity, and autonomy of the individual from whom that information was obtained are protected. The aim of this paper is to reflect on these criteria through a critical analysis of the literature. To guarantee these values, ethical criteria need to be established in several respects. For instance, the question must be posed whether the information requires special attention and protection (so-called genetic exceptionalism). Another aspect to bear in mind is the most appropriate type of consent to be given by the person involved, on the one hand favouring research and the reuse of genetic information while on the other protecting the autonomy of that person. Finally, there is a need to determine what protection such reuse should have in order to avoid detrimental consequences and protect the rights of the individual. The main conclusions are that genetic information requires special care and protection (genetic exceptionalism) and that broad consent is the most practical and trustworthy type of consent for the reuse of genetic information.
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Affiliation(s)
- David Lorenzo
- Institut Borja de Bioetica, Universitat Ramon Llull, Barcelona, Spain
- EUI San Joan de Deu, Barcelona, Spain
| | - Montse Esquerda
- Institut Borja de Bioetica, Universitat Ramon Llull, Barcelona, Spain.
- Sant Joan de Deu Terres de Lleida, Lleida, Spain.
| | | | - Victoria Cusi
- Institut Borja de Bioetica, Universitat Ramon Llull, Barcelona, Spain
| | - Helena Roig
- Institut Borja de Bioetica, Universitat Ramon Llull, Barcelona, Spain
| | - Joan Bertran
- Institut Borja de Bioetica, Universitat Ramon Llull, Barcelona, Spain
| | - Joan Carrera
- Institut Borja de Bioetica, Universitat Ramon Llull, Barcelona, Spain
| | - Francesc Torralba
- Institut Borja de Bioetica, Universitat Ramon Llull, Barcelona, Spain
| | - Francisco José Cambra
- Institut Borja de Bioetica, Universitat Ramon Llull, Barcelona, Spain
- Hospital Universitari Sant Joan de Déu Barcelona, Barcelona, Spain
| | - Martí Vila
- Institut Borja de Bioetica, Universitat Ramon Llull, Barcelona, Spain
| | - Martina Garriga
- Institut Borja de Bioetica, Universitat Ramon Llull, Barcelona, Spain
| | - Francesc Palau
- Department of Genetic Medicine and Pediatric Institut of Rare Diseases, Hospital Sant Joan de Déu, Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
- CIBER de Enfermedades Raras, ISCIII, Madrid, Spain
- Division of Pediatrics, University of Barcelona School of Medicine and Health Sciences, Barcelona, Spain
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16
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Curic E, Ewans L, Pysar R, Taylan F, Botto LD, Nordgren A, Gahl W, Palmer EE. International Undiagnosed Diseases Programs (UDPs): components and outcomes. Orphanet J Rare Dis 2023; 18:348. [PMID: 37946247 PMCID: PMC10633944 DOI: 10.1186/s13023-023-02966-1] [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: 04/27/2023] [Accepted: 10/30/2023] [Indexed: 11/12/2023] Open
Abstract
Over the last 15 years, Undiagnosed Diseases Programs have emerged to address the significant number of individuals with suspected but undiagnosed rare genetic diseases, integrating research and clinical care to optimize diagnostic outcomes. This narrative review summarizes the published literature surrounding Undiagnosed Diseases Programs worldwide, including thirteen studies that evaluate outcomes and two commentary papers. Commonalities in the diagnostic and research process of Undiagnosed Diseases Programs are explored through an appraisal of available literature. This exploration allowed for an assessment of the strengths and limitations of each of the six common steps, namely enrollment, comprehensive clinical phenotyping, research diagnostics, data sharing and matchmaking, results, and follow-up. Current literature highlights the potential utility of Undiagnosed Diseases Programs in research diagnostics. Since participants have often had extensive previous genetic studies, research pipelines allow for diagnostic approaches beyond exome or whole genome sequencing, through reanalysis using research-grade bioinformatics tools and multi-omics technologies. The overall diagnostic yield is presented by study, since different selection criteria at enrollment and reporting processes make comparisons challenging and not particularly informative. Nonetheless, diagnostic yield in an undiagnosed cohort reflects the potential of an Undiagnosed Diseases Program. Further comparisons and exploration of the outcomes of Undiagnosed Diseases Programs worldwide will allow for the development and improvement of the diagnostic and research process and in turn improve the value and utility of an Undiagnosed Diseases Program.
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Affiliation(s)
- Ela Curic
- Discipline of Paediatrics and Child Health, Faculty of Medicine and Health, School of Clinical Medicine, University of New South Wales, Bright Alliance Building, Level 8, Randwick, NSW, Australia
| | - Lisa Ewans
- Discipline of Paediatrics and Child Health, Faculty of Medicine and Health, School of Clinical Medicine, University of New South Wales, Bright Alliance Building, Level 8, Randwick, NSW, Australia
- Centre for Clinical Genetics, Sydney Children's Hospital, Randwick, NSW, Australia
- Genomics and Inherited Disease Program, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
| | - Ryan Pysar
- Discipline of Paediatrics and Child Health, Faculty of Medicine and Health, School of Clinical Medicine, University of New South Wales, Bright Alliance Building, Level 8, Randwick, NSW, Australia
- Centre for Clinical Genetics, Sydney Children's Hospital, Randwick, NSW, Australia
- Department of Clinical Genetics, The Children's Hospital at Westmead, Westmead, NSW, Australia
| | - Fulya Taylan
- Department of Molecular Medicine and Surgery, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics and Genomics, Karolinska University Hospital, Stockholm, Sweden
| | - Lorenzo D Botto
- Division of Medical Genetics, Department of Pediatrics, University of Utah, Salt Lake City, Utah, USA
| | - Ann Nordgren
- Department of Molecular Medicine and Surgery, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics and Genomics, Karolinska University Hospital, Stockholm, Sweden
- Department of Laboratory Medicine, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - William Gahl
- Medical Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Elizabeth Emma Palmer
- Discipline of Paediatrics and Child Health, Faculty of Medicine and Health, School of Clinical Medicine, University of New South Wales, Bright Alliance Building, Level 8, Randwick, NSW, Australia.
- Centre for Clinical Genetics, Sydney Children's Hospital, Randwick, NSW, Australia.
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17
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Caliskan A, Dangwal S, Dandekar T. Metadata integrity in bioinformatics: Bridging the gap between data and knowledge. Comput Struct Biotechnol J 2023; 21:4895-4913. [PMID: 37860229 PMCID: PMC10582761 DOI: 10.1016/j.csbj.2023.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 10/04/2023] [Accepted: 10/04/2023] [Indexed: 10/21/2023] Open
Abstract
In the fast-evolving landscape of biomedical research, the emergence of big data has presented researchers with extraordinary opportunities to explore biological complexities. In biomedical research, big data imply also a big responsibility. This is not only due to genomics data being sensitive information but also due to genomics data being shared and re-analysed among the scientific community. This saves valuable resources and can even help to find new insights in silico. To fully use these opportunities, detailed and correct metadata are imperative. This includes not only the availability of metadata but also their correctness. Metadata integrity serves as a fundamental determinant of research credibility, supporting the reliability and reproducibility of data-driven findings. Ensuring metadata availability, curation, and accuracy are therefore essential for bioinformatic research. Not only must metadata be readily available, but they must also be meticulously curated and ideally error-free. Motivated by an accidental discovery of a critical metadata error in patient data published in two high-impact journals, we aim to raise awareness for the need of correct, complete, and curated metadata. We describe how the metadata error was found, addressed, and present examples for metadata-related challenges in omics research, along with supporting measures, including tools for checking metadata and software to facilitate various steps from data analysis to published research.
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Affiliation(s)
- Aylin Caliskan
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Seema Dangwal
- Stanford Cardiovascular Institute, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305-5101, United States
| | - Thomas Dandekar
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
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18
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Ziemann M, Poulain P, Bora A. The five pillars of computational reproducibility: bioinformatics and beyond. Brief Bioinform 2023; 24:bbad375. [PMID: 37870287 PMCID: PMC10591307 DOI: 10.1093/bib/bbad375] [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: 06/16/2023] [Revised: 09/26/2023] [Accepted: 09/30/2023] [Indexed: 10/24/2023] Open
Abstract
Computational reproducibility is a simple premise in theory, but is difficult to achieve in practice. Building upon past efforts and proposals to maximize reproducibility and rigor in bioinformatics, we present a framework called the five pillars of reproducible computational research. These include (1) literate programming, (2) code version control and sharing, (3) compute environment control, (4) persistent data sharing and (5) documentation. These practices will ensure that computational research work can be reproduced quickly and easily, long into the future. This guide is designed for bioinformatics data analysts and bioinformaticians in training, but should be relevant to other domains of study.
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Affiliation(s)
- Mark Ziemann
- Deakin University, School of Life and Environmental Sciences, Geelong, Australia
- Burnet Institute, Melbourne, Australia
| | - Pierre Poulain
- Université Paris Cité, CNRS, Institut Jacques Monod, Paris, France
| | - Anusuiya Bora
- Deakin University, School of Life and Environmental Sciences, Geelong, Australia
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19
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Kemmer I, Keppler A, Serrano-Solano B, Rybina A, Özdemir B, Bischof J, El Ghadraoui A, Eriksson JE, Mathur A. Building a FAIR image data ecosystem for microscopy communities. Histochem Cell Biol 2023; 160:199-209. [PMID: 37341795 PMCID: PMC10492678 DOI: 10.1007/s00418-023-02203-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/27/2023] [Indexed: 06/22/2023]
Abstract
Bioimaging has now entered the era of big data with faster-than-ever development of complex microscopy technologies leading to increasingly complex datasets. This enormous increase in data size and informational complexity within those datasets has brought with it several difficulties in terms of common and harmonized data handling, analysis, and management practices, which are currently hampering the full potential of image data being realized. Here, we outline a wide range of efforts and solutions currently being developed by the microscopy community to address these challenges on the path towards FAIR bioimaging data. We also highlight how different actors in the microscopy ecosystem are working together, creating synergies that develop new approaches, and how research infrastructures, such as Euro-BioImaging, are fostering these interactions to shape the field.
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Affiliation(s)
- Isabel Kemmer
- Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117, Heidelberg, Germany
| | - Antje Keppler
- Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117, Heidelberg, Germany
| | - Beatriz Serrano-Solano
- Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117, Heidelberg, Germany
| | - Arina Rybina
- Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117, Heidelberg, Germany
| | - Buğra Özdemir
- Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117, Heidelberg, Germany
| | - Johanna Bischof
- Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117, Heidelberg, Germany
| | - Ayoub El Ghadraoui
- Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117, Heidelberg, Germany
| | - John E Eriksson
- Euro-BioImaging ERIC Statutory Seat, Tykistökatu 6, P.O. Box 123, 20521, Turku, Finland
| | - Aastha Mathur
- Euro-BioImaging ERIC Bio-Hub, European Molecular Biology Laboratory (EMBL) Heidelberg, Meyerhofstraße 1, 69117, Heidelberg, Germany.
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20
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Chen C, Wang J, Pan D, Wang X, Xu Y, Yan J, Wang L, Yang X, Yang M, Liu G. Applications of multi-omics analysis in human diseases. MedComm (Beijing) 2023; 4:e315. [PMID: 37533767 PMCID: PMC10390758 DOI: 10.1002/mco2.315] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 05/25/2023] [Accepted: 05/31/2023] [Indexed: 08/04/2023] Open
Abstract
Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.
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Affiliation(s)
- Chongyang Chen
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
- Co‐innovation Center of NeurodegenerationNantong UniversityNantongChina
| | - Jing Wang
- Shenzhen Key Laboratory of Modern ToxicologyShenzhen Medical Key Discipline of Health Toxicology (2020–2024)Shenzhen Center for Disease Control and PreventionShenzhenChina
| | - Donghui Pan
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Xinyu Wang
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Yuping Xu
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Junjie Yan
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Lizhen Wang
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Xifei Yang
- Shenzhen Key Laboratory of Modern ToxicologyShenzhen Medical Key Discipline of Health Toxicology (2020–2024)Shenzhen Center for Disease Control and PreventionShenzhenChina
| | - Min Yang
- Key Laboratory of Nuclear MedicineMinistry of HealthJiangsu Key Laboratory of Molecular Nuclear MedicineJiangsu Institute of Nuclear MedicineWuxiChina
| | - Gong‐Ping Liu
- Co‐innovation Center of NeurodegenerationNantong UniversityNantongChina
- Department of PathophysiologySchool of Basic MedicineKey Laboratory of Ministry of Education of China and Hubei Province for Neurological DisordersTongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
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21
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Wang X, Dervishi L, Li W, Jiang X, Ayday E, Vaidya J. Efficient Federated Kinship Relationship Identification. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2023; 2023:534-543. [PMID: 37351796 PMCID: PMC10283133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/24/2023]
Abstract
Kinship relationship estimation plays a significant role in today's genome studies. Since genetic data are mostly stored and protected in different silos, retrieving the desirable kinship relationships across federated data warehouses is a non-trivial problem. The ability to identify and connect related individuals is important for both research and clinical applications. In this work, we propose a new privacy-preserving kinship relationship estimation framework: Incremental Update Kinship Identification (INK). The proposed framework includes three key components that allow us to control the balance between privacy and accuracy (of kinship estimation): an incremental process coupled with the use of auxiliary information and informative scores. Our empirical evaluation shows that INK can achieve higher kinship identification correctness while exposing fewer genetic markers.
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Affiliation(s)
| | | | | | | | - Erman Ayday
- Case Western Reserve University, Cleveland, OH
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22
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Stott J, Wright T, Holmes J, Wilson J, Griffiths-Jones S, Foster D, Wright B. A systematic review of non-coding RNA genes with differential expression profiles associated with autism spectrum disorders. PLoS One 2023; 18:e0287131. [PMID: 37319303 PMCID: PMC10270643 DOI: 10.1371/journal.pone.0287131] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 05/30/2023] [Indexed: 06/17/2023] Open
Abstract
AIMS To identify differential expression of shorter non-coding RNA (ncRNA) genes associated with autism spectrum disorders (ASD). BACKGROUND ncRNA are functional molecules that derive from non-translated DNA sequence. The HUGO Gene Nomenclature Committee (HGNC) have approved ncRNA gene classes with alignment to the reference human genome. One subset is microRNA (miRNA), which are highly conserved, short RNA molecules that regulate gene expression by direct post-transcriptional repression of messenger RNA. Several miRNA genes are implicated in the development and regulation of the nervous system. Expression of miRNA genes in ASD cohorts have been examined by multiple research groups. Other shorter classes of ncRNA have been examined less. A comprehensive systematic review examining expression of shorter ncRNA gene classes in ASD is timely to inform the direction of research. METHODS We extracted data from studies examining ncRNA gene expression in ASD compared with non-ASD controls. We included studies on miRNA, piwi-interacting RNA (piRNA), small NF90 (ILF3) associated RNA (snaR), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), transfer RNA (tRNA), vault RNA (vtRNA) and Y RNA. The following electronic databases were searched: Cochrane Library, EMBASE, PubMed, Web of Science, PsycINFO, ERIC, AMED and CINAHL for papers published from January 2000 to May 2022. Studies were screened by two independent investigators with a third resolving discrepancies. Data was extracted from eligible papers. RESULTS Forty-eight eligible studies were included in our systematic review with the majority examining miRNA gene expression alone. Sixty-four miRNA genes had differential expression in ASD compared to controls as reported in two or more studies, but often in opposing directions. Four miRNA genes had differential expression in the same direction in the same tissue type in at least 3 separate studies. Increased expression was reported in miR-106b-5p, miR-155-5p and miR-146a-5p in blood, post-mortem brain, and across several tissue types, respectively. Decreased expression was reported in miR-328-3p in bloods samples. Seven studies examined differential expression from other classes of ncRNA, including piRNA, snRNA, snoRNA and Y RNA. No individual ncRNA genes were reported in more than one study. Six studies reported differentially expressed snoRNA genes in ASD. A meta-analysis was not possible because of inconsistent methodologies, disparate tissue types examined, and varying forms of data presented. CONCLUSION There is limited but promising evidence associating the expression of certain miRNA genes and ASD, although the studies are of variable methodological quality and the results are largely inconsistent. There is emerging evidence associating differential expression of snoRNA genes in ASD. It is not currently possible to say whether the reports of differential expression in ncRNA may relate to ASD aetiology, a response to shared environmental factors linked to ASD such as sleep and nutrition, other molecular functions, human diversity, or chance findings. To improve our understanding of any potential association, we recommend improved and standardised methodologies and reporting of raw data. Further high-quality research is required to shine a light on possible associations, which may yet yield important information.
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Affiliation(s)
- Jon Stott
- Child Oriented Mental Health Intervention Collaborative (COMIC), University of York in Collaboration with Leeds and York Partnership NHS Foundation Trust, York, United Kingdom
- Tees, Esk & Wear Valleys NHS Foundation Trust, Foss Park Hospital, York, United Kingdom
| | - Thomas Wright
- Manchester Centre for Genomic Medicine, Clinical Genetics Service, Saint Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester, United Kingdom
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Jannah Holmes
- Child Oriented Mental Health Intervention Collaborative (COMIC), University of York in Collaboration with Leeds and York Partnership NHS Foundation Trust, York, United Kingdom
- Hull York Medical School, University of York, Heslington, York, United Kingdom
| | - Julie Wilson
- Department of Mathematics, University of York, Heslington, York, United Kingdom
| | - Sam Griffiths-Jones
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
| | - Deborah Foster
- Tees, Esk & Wear Valleys NHS Foundation Trust, Foss Park Hospital, York, United Kingdom
| | - Barry Wright
- Child Oriented Mental Health Intervention Collaborative (COMIC), University of York in Collaboration with Leeds and York Partnership NHS Foundation Trust, York, United Kingdom
- Hull York Medical School, University of York, Heslington, York, United Kingdom
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23
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Banerjee J, Taroni JN, Allaway RJ, Prasad DV, Guinney J, Greene C. Machine learning in rare disease. Nat Methods 2023:10.1038/s41592-023-01886-z. [PMID: 37248386 DOI: 10.1038/s41592-023-01886-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 04/22/2023] [Indexed: 05/31/2023]
Abstract
High-throughput profiling methods (such as genomics or imaging) have accelerated basic research and made deep molecular characterization of patient samples routine. These approaches provide a rich portrait of genes, molecular pathways and cell types involved in disease phenotypes. Machine learning (ML) can be a useful tool for extracting disease-relevant patterns from high-dimensional datasets. However, depending upon the complexity of the biological question, machine learning often requires many samples to identify recurrent and biologically meaningful patterns. Rare diseases are inherently limited in clinical cases, leading to few samples to study. In this Perspective, we outline the challenges and emerging solutions for using ML for small sample sets, specifically in rare diseases. Advances in ML methods for rare diseases are likely to be informative for applications beyond rare diseases for which few samples exist with high-dimensional data. We propose that the method community prioritize the development of ML techniques for rare disease research.
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Affiliation(s)
| | - Jaclyn N Taroni
- Childhood Cancer Data Lab, Alex's Lemonade Stand Foundation, Philadelphia, PA, USA
| | | | | | | | - Casey Greene
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA.
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24
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Sonrel A, Luetge A, Soneson C, Mallona I, Germain PL, Knyazev S, Gilis J, Gerber R, Seurinck R, Paul D, Sonder E, Crowell HL, Fanaswala I, Al-Ajami A, Heidari E, Schmeing S, Milosavljevic S, Saeys Y, Mangul S, Robinson MD. Meta-analysis of (single-cell method) benchmarks reveals the need for extensibility and interoperability. Genome Biol 2023; 24:119. [PMID: 37198712 DOI: 10.1186/s13059-023-02962-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 05/06/2023] [Indexed: 05/19/2023] Open
Abstract
Computational methods represent the lifeblood of modern molecular biology. Benchmarking is important for all methods, but with a focus here on computational methods, benchmarking is critical to dissect important steps of analysis pipelines, formally assess performance across common situations as well as edge cases, and ultimately guide users on what tools to use. Benchmarking can also be important for community building and advancing methods in a principled way. We conducted a meta-analysis of recent single-cell benchmarks to summarize the scope, extensibility, and neutrality, as well as technical features and whether best practices in open data and reproducible research were followed. The results highlight that while benchmarks often make code available and are in principle reproducible, they remain difficult to extend, for example, as new methods and new ways to assess methods emerge. In addition, embracing containerization and workflow systems would enhance reusability of intermediate benchmarking results, thus also driving wider adoption.
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Affiliation(s)
- Anthony Sonrel
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Almut Luetge
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Charlotte Soneson
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Izaskun Mallona
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Pierre-Luc Germain
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
- D-HEST Institute for Neuroscience, ETH Zürich, Zurich, Switzerland
| | - Sergey Knyazev
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, CA, USA
| | - Jeroen Gilis
- Department of Applied Mathematics, Computer Science & Statistics, Ghent University, Ghent, Belgium
- Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium
- Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
| | - Reto Gerber
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Ruth Seurinck
- Department of Applied Mathematics, Computer Science & Statistics, Ghent University, Ghent, Belgium
- Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium
| | - Dominique Paul
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Emanuel Sonder
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
- D-HEST Institute for Neuroscience, ETH Zürich, Zurich, Switzerland
| | - Helena L Crowell
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Imran Fanaswala
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Ahmad Al-Ajami
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Elyas Heidari
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Stephan Schmeing
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Stefan Milosavljevic
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
| | - Yvan Saeys
- Department of Applied Mathematics, Computer Science & Statistics, Ghent University, Ghent, Belgium
- Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium
| | - Serghei Mangul
- Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, CA, USA
| | - Mark D Robinson
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.
- SIB Swiss Institute of Bioinformatics, Zurich, Switzerland.
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Mogilenko DA, Sergushichev A, Artyomov MN. Systems Immunology Approaches to Metabolism. Annu Rev Immunol 2023; 41:317-342. [PMID: 37126419 DOI: 10.1146/annurev-immunol-101220-031513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Over the last decade, immunometabolism has emerged as a novel interdisciplinary field of research and yielded significant fundamental insights into the regulation of immune responses. Multiple classical approaches to interrogate immunometabolism, including bulk metabolic profiling and analysis of metabolic regulator expression, paved the way to appreciating the physiological complexity of immunometabolic regulation in vivo. Studying immunometabolism at the systems level raised the need to transition towards the next-generation technology for metabolic profiling and analysis. Spatially resolved metabolic imaging and computational algorithms for multi-modal data integration are new approaches to connecting metabolism and immunity. In this review, we discuss recent studies that highlight the complex physiological interplay between immune responses and metabolism and give an overview of technological developments that bear the promise of capturing this complexity most directly and comprehensively.
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Affiliation(s)
- Denis A Mogilenko
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
- Current affiliation: Department of Medicine, Department of Pathology, Microbiology, and Immunology, and Vanderbilt Center for Immunobiology, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Alexey Sergushichev
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
- Computer Technologies Laboratory, ITMO University, Saint Petersburg, Russia
| | - Maxim N Artyomov
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
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26
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Wagner JK, Yu JH, Fullwiley D, Moore C, Wilson JF, Bamshad MJ, Royal CD. Guidelines for genetic ancestry inference created through roundtable discussions. HGG ADVANCES 2023; 4:100178. [PMID: 36798092 PMCID: PMC9926022 DOI: 10.1016/j.xhgg.2023.100178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 01/03/2023] [Indexed: 01/15/2023] Open
Abstract
The use of genetic and genomic technology to infer ancestry is commonplace in a variety of contexts, particularly in biomedical research and for direct-to-consumer genetic testing. In 2013 and 2015, two roundtables engaged a diverse group of stakeholders toward the development of guidelines for inferring genetic ancestry in academia and industry. This report shares the stakeholder groups' work and provides an analysis of, commentary on, and views from the groundbreaking and sustained dialogue. We describe the engagement processes and the stakeholder groups' resulting statements and proposed guidelines. The guidelines focus on five key areas: application of genetic ancestry inference, assumptions and confidence/laboratory and statistical methods, terminology and population identifiers, impact on individuals and groups, and communication or translation of genetic ancestry inferences. We delineate the terms and limitations of the guidelines and discuss their critical role in advancing the development and implementation of best practices for inferring genetic ancestry and reporting the results. These efforts should inform both governmental regulation and self-regulation.
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Affiliation(s)
- Jennifer K. Wagner
- School of Engineering Design and Innovation, Pennsylvania State University, University Park, PA 16802, USA
- Institute for Computational and Data Science, Pennsylvania State University, University Park, PA 16802, USA
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA 16802, USA
- Rock Ethics Institute, Pennsylvania State University, University Park, PA 16802, USA
- Penn State Law, University Park, PA 16802, USA
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA 16802, USA
| | - Joon-Ho Yu
- Department of Pediatrics and Institute for Public Health Genetics, University of Washington, Seattle, WA 98195, USA
- Treuman Katz Center for Pediatric Bioethics, Seattle Children’s Hospital and Research Institute, Seattle, WA 98101, USA
| | - Duana Fullwiley
- Department of Anthropology, Stanford University, Stanford, CA 94305, USA
| | | | - James F. Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, Scotland
| | - Michael J. Bamshad
- Department of Pediatrics and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- Division of Genetic Medicine, Seattle Children’s Hospital, Seattle, WA 98101, USA
| | - Charmaine D. Royal
- Departments of African and African American Studies, Biology, Global Health, and Family Medicine and Community Health, Duke University, Durham, NC 27708, USA
| | - Genetic Ancestry Inference Roundtable Participants
- School of Engineering Design and Innovation, Pennsylvania State University, University Park, PA 16802, USA
- Institute for Computational and Data Science, Pennsylvania State University, University Park, PA 16802, USA
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA 16802, USA
- Rock Ethics Institute, Pennsylvania State University, University Park, PA 16802, USA
- Penn State Law, University Park, PA 16802, USA
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA 16802, USA
- Department of Pediatrics and Institute for Public Health Genetics, University of Washington, Seattle, WA 98195, USA
- Treuman Katz Center for Pediatric Bioethics, Seattle Children’s Hospital and Research Institute, Seattle, WA 98101, USA
- Department of Anthropology, Stanford University, Stanford, CA 94305, USA
- The DNA Detectives, Dana Point, CA, USA
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, Scotland
- Department of Pediatrics and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- Division of Genetic Medicine, Seattle Children’s Hospital, Seattle, WA 98101, USA
- Departments of African and African American Studies, Biology, Global Health, and Family Medicine and Community Health, Duke University, Durham, NC 27708, USA
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NeuroLINCS Proteomics: Defining human-derived iPSC proteomes and protein signatures of pluripotency. Sci Data 2023; 10:24. [PMID: 36631473 PMCID: PMC9834231 DOI: 10.1038/s41597-022-01687-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 09/07/2022] [Indexed: 01/13/2023] Open
Abstract
The National Institute of Health (NIH) Library of integrated network-based cellular signatures (LINCS) program is premised on the generation of a publicly available data resource of cell-based biochemical responses or "signatures" to genetic or environmental perturbations. NeuroLINCS uses human inducible pluripotent stem cells (hiPSCs), derived from patients and healthy controls, and differentiated into motor neuron cell cultures. This multi-laboratory effort strives to establish i) robust multi-omic workflows for hiPSC and differentiated neuronal cultures, ii) public annotated data sets and iii) relevant and targetable biological pathways of spinal muscular atrophy (SMA) and amyotrophic lateral sclerosis (ALS). Here, we focus on the proteomics and the quality of the developed workflow of hiPSC lines from 6 individuals, though epigenomics and transcriptomics data are also publicly available. Known and commonly used markers representing 73 proteins were reproducibly quantified with consistent expression levels across all hiPSC lines. Data quality assessments, data levels and metadata of all 6 genetically diverse human iPSCs analysed by DIA-MS are parsable and available as a high-quality resource to the public.
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28
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Oza VH, Whitlock JH, Wilk EJ, Uno-Antonison A, Wilk B, Gajapathy M, Howton TC, Trull A, Ianov L, Worthey EA, Lasseigne BN. Ten simple rules for using public biological data for your research. PLoS Comput Biol 2023; 19:e1010749. [PMID: 36602970 DOI: 10.1371/journal.pcbi.1010749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
With an increasing amount of biological data available publicly, there is a need for a guide on how to successfully download and use this data. The 10 simple rules for using public biological data are: (1) use public data purposefully in your research; (2) evaluate data for your use case; (3) check data reuse requirements and embargoes; (4) be aware of ethics for data reuse; (5) plan for data storage and compute requirements; (6) know what you are downloading; (7) download programmatically and verify integrity; (8) properly cite data; (9) make reprocessed data and models Findable, Accessible, Interoperable, and Reusable (FAIR) and share; and (10) make pipelines and code FAIR and share. These rules are intended as a guide for researchers wanting to make use of available data and to increase data reuse and reproducibility.
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Affiliation(s)
- Vishal H Oza
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Jordan H Whitlock
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Elizabeth J Wilk
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Angelina Uno-Antonison
- Center for Computational Genomics and Data Sciences, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
- Department of Pediatrics, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
- Department of Pathology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Brandon Wilk
- Center for Computational Genomics and Data Sciences, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
- Department of Pediatrics, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
- Department of Pathology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Manavalan Gajapathy
- Center for Computational Genomics and Data Sciences, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
- Department of Pediatrics, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
- Department of Pathology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Timothy C Howton
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Austyn Trull
- Center for Computational Genomics and Data Sciences, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
- Department of Pediatrics, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
- Department of Pathology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Lara Ianov
- Civitan International Research Center, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Elizabeth A Worthey
- Center for Computational Genomics and Data Sciences, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
- Department of Pediatrics, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
- Department of Pathology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Brittany N Lasseigne
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America
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Chen S, Duan B, Zhu C, Tang C, Wang S, Gao Y, Fu S, Fan L, Yang Q, Liu Q. Privacy-preserving integration of multiple institutional data for single-cell type identification with scPrivacy. SCIENCE CHINA. LIFE SCIENCES 2022; 66:1183-1195. [PMID: 36543995 PMCID: PMC9771767 DOI: 10.1007/s11427-022-2224-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 09/15/2022] [Indexed: 12/24/2022]
Abstract
The rapid accumulation of large-scale single-cell RNA-seq datasets from multiple institutions presents remarkable opportunities for automatically cell annotations through integrative analyses. However, the privacy issue has existed but being ignored, since we are limited to access and utilize all the reference datasets distributed in different institutions globally due to the prohibited data transmission across institutions by data regulation laws. To this end, we present scPrivacy, which is the first and generalized automatically single-cell type identification prototype to facilitate single cell annotations in a data privacy-preserving collaboration manner. We evaluated scPrivacy on a comprehensive set of publicly available benchmark datasets for single-cell type identification to stimulate the scenario that the reference datasets are rapidly generated and distributed in multiple institutions, while they are prohibited to be integrated directly or exposed to each other due to the data privacy regulations, demonstrating its effectiveness, time efficiency and robustness for privacy-preserving integration of multiple institutional datasets in single cell annotations.
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Affiliation(s)
- Shaoqi Chen
- grid.24516.340000000123704535Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092 China
| | - Bin Duan
- grid.24516.340000000123704535Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092 China
| | - Chenyu Zhu
- grid.24516.340000000123704535Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092 China
| | - Chen Tang
- grid.24516.340000000123704535Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092 China
| | - Shuguang Wang
- grid.24516.340000000123704535Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092 China
| | - Yicheng Gao
- grid.24516.340000000123704535Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092 China
| | - Shaliu Fu
- grid.24516.340000000123704535Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092 China
| | - Lixin Fan
- Department of AI, WeBank, Shenzhen, 518055 China
| | - Qiang Yang
- Department of AI, WeBank, Shenzhen, 518055 China
| | - Qi Liu
- grid.24516.340000000123704535Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092 China ,grid.24516.340000000123704535Translational Medical Center for Stem Cell Therapy and Institution for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092 China ,Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210 China
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30
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Policies to regulate data sharing of cohorts via data infrastructures: An interview study with funding agencies. Int J Med Inform 2022; 168:104900. [DOI: 10.1016/j.ijmedinf.2022.104900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 10/13/2022] [Accepted: 10/14/2022] [Indexed: 11/08/2022]
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31
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Gerussi A, Scaravaglio M, Cristoferi L, Verda D, Milani C, De Bernardi E, Ippolito D, Asselta R, Invernizzi P, Kather JN, Carbone M. Artificial intelligence for precision medicine in autoimmune liver disease. Front Immunol 2022; 13:966329. [PMID: 36439097 PMCID: PMC9691668 DOI: 10.3389/fimmu.2022.966329] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 10/13/2022] [Indexed: 09/10/2023] Open
Abstract
Autoimmune liver diseases (AiLDs) are rare autoimmune conditions of the liver and the biliary tree with unknown etiology and limited treatment options. AiLDs are inherently characterized by a high degree of complexity, which poses great challenges in understanding their etiopathogenesis, developing novel biomarkers and risk-stratification tools, and, eventually, generating new drugs. Artificial intelligence (AI) is considered one of the best candidates to support researchers and clinicians in making sense of biological complexity. In this review, we offer a primer on AI and machine learning for clinicians, and discuss recent available literature on its applications in medicine and more specifically how it can help to tackle major unmet needs in AiLDs.
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Affiliation(s)
- Alessio Gerussi
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
| | - Miki Scaravaglio
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
| | - Laura Cristoferi
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
- Bicocca Bioinformatics Biostatistics and Bioimaging Centre - B4, School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | | | - Chiara Milani
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
| | - Elisabetta De Bernardi
- Department of Medicine and Surgery and Tecnomed Foundation, University of Milano - Bicocca, Monza, Italy
| | | | - Rosanna Asselta
- Humanitas Clinical and Research Center, Rozzano, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Pietro Invernizzi
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
| | - Jakob Nikolas Kather
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Marco Carbone
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
- European Reference Network on Hepatological Diseases (ERN RARE-LIVER), San Gerardo Hospital, Monza, Italy
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Hawkins NT, Maldaver M, Yannakopoulos A, Guare LA, Krishnan A. Systematic tissue annotations of genomics samples by modeling unstructured metadata. Nat Commun 2022; 13:6736. [PMID: 36347858 PMCID: PMC9643451 DOI: 10.1038/s41467-022-34435-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 10/25/2022] [Indexed: 11/10/2022] Open
Abstract
There are currently >1.3 million human -omics samples that are publicly available. This valuable resource remains acutely underused because discovering particular samples from this ever-growing data collection remains a significant challenge. The major impediment is that sample attributes are routinely described using varied terminologies written in unstructured natural language. We propose a natural-language-processing-based machine learning approach (NLP-ML) to infer tissue and cell-type annotations for genomics samples based only on their free-text metadata. NLP-ML works by creating numerical representations of sample descriptions and using these representations as features in a supervised learning classifier that predicts tissue/cell-type terms. Our approach significantly outperforms an advanced graph-based reasoning annotation method (MetaSRA) and a baseline exact string matching method (TAGGER). Model similarities between related tissues demonstrate that NLP-ML models capture biologically-meaningful signals in text. Additionally, these models correctly classify tissue-associated biological processes and diseases based on their text descriptions alone. NLP-ML models are nearly as accurate as models based on gene-expression profiles in predicting sample tissue annotations but have the distinct capability to classify samples irrespective of the genomics experiment type based on their text metadata. Python NLP-ML prediction code and trained tissue models are available at https://github.com/krishnanlab/txt2onto .
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Affiliation(s)
- Nathaniel T. Hawkins
- grid.17088.360000 0001 2150 1785Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824 USA
| | - Marc Maldaver
- grid.17088.360000 0001 2150 1785Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824 USA
| | - Anna Yannakopoulos
- grid.17088.360000 0001 2150 1785Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824 USA
| | - Lindsay A. Guare
- grid.17088.360000 0001 2150 1785Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824 USA ,grid.17088.360000 0001 2150 1785Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824 USA ,grid.17088.360000 0001 2150 1785Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, MI 48824 USA
| | - Arjun Krishnan
- grid.17088.360000 0001 2150 1785Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824 USA ,grid.17088.360000 0001 2150 1785Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824 USA ,grid.430503.10000 0001 0703 675XDepartment of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045 USA
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33
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Rodriguez JJRB, Cuales JMD, Herrera MJB, Zubiri LAM, Muallil RM, Ishmael AI, Jimenez EB, Stoneking M, De Ungria MCA. Ethical challenges in genetic research among Philippine Indigenous Peoples: Insights from fieldwork in Zamboanga and the Sulu Archipelago. Front Genet 2022; 13:901515. [DOI: 10.3389/fgene.2022.901515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 09/21/2022] [Indexed: 11/13/2022] Open
Abstract
The Philippines, with the recent discovery of an archaic hominin in Luzon and an extensive ethnolinguistic diversity of more than 100 Indigenous peoples, is crucial to understanding human evolution and population history in Island Southeast Asia. Advances in DNA sequencing technologies enable the rapid generation of genomic data to robustly address questions about origins, relatedness, and population movements. With the increased genetic sampling in the country, especially by international scientists, it is vital to revisit ethical rules and guidelines relevant to conducting research among Indigenous peoples. Our team led fieldwork expeditions between 2019 and February 2020 in Zamboanga and the Sulu Archipelago, a chain of islands connecting the Mindanao and Borneo landmasses. The trips concluded with a collection of 2,149 DNA samples from 104 field sites. We present our fieldwork experience among the mostly sea-oriented Sama-Bajaw and Tausug-speaking communities and propose recommendations to address the ethical challenges of conducting such research. This work contributes toward building an enabling research environment in the Philippines that respects the rights and autonomy of Indigenous peoples, who are the rightful owners of their DNA and all genetic information contained therein.
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34
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Medeiros GC, Gould TD, Prueitt WL, Nanavati J, Grunebaum MF, Farber NB, Singh B, Selvaraj S, Machado-Vieira R, Achtyes ED, Parikh SV, Frye MA, Zarate CA, Goes FS. Blood-based biomarkers of antidepressant response to ketamine and esketamine: A systematic review and meta-analysis. Mol Psychiatry 2022; 27:3658-3669. [PMID: 35760879 PMCID: PMC9933928 DOI: 10.1038/s41380-022-01652-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 05/17/2022] [Accepted: 05/31/2022] [Indexed: 02/08/2023]
Abstract
(R,S)-ketamine (ketamine) and its enantiomer (S)-ketamine (esketamine) can produce rapid and substantial antidepressant effects. However, individual response to ketamine/esketamine is variable, and there are no well-accepted methods to differentiate persons who are more likely to benefit. Numerous potential peripheral biomarkers have been reported, but their current utility is unclear. We conducted a systematic review/meta-analysis examining the association between baseline levels and longitudinal changes in blood-based biomarkers, and response to ketamine/esketamine. Of the 5611 citations identified, 56 manuscripts were included (N = 2801 participants), and 26 were compatible with meta-analytical calculations. Random-effect models were used, and effect sizes were reported as standardized mean differences (SMD). Our assessments revealed that more than 460 individual biomarkers were examined. Frequently studied groups included neurotrophic factors (n = 15), levels of ketamine and ketamine metabolites (n = 13), and inflammatory markers (n = 12). There were no consistent associations between baseline levels of blood-based biomarkers, and response to ketamine. However, in a longitudinal analysis, ketamine responders had statistically significant increases in brain-derived neurotrophic factor (BDNF) when compared to pre-treatment levels (SMD [95% CI] = 0.26 [0.03, 0.48], p = 0.02), whereas non-responders showed no significant changes in BDNF levels (SMD [95% CI] = 0.05 [-0.19, 0.28], p = 0.70). There was no consistent evidence to support any additional longitudinal biomarkers. Findings were inconclusive for esketamine due to the small number of studies (n = 2). Despite a diverse and substantial literature, there is limited evidence that blood-based biomarkers are associated with response to ketamine, and no current evidence of clinical utility.
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Affiliation(s)
- Gustavo C. Medeiros
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA.,Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Todd D. Gould
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA.,Departments of Pharmacology and Anatomy & Neurobiology, University of Maryland School of Medicine, Baltimore, MD, USA.,Veterans Affairs Maryland Health Care System, Baltimore, MD, USA
| | | | - Julie Nanavati
- Welch Medical Library, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Michael F. Grunebaum
- Columbia University Irving Medical Center and New York State Psychiatric Institute, New York City, NY, USA
| | - Nuri B. Farber
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Balwinder Singh
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Sudhakar Selvaraj
- Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA
| | - Rodrigo Machado-Vieira
- Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA
| | - Eric D. Achtyes
- Division of Psychiatry and Behavioral Medicine, Michigan State University College of Human Medicine, Grand Rapids, MI, USA.,Pine Rest Christian Mental Health Services, Grand Rapids, MI, USA
| | - Sagar V. Parikh
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Mark A. Frye
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Carlos A. Zarate
- Experimental Therapeutics & Pathophysiology Branch, NIMH-NIH, Bethesda, MD, USA
| | - Fernando S. Goes
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA.,Correspondence and requests for materials should be addressed to Fernando S. Goes.,
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Abstract
Genomics data are important for advancing biomedical research, improving clinical care, and informing other disciplines such as forensics and genealogy. However, privacy concerns arise when genomic data are shared. In particular, the identifying nature of genetic information, its direct relationship to health status, and the potential financial harm and stigmatization posed to individuals and their blood relatives call for a survey of the privacy issues related to sharing genetic and related data and potential solutions to overcome these issues. In this work, we provide an overview of the importance of genomic privacy, the information gleaned from genomics data, the sources of potential private information leakages in genomics, and ways to preserve privacy while utilizing the genetic information in research. We discuss the relationship between trust in the scientific community and protecting privacy, illuminating a future roadmap for data sharing and study participation.
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Affiliation(s)
- Gamze Gürsoy
- Department of Biomedical Informatics, Columbia University, New York, NY, USA; .,New York Genome Center, New York, NY, USA
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36
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Heacock ML, Lopez AR, Amolegbe SM, Carlin DJ, Henry HF, Trottier BA, Velasco ML, Suk WA. Enhancing Data Integration, Interoperability, and Reuse to Address Complex and Emerging Environmental Health Problems. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:7544-7552. [PMID: 35549252 PMCID: PMC9227711 DOI: 10.1021/acs.est.1c08383] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Indexed: 05/21/2023]
Abstract
Environmental health sciences (EHS) span many diverse disciplines. Within the EHS community, the National Institute of Environmental Health Sciences Superfund Research Program (SRP) funds multidisciplinary research aimed to address pressing and complex issues on how people are exposed to hazardous substances and their related health consequences with the goal of identifying strategies to reduce exposures and protect human health. While disentangling the interrelationships that contribute to environmental exposures and their effects on human health over the course of life remains difficult, advances in data science and data sharing offer a path forward to explore data across disciplines to reveal new insights. Multidisciplinary SRP-funded teams are well-positioned to examine how to best integrate EHS data across diverse research domains to address multifaceted environmental health problems. As such, SRP supported collaborative research projects designed to foster and enhance the interoperability and reuse of diverse and complex data streams. This perspective synthesizes those experiences as a landscape view of the challenges identified while working to increase the FAIR-ness (Findable, Accessible, Interoperable, and Reusable) of EHS data and opportunities to address them.
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Affiliation(s)
- Michelle L. Heacock
- Superfund
Research Program, National Institute of Environmental Health Sciences
(NIEHS), National Institutes
of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina 27709, United States
- . Tel: 984-287-3267
| | | | - Sara M. Amolegbe
- Superfund
Research Program, National Institute of Environmental Health Sciences
(NIEHS), National Institutes
of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina 27709, United States
| | - Danielle J. Carlin
- Superfund
Research Program, National Institute of Environmental Health Sciences
(NIEHS), National Institutes
of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina 27709, United States
| | - Heather F. Henry
- Superfund
Research Program, National Institute of Environmental Health Sciences
(NIEHS), National Institutes
of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina 27709, United States
| | - Brittany A. Trottier
- Superfund
Research Program, National Institute of Environmental Health Sciences
(NIEHS), National Institutes
of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina 27709, United States
| | | | - William A. Suk
- Superfund
Research Program, National Institute of Environmental Health Sciences
(NIEHS), National Institutes
of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina 27709, United States
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Gao P, Dong HM, Liu SM, Fan XR, Jiang C, Wang YS, Margulies D, Li HF, Zuo XN. A Chinese multi-modal neuroimaging data release for increasing diversity of human brain mapping. Sci Data 2022; 9:286. [PMID: 35680932 PMCID: PMC9184635 DOI: 10.1038/s41597-022-01413-3] [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: 11/04/2021] [Accepted: 05/24/2022] [Indexed: 11/09/2022] Open
Abstract
The big-data use is becoming a standard practice in the neuroimaging field through data-sharing initiatives. It is important for the community to realize that such open science effort must protect personal, especially facial information when raw neuroimaging data are shared. An ideal tool for the face anonymization should not disturb subsequent brain tissue extraction and further morphological measurements. Using the high-resolution head images from magnetic resonance imaging (MRI) of 215 healthy Chinese, we discovered and validated a template effect on the face anonymization. Improved facial anonymization was achieved when the Chinese head templates but not the Western templates were applied to obscure the faces of Chinese brain images. This finding has critical implications for international brain imaging data-sharing. To facilitate the further investigation of potential culture-related impacts on and increase diversity of data-sharing for the human brain mapping, we released the 215 Chinese multi-modal MRI data into a database for imaging Chinese young brains, namely’I See your Brains (ISYB)’, to the public via the Science Data Bank (10.11922/sciencedb.00740). Measurement(s) | brain imaging measurements | Technology Type(s) | magnetic resonance imaging | Factor Type(s) | multimodal neuroimaging metrics | Sample Characteristic - Organism | Homo | Sample Characteristic - Environment | magnetic | Sample Characteristic - Location | North China |
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Affiliation(s)
- Peng Gao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Hao-Ming Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.,National Basic Science Data Center, Beijing, 100109, China
| | - Si-Man Liu
- Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xue-Ru Fan
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Chao Jiang
- School of Psychology, Capital Normal University, Beijing, 100048, China
| | - Yin-Shan Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Daniel Margulies
- Centre National de la Recherche Scientifique, Frontlab, Brain and Spinal Cord Institute, Paris, UMR 7225, France
| | - Hai-Fang Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China.
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China. .,National Basic Science Data Center, Beijing, 100109, China. .,Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China. .,Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China. .,Key Laboratory of Brain and Education, School of Education Science, Nanning Normal University, Nanning, 530001, China.
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38
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Opportunities and challenges for the use of common controls in sequencing studies. Nat Rev Genet 2022; 23:665-679. [PMID: 35581355 DOI: 10.1038/s41576-022-00487-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/22/2022] [Indexed: 01/02/2023]
Abstract
Genome-wide association studies using large-scale genome and exome sequencing data have become increasingly valuable in identifying associations between genetic variants and disease, transforming basic research and translational medicine. However, this progress has not been equally shared across all people and conditions, in part due to limited resources. Leveraging publicly available sequencing data as external common controls, rather than sequencing new controls for every study, can better allocate resources by augmenting control sample sizes or providing controls where none existed. However, common control studies must be carefully planned and executed as even small differences in sample ascertainment and processing can result in substantial bias. Here, we discuss challenges and opportunities for the robust use of common controls in high-throughput sequencing studies, including study design, quality control and statistical approaches. Thoughtful generation and use of large and valuable genetic sequencing data sets will enable investigation of a broader and more representative set of conditions, environments and genetic ancestries than otherwise possible.
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39
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Muenzen KD, Amendola LM, Kauffman TL, Mittendorf KF, Bensen JT, Chen F, Green R, Powell BC, Kvale M, Angelo F, Farnan L, Fullerton SM, Robinson JO, Li T, Murali P, Lawlor JM, Ou J, Hindorff LA, Jarvik GP, Crosslin DR. Lessons learned and recommendations for data coordination in collaborative research: The CSER consortium experience. HGG ADVANCES 2022; 3:100120. [PMID: 35707062 PMCID: PMC9190054 DOI: 10.1016/j.xhgg.2022.100120] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 05/16/2022] [Indexed: 11/18/2022] Open
Abstract
Integrating data across heterogeneous research environments is a key challenge in multi-site, collaborative research projects. While it is important to allow for natural variation in data collection protocols across research sites, it is also important to achieve interoperability between datasets in order to reap the full benefits of collaborative work. However, there are few standards to guide the data coordination process from project conception to completion. In this paper, we describe the experiences of the Clinical Sequence Evidence-Generating Research (CSER) consortium Data Coordinating Center (DCC), which coordinated harmonized survey and genomic sequencing data from seven clinical research sites from 2020 to 2022. Using input from multiple consortium working groups and from CSER leadership, we first identify 14 lessons learned from CSER in the categories of communication, harmonization, informatics, compliance, and analytics. We then distill these lessons learned into 11 recommendations for future research consortia in the areas of planning, communication, informatics, and analytics. We recommend that planning and budgeting for data coordination activities occur as early as possible during consortium conceptualization and development to minimize downstream complications. We also find that clear, reciprocal, and continuous communication between consortium stakeholders and the DCC is equally important to maintaining a secure and centralized informatics ecosystem for pooling data. Finally, we discuss the importance of actively interrogating current approaches to data governance, particularly for research studies that straddle the research-clinical divide.
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40
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Lee A, Bessell N, van den Heuvel H, Saalasti S, Klessa K, Müller N, Ball MJ. The latest development of the DELAD project for sharing corpora of speech disorders. CLINICAL LINGUISTICS & PHONETICS 2022; 36:102-110. [PMID: 33890543 DOI: 10.1080/02699206.2021.1913514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 03/31/2021] [Accepted: 03/31/2021] [Indexed: 06/12/2023]
Abstract
Corpora of speech of individuals with communication disorders (CSD) are invaluable resources for education and research, but they are costly and hard to build and difficult to share for various reasons. DELAD, which means 'shared' in Swedish, is a project initiated by Professors Nicole Müller and Martin Ball in 2015 that aims to address this issue by establishing a platform for researchers to share datasets of speech disorders with interested audiences. To date four workshops have been held, where selected participants, covering various expertise including researchers in clinical phonetics and linguistics, speech and language therapy, infrastructure specialists, and ethics and legal specialists, participated to discuss relevant issues in setting up such an archive. Positive and steady progress has been made since 2015, including refurbishing the DELAD website (http://delad.net/) with information and application forms for researchers to join and share their datasets and linking with the CLARIN K-Centre for Atypical Communication Expertise (https://ace.ruhosting.nl/) where CSD can be hosted and accessed through the CLARIN B-Centres, The Language Archive (https://tla.mpi.nl/tools/tla-tools/) and TalkBank (https://talkbank.org/). The latest workshop, which was funded by CLARIN (Common Language Resources and Technology Infrastructure) was held as an online event in January 2021 on topics including Data Protection Impact Assessments, reviewing changes in ethics perspectives in academia on sharing CSD, and voice conversion as a mean to pseudonomise speech. This paper reports the latest progress of DELAD and discusses the directions for further advance of the initiative, with information on how researchers can contribute to the repository.
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Affiliation(s)
- Alice Lee
- Department of Speech and Hearing Sciences, University College Cork, Cork, Ireland
| | - Nicola Bessell
- Department of Speech and Hearing Sciences, University College Cork, Cork, Ireland
| | - Henk van den Heuvel
- Centre for Language and Speech Technology, Radboud University, Nijmegen, The Netherlands
| | - Satu Saalasti
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| | - Katarzyna Klessa
- Department of Phonetics, Adam Mickiewicz University in Poznań, Poznań, Poland
| | - Nicole Müller
- Department of Speech and Hearing Sciences, University College Cork, Cork, Ireland
| | - Martin J Ball
- School of Languages, Literatures and Linguistics, Bangor University, Bangor, UK
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41
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Lee BD, Gitter A, Greene CS, Raschka S, Maguire F, Titus AJ, Kessler MD, Lee AJ, Chevrette MG, Stewart PA, Britto-Borges T, Cofer EM, Yu KH, Carmona JJ, Fertig EJ, Kalinin AA, Signal B, Lengerich BJ, Triche TJ, Boca SM. Ten quick tips for deep learning in biology. PLoS Comput Biol 2022; 18:e1009803. [PMID: 35324884 PMCID: PMC8946751 DOI: 10.1371/journal.pcbi.1009803] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Affiliation(s)
- Benjamin D. Lee
- In-Q-Tel Labs, Arlington, Virginia, United States of America
- School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, United States of America
- Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Anthony Gitter
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Morgridge Institute for Research, Madison, Wisconsin, United States of America
| | - Casey S. Greene
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, Colorado, United States of America
- Center for Health AI, University of Colorado School of Medicine, Aurora, Colorado, United States of America
| | - Sebastian Raschka
- Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Finlay Maguire
- Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Alexander J. Titus
- University of New Hampshire, Manchester, New Hampshire, United States of America
- Bioeconomy.XYZ, Manchester, New Hampshire, United States of America
| | - Michael D. Kessler
- Department of Oncology, Johns Hopkins University, Baltimore, Maryland, United States of America
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Alexandra J. Lee
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Genomics and Computational Biology Graduate Program, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Marc G. Chevrette
- Wisconsin Institute for Discovery and Department of Plant Pathology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Paul Allen Stewart
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, Florida, United States of America
| | - Thiago Britto-Borges
- Section of Bioinformatics and Systems Cardiology, Klaus Tschira Institute for Integrative Computational Cardiology, University Hospital Heidelberg, Heidelberg, Germany
- Department of Internal Medicine III (Cardiology, Angiology, and Pneumology), University Hospital Heidelberg, Heidelberg, Germany
| | - Evan M. Cofer
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
- Graduate Program in Quantitative and Computational Biology, Princeton University, Princeton, New Jersey, United States of America
| | - Kun-Hsing Yu
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Juan Jose Carmona
- Philips Healthcare, Cambridge, Massachusetts, United States of America
| | - Elana J. Fertig
- Department of Oncology, Johns Hopkins University, Baltimore, Maryland, United States of America
- Department of Biomedical Engineering, Department of Applied Mathematics and Statistics, Convergence Institute, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Alexandr A. Kalinin
- Medical Big Data Group, Shenzhen Research Institute of Big Data, Shenzhen, China
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Brandon Signal
- School of Medicine, College of Health and Medicine, University of Tasmania, Hobart, Australia
| | - Benjamin J. Lengerich
- Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Timothy J. Triche
- Center for Epigenetics, Van Andel Research Institute, Grand Rapids, Michigan, United States of America
- Department of Pediatrics, College of Human Medicine, Michigan State University, East Lansing, Michigan, United States of America
- Department of Translational Genomics, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Simina M. Boca
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, District of Columbia, United States of America
- Department of Oncology, Georgetown University Medical Center, Washington, DC, United States of America
- Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University Medical Center, Washington, DC, United States of America
- Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Washington, DC, United States of America
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Schapiro D, Yapp C, Sokolov A, Reynolds SM, Chen YA, Sudar D, Xie Y, Muhlich J, Arias-Camison R, Arena S, Taylor AJ, Nikolov M, Tyler M, Lin JR, Burlingame EA, Chang YH, Farhi SL, Thorsson V, Venkatamohan N, Drewes JL, Pe'er D, Gutman DA, Herrmann MD, Gehlenborg N, Bankhead P, Roland JT, Herndon JM, Snyder MP, Angelo M, Nolan G, Swedlow JR, Schultz N, Merrick DT, Mazzili SA, Cerami E, Rodig SJ, Santagata S, Sorger PK. MITI minimum information guidelines for highly multiplexed tissue images. Nat Methods 2022; 19:262-267. [PMID: 35277708 PMCID: PMC9009186 DOI: 10.1038/s41592-022-01415-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
The imminent release of tissue atlases combining multi-channel microscopy with single cell sequencing and other omics data from normal and diseased specimens creates an urgent need for data and metadata standards that guide data deposition, curation and release. We describe a Minimum Information about highly multiplexed Tissue Imaging (MITI) standard that applies best practices developed for genomics and other microscopy data to highly multiplexed tissue images and traditional histology.
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Affiliation(s)
- Denis Schapiro
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University Hospital and Heidelberg University, Heidelberg, Germany
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Clarence Yapp
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
- Image and Data Analysis Core, Harvard Medical School, Boston, MA, USA
| | - Artem Sokolov
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | | | - Yu-An Chen
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
| | - Damir Sudar
- Quantitative Imaging Systems LLC, Portland, OR, USA
| | - Yubin Xie
- Program in Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jeremy Muhlich
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
| | - Raquel Arias-Camison
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
| | - Sarah Arena
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
| | | | | | - Madison Tyler
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
| | - Jia-Ren Lin
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
| | - Erik A Burlingame
- Oregon Health and Science University, Portland, OR, USA
- Indica Labs, Albuquerque, NM, USA
| | - Young H Chang
- Oregon Health and Science University, Portland, OR, USA
| | - Samouil L Farhi
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Julia L Drewes
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Dana Pe'er
- Program in Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Markus D Herrmann
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Peter Bankhead
- Edinburgh Pathology, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Joseph T Roland
- Vanderbilt University School of Medicine, Nashville, TN, USA
| | - John M Herndon
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Michael Angelo
- School of Medicine, Stanford University, Stanford, CA, USA
| | - Garry Nolan
- School of Medicine, Stanford University, Stanford, CA, USA
| | - Jason R Swedlow
- Division of Computational Biology and Centre for Gene Regulation and Expression, University of Dundee, Dundee, UK
| | - Nikolaus Schultz
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | | | | | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Sandro Santagata
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA.
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA.
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
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43
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Kim Y. Data sharing by biologists: A comparative study of genome sequence data and lab experiment data. LIBRARY & INFORMATION SCIENCE RESEARCH 2022. [DOI: 10.1016/j.lisr.2022.101139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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McGrew S, Raskoff S, Berkman BE. When Not to Ask: A Defense of Choice-Masking Nudges in Medical Research. JOURNAL OF HEALTH CARE LAW & POLICY 2022; 25:1-48. [PMID: 37034557 PMCID: PMC10078241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
Abstract
In this article, we examine the legality and ethics of a controversial but widespread practice in clinical research: choice-masking nudges. A choice-masking nudge (CMN) exists when a research team explicitly obscures a meaningful choice from participants by presenting a default decision as the standard way forward. Even though an easy-to-use opt-out mechanism is available for participants who independently express concerns with the standard default, the fact that a default has been pre-selected is not made obvious to research participants. To opt out of the nudge, a participant must overtly request non-standard treatment. We argue that use of such nudges in medical research can be justified by their individual, collective, and social benefits, provided that they respect autonomy and satisfy our four further acceptability conditions. The structure of this Article is as follows. In Part II, we describe three controversial cases of CMNs in medical research. In Part III, we provide background on nudging and explain how our proposed CMNs fit into the existing literature on nudging and libertarian paternalism. In Part IV, we explain how the reasonable person standard as employed by United States research regulations can be used to support CMNs. In Part IV, we anticipate some of the strongest objections to CMNs by explaining how CMNs are compatible with a wide range of plausible accounts of autonomy. Finally, in Part VI, we discuss four additional core considerations an acceptable CMN must meet: legitimate policy goals; benefits outweighing harms; burdens distributed fairly; and absence of ethically superior feasible alternatives. We also analyze the three existing controversies explored in Part II and show how each would benefit from the conceptual clarity offered by our analytic framework. Medical research is complicated and can be difficult for participants to understand; thoughtfully designed CMNs can play an important role in gently guiding large numbers of research participants toward decision outcomes that really are best for them and their communities.
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Affiliation(s)
- Susanna McGrew
- in the Department of Bioethics at the National Institutes of Health
| | - Sarah Raskoff
- in the Department of Bioethics at the National Institutes of Health
| | - Benjamin E Berkman
- Department of Bioethics at the National Institutes of Health, where he is the head of the section on the ethics of genetics and emerging technologies. He has a joint appointment in the National Human Genome Research Institute, where he serves as the Deputy Director of the NHGRI Bioethics Core
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Rhee SY, Kassaye SG, Jordan MR, Kouamou V, Katzenstein D, Shafer RW. Public availability of HIV-1 drug resistance sequence and treatment data: a systematic review. THE LANCET MICROBE 2022; 3:e392-e398. [PMID: 35544100 PMCID: PMC9095989 DOI: 10.1016/s2666-5247(21)00250-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 08/12/2021] [Accepted: 09/06/2021] [Indexed: 11/28/2022] Open
Abstract
HIV-1 pol sequences from antiretroviral therapy (ART)-naive and ART-experienced people living with HIV-1 are fundamental to understanding the genetic correlates and epidemiology of HIV-1 drug resistance (HIVDR). To assess the public availability of HIV-1 pol sequences and ART histories of the individuals from whom sequenced viruses were obtained, we performed a systematic review of PubMed and GenBank for HIVDR studies published between 2010 and 2019 that reported HIV-1 pol sequences. 934 studies met inclusion criteria, including 461 studies of ART-naive adults, 407 of ART-experienced adults, and 66 of ART-naive and ART-experienced children. Sequences were available for 317 (68·8%) studies of ART-naive individuals, 190 (46·7%) of ART-experienced individuals, and 45 (68·2%) of children. Among ART-experienced individuals, sequences plus linked ART histories were available for 82 (20·1%) studies. Sequences were available for 21 (29·2%) of 72 clinical trials. Among journals publishing more than ten studies, the proportion with available sequences ranged from 8·3% to 86·9%. Strengthened implementation of data sharing policies is required to increase the number of studies with available HIVDR data to support the enterprise of global ART in the face of emerging HIVDR. the Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations.
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Affiliation(s)
- Soo-Yon Rhee
- Department of Medicine, Stanford University, Stanford, CA, USA.
| | - Seble G Kassaye
- Department of Medicine, Georgetown University, Washington, DC, USA
| | - Michael R Jordan
- Levy Center for Integrated Management of Antimicrobial Resistance, Tufts University, Boston, MA, USA; Division of Geographic Medicine and Infectious Diseases, Tufts Medical Center, Boston, MA, USA
| | - Vinie Kouamou
- Unit of Medicine, Faculty of Medicine and Health Sciences, University of Zimbabwe, Harare, Zimbabwe
| | - David Katzenstein
- Department of Molecular Biology, Biomedical Research and Training Institute, Harare, Zimbabwe
| | - Robert W Shafer
- Department of Medicine, Stanford University, Stanford, CA, USA
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46
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Dagenais S, Russo L, Madsen A, Webster J, Becnel L. Use of Real-World Evidence to Drive Drug Development Strategy and Inform Clinical Trial Design. Clin Pharmacol Ther 2022; 111:77-89. [PMID: 34839524 PMCID: PMC9299990 DOI: 10.1002/cpt.2480] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 10/30/2021] [Indexed: 12/28/2022]
Abstract
Interest in real-world data (RWD) and real-world evidence (RWE) to expedite and enrich the development of new biopharmaceutical products has proliferated in recent years, spurred by the 21st Century Cures Act in the United States and similar policy efforts in other countries, willingness by regulators to consider RWE in their decisions, demands from third-party payers, and growing concerns about the limitations of traditional clinical trials. Although much of the recent literature on RWE has focused on potential regulatory uses (e.g., product approvals in oncology or rare diseases based on single-arm trials with external control arms), this article reviews how biopharmaceutical companies can leverage RWE to inform internal decisions made throughout the product development process. Specifically, this article will review use of RWD to guide pipeline and portfolio strategy; use of novel sources of RWD to inform product development, use of RWD to inform clinical development, use of advanced analytics to harness "big" RWD, and considerations when using RWD to inform internal decisions. Topics discussed will include the use of molecular, clinicogenomic, medical imaging, radiomic, and patient-derived xenograft data to augment traditional sources of RWE, the use of RWD to inform clinical trial eligibility criteria, enrich trial population based on predicted response, select endpoints, estimate sample size, understand disease progression, and enhance diversity of participants, the growing use of data tokenization and advanced analytical techniques based on artificial intelligence in RWE, as well as the importance of data quality and methodological transparency in RWE.
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Affiliation(s)
| | - Leo Russo
- Global Medical Epidemiology, Worldwide Medical and SafetyPfizer IncCollegevillePennsylvaniaUSA
| | - Ann Madsen
- Global Medical Epidemiology, Worldwide Medical and SafetyPfizer IncNew YorkNew YorkUSA
| | - Jen Webster
- Real World EvidencePfizer IncNew YorkNew YorkUSA
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47
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Brambilla Pisoni G, Taddeo M. Apropos Data Sharing: Abandon the Distrust and Embrace the Opportunity. DNA Cell Biol 2022; 41:11-15. [PMID: 34941450 PMCID: PMC8787700 DOI: 10.1089/dna.2021.0501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 09/21/2021] [Accepted: 10/05/2021] [Indexed: 11/16/2022] Open
Abstract
In this commentary, we focus on the ethical challenges of data sharing and its potential in supporting biomedical research. Taking human genomics (HG) and European governance for sharing genomic data as a case study, we consider how to balance competing rights and interests-balancing protection of the privacy of data subjects and data security, with scientific progress and the need to promote public health. This is of particular relevancy in light of the current pandemic, which stresses the urgent need for international collaborations to promote health for all. We draw from existing ethical codes for data sharing in HG to offer recommendations as to how to protect rights while fostering scientific research and open science.
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Affiliation(s)
| | - Mariarosaria Taddeo
- Oxford Internet Institute, University of Oxford, Oxford, United Kingdom
- Alan Turing Institute, London, United Kingdom
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48
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Hartley C. Toward personalized medicine for pharmacological interventions in neonates using vital signs. PAEDIATRIC AND NEONATAL PAIN 2021; 3:147-155. [PMID: 35372840 PMCID: PMC8937573 DOI: 10.1002/pne2.12065] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 10/22/2021] [Accepted: 11/15/2021] [Indexed: 11/17/2022]
Abstract
Vital signs, such as heart rate and oxygen saturation, are continuously monitored for infants in neonatal care units. Pharmacological interventions can alter an infant's vital signs, either as an intended effect or as a side effect, and consequently could provide an approach to explore the wide variability in pharmacodynamics across infants and could be used to develop models to predict outcome (efficacy or adverse effects) in an individual infant. This will enable doses to be tailored according to the individual, shifting the balance toward efficacy and away from the adverse effects of a drug. Pharmacological analgesics are frequently not given in part due to the risk of adverse effects, yet this exposes infants to the short‐ and long‐term effects of painful procedures. Personalized analgesic dosing will be an important step forward in providing safer effective pain relief in infants. The aim of this paper was to describe a framework to develop predictive models of drug outcome from analysis of vital signs data, focusing on analgesics as a representative example. This framework investigates changes in vital signs in response to the analgesic (prior to the painful procedure) and proposes using machine learning to examine if these changes are predictive of outcome—either efficacy (with pain response measured using a multimodal approach, as changes in vital signs alone have limited sensitivity and specificity) or adverse effects. The framework could be applied to both preterm and term infants in neonatal care units, as well as older children. Sharing vital signs data are proposed as a means to achieve this aim and bring personalized medicine rapidly to the forefront in neonatology.
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49
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Heil BJ, Hoffman MM, Markowetz F, Lee SI, Greene CS, Hicks SC. Reproducibility standards for machine learning in the life sciences. Nat Methods 2021; 18:1132-1135. [PMID: 34462593 PMCID: PMC9131851 DOI: 10.1038/s41592-021-01256-7] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
To make machine learning analyses in the life sciences more computationally reproducible, we propose standards based on data, model, and code publication, programming best practices, and workflow automation. By meeting these standards, the community of researchers applying machine learning methods in the life sciences can ensure that their analyses are worthy of trust. this article has been peer reviewed.
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Affiliation(s)
- Benjamin J Heil
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael M Hoffman
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Florian Markowetz
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Su-In Lee
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Casey S Greene
- Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, CO, USA.
- Center for Health AI, University of Colorado School of Medicine, Aurora, CO, USA.
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
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50
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Way GP, Greene CS, Carninci P, Carvalho BS, de Hoon M, Finley SD, Gosline SJC, Lȇ Cao KA, Lee JSH, Marchionni L, Robine N, Sindi SS, Theis FJ, Yang JYH, Carpenter AE, Fertig EJ. A field guide to cultivating computational biology. PLoS Biol 2021; 19:e3001419. [PMID: 34618807 PMCID: PMC8525744 DOI: 10.1371/journal.pbio.3001419] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Revised: 10/19/2021] [Indexed: 11/18/2022] Open
Abstract
Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.
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Affiliation(s)
- Gregory P. Way
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- Center for Health AI, University of Colorado School of Medicine, Aurora, Colorado, United States of America
| | - Casey S. Greene
- Center for Health AI, University of Colorado School of Medicine, Aurora, Colorado, United States of America
| | - Piero Carninci
- RIKEN Center for Integrative Medical Sciences Yokohama, Kanagawa, Japan
- Human Technopole, Milan, Italy
| | - Benilton S. Carvalho
- Department of Statistics, Institute of Mathematics, Statistics and Scientific Computing, University of Campinas, Campinas, Brazil
| | - Michiel de Hoon
- RIKEN Center for Integrative Medical Sciences Yokohama, Kanagawa, Japan
| | - Stacey D. Finley
- Department of Biomedical Engineering, Quantitative and Computational Biology, and Chemical Engineering & Materials Science, University of Southern California, Los Angeles, California, United States of America
| | - Sara J. C. Gosline
- Pacific Northwest National Laboratory, Seattle, Washington, United States of America
| | - Kim-Anh Lȇ Cao
- Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
| | - Jerry S. H. Lee
- Ellison Institute and Departments of Medicine/Oncology, Chemical Engineering, and Material Sciences, University of Southern California, Los Angeles, California, United States of America
| | - Luigi Marchionni
- Department of Pathology and Laboratory Medicine, Weill-Cornell Medicine, New York, New York, United States of America
| | - Nicolas Robine
- Computational Biology Lab, New York Genome Center, New York, New York, United States of America
| | - Suzanne S. Sindi
- Department of Applied Mathematics, University of California Merced, Merced, California, United States of America
| | - Fabian J. Theis
- Institute of Computational Biology, Helmholtz Center Munich and Department of Mathematics, Technical University of Munich, Munich, Germany
| | - Jean Y. H. Yang
- Charles Perkins Centre and School of Mathematics and Statistics, The University of Sydney, Australia
| | - Anne E. Carpenter
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Elana J. Fertig
- Convergence Institute, Departments of Oncology, Biomedical Engineering, and Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, Maryland, United States of America
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