1
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Crowdsourced benchmarking of taxonomic metagenome profilers: lessons learned from the sbv IMPROVER Microbiomics challenge. BMC Genomics 2022; 23:624. [PMID: 36042406 PMCID: PMC9429340 DOI: 10.1186/s12864-022-08803-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 07/25/2022] [Indexed: 11/10/2022] Open
Abstract
Background Selection of optimal computational strategies for analyzing metagenomics data is a decisive step in determining the microbial composition of a sample, and this procedure is complex because of the numerous tools currently available. The aim of this research was to summarize the results of crowdsourced sbv IMPROVER Microbiomics Challenge designed to evaluate the performance of off-the-shelf metagenomics software as well as to investigate the robustness of these results by the extended post-challenge analysis. In total 21 off-the-shelf taxonomic metagenome profiling pipelines were benchmarked for their capacity to identify the microbiome composition at various taxon levels across 104 shotgun metagenomics datasets of bacterial genomes (representative of various microbiome samples) from public databases. Performance was determined by comparing predicted taxonomy profiles with the gold standard. Results Most taxonomic profilers performed homogeneously well at the phylum level but generated intermediate and heterogeneous scores at the genus and species levels, respectively. kmer-based pipelines using Kraken with and without Bracken or using CLARK-S performed best overall, but they exhibited lower precision than the two marker-gene-based methods MetaPhlAn and mOTU. Filtering out the 1% least abundance species—which were not reliably predicted—helped increase the performance of most profilers by increasing precision but at the cost of recall. However, the use of adaptive filtering thresholds determined from the sample’s Shannon index increased the performance of most kmer-based profilers while mitigating the tradeoff between precision and recall. Conclusions kmer-based metagenomic pipelines using Kraken/Bracken or CLARK-S performed most robustly across a large variety of microbiome datasets. Removing non-reliably predicted low-abundance species by using diversity-dependent adaptive filtering thresholds further enhanced the performance of these tools. This work demonstrates the applicability of computational pipelines for accurately determining taxonomic profiles in clinical and environmental contexts and exemplifies the power of crowdsourcing for unbiased evaluation. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08803-2.
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2
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Lucas M. Future Challenges in Plant Systems Biology. Methods Mol Biol 2022; 2395:325-337. [PMID: 34822161 DOI: 10.1007/978-1-0716-1816-5_16] [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] [Indexed: 06/13/2023]
Abstract
Plant systems biology is currently facing several important challenges, whose nature depend on the considered frame of reference and associated scale. This review covers some of the issues associated respectively with the molecular, tissue, and whole-plant scales, as well as discusses the potential for latest advances in synthetic biology and machine-learning methods to be of use in the future of plant systems biology.
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Affiliation(s)
- Mikaël Lucas
- DIADE, Univ Montpellier, IRD, CIRAD, Montpellier, France.
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3
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Abstract
The current focus on biomedical research has led to dearth of funding for basic research in evolution or ecology with the aim to better understand life.
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Affiliation(s)
- Srđan Kesić
- Institute for Biological ResearchBelgradeSerbia
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4
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Mangul S, Martin LS, Hill BL, Lam AKM, Distler MG, Zelikovsky A, Eskin E, Flint J. Systematic benchmarking of omics computational tools. Nat Commun 2019; 10:1393. [PMID: 30918265 PMCID: PMC6437167 DOI: 10.1038/s41467-019-09406-4] [Citation(s) in RCA: 86] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Accepted: 03/06/2019] [Indexed: 01/11/2023] Open
Abstract
Computational omics methods packaged as software have become essential to modern biological research. The increasing dependence of scientists on these powerful software tools creates a need for systematic assessment of these methods, known as benchmarking. Adopting a standardized benchmarking practice could help researchers who use omics data to better leverage recent technological innovations. Our review summarizes benchmarking practices from 25 recent studies and discusses the challenges, advantages, and limitations of benchmarking across various domains of biology. We also propose principles that can make computational biology benchmarking studies more sustainable and reproducible, ultimately increasing the transparency of biomedical data and results. Benchmarking studies are important for comprehensively understanding and evaluating different computational omics methods. Here, the authors review practices from 25 recent studies and propose principles to improve the quality of benchmarking studies.
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Affiliation(s)
- Serghei Mangul
- Department of Computer Science, University of California Los Angeles, 580 Portola Plaza, Los Angeles, CA, 90095, USA. .,Institute for Quantitative and Computational Biosciences, University of California Los Angeles, 611 Charles E Young Drive East, Los Angeles, CA, 90095, USA.
| | - Lana S Martin
- Institute for Quantitative and Computational Biosciences, University of California Los Angeles, 611 Charles E Young Drive East, Los Angeles, CA, 90095, USA
| | - Brian L Hill
- Department of Computer Science, University of California Los Angeles, 580 Portola Plaza, Los Angeles, CA, 90095, USA
| | - Angela Ka-Mei Lam
- Department of Computer Science, University of California Los Angeles, 580 Portola Plaza, Los Angeles, CA, 90095, USA
| | - Margaret G Distler
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Alex Zelikovsky
- Department of Computer Science, Georgia State University, Atlanta, GA, 30303, USA.,The Laboratory of Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Eleazar Eskin
- Department of Computer Science, University of California Los Angeles, 580 Portola Plaza, Los Angeles, CA, 90095, USA.,Department of Human Genetics, University of California Los Angeles, 695 Charles E. Young, Los Angeles, CA, USA
| | - Jonathan Flint
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095, USA
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5
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Jurman G, Filosi M, Visintainer R, Riccadonna S, Furlanello C. Stability in GRN Inference. Methods Mol Biol 2019; 1883:323-346. [PMID: 30547407 DOI: 10.1007/978-1-4939-8882-2_14] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Reconstructing a gene regulatory network from one or more sets of omics measurements has been a major task of computational biology in the last 20 years. Despite an overwhelming number of algorithms proposed to solve the network inference problem either in the general scenario or in an ad-hoc tailored situation, assessing the stability of reconstruction is still an uncharted territory and exploratory studies mainly tackled theoretical aspects. We introduce here empirical stability, which is induced by variability of reconstruction as a function of data subsampling. By evaluating differences between networks that are inferred using different subsets of the same data we obtain quantitative indicators of the robustness of the algorithm, of the noise level affecting the data, and, overall, of the reliability of the reconstructed graph. We show that empirical stability can be used whenever no ground truth is available to compute a direct measure of the similarity between the inferred structure and the true network. The main ingredient here is a suite of indicators, called NetSI, providing statistics of distances between graphs generated by a given algorithm fed with different data subsets, where the chosen metric is the Hamming-Ipsen-Mikhailov (HIM) distance evaluating dissimilarity of graph topologies with shared nodes. Operatively, the NetSI family is demonstrated here on synthetic and high-throughput datasets, inferring graphs at different resolution levels (topology, direction, weight), showing how the stability indicators can be effectively used for the quantitative comparison of the stability of different reconstruction algorithms.
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Affiliation(s)
| | | | - Roberto Visintainer
- The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
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6
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Lee AY, Ewing AD, Ellrott K, Hu Y, Houlahan KE, Bare JC, Espiritu SMG, Huang V, Dang K, Chong Z, Caloian C, Yamaguchi TN, Kellen MR, Chen K, Norman TC, Friend SH, Guinney J, Stolovitzky G, Haussler D, Margolin AA, Stuart JM, Boutros PC. Combining accurate tumor genome simulation with crowdsourcing to benchmark somatic structural variant detection. Genome Biol 2018; 19:188. [PMID: 30400818 PMCID: PMC6219177 DOI: 10.1186/s13059-018-1539-5] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Accepted: 09/12/2018] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND The phenotypes of cancer cells are driven in part by somatic structural variants. Structural variants can initiate tumors, enhance their aggressiveness, and provide unique therapeutic opportunities. Whole-genome sequencing of tumors can allow exhaustive identification of the specific structural variants present in an individual cancer, facilitating both clinical diagnostics and the discovery of novel mutagenic mechanisms. A plethora of somatic structural variant detection algorithms have been created to enable these discoveries; however, there are no systematic benchmarks of them. Rigorous performance evaluation of somatic structural variant detection methods has been challenged by the lack of gold standards, extensive resource requirements, and difficulties arising from the need to share personal genomic information. RESULTS To facilitate structural variant detection algorithm evaluations, we create a robust simulation framework for somatic structural variants by extending the BAMSurgeon algorithm. We then organize and enable a crowdsourced benchmarking within the ICGC-TCGA DREAM Somatic Mutation Calling Challenge (SMC-DNA). We report here the results of structural variant benchmarking on three different tumors, comprising 204 submissions from 15 teams. In addition to ranking methods, we identify characteristic error profiles of individual algorithms and general trends across them. Surprisingly, we find that ensembles of analysis pipelines do not always outperform the best individual method, indicating a need for new ways to aggregate somatic structural variant detection approaches. CONCLUSIONS The synthetic tumors and somatic structural variant detection leaderboards remain available as a community benchmarking resource, and BAMSurgeon is available at https://github.com/adamewing/bamsurgeon .
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Affiliation(s)
- Anna Y Lee
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Adam D Ewing
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA.,Mater Research Institute, University of Queensland, Woolloongabba, QLD, Australia
| | - Kyle Ellrott
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA.,Computational Biology Program, Oregon Health & Science University, Portland, OR, USA
| | - Yin Hu
- Sage Bionetworks, Seattle, WA, USA
| | | | | | | | - Vincent Huang
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | | | - Zechen Chong
- Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Department of Genetics, University of Alabama at Birmingham, Birmingham, AL, USA.,Informatics Institute, University of Alabama at Birmingham, Birmingham, AL, USA
| | | | | | | | | | - Ken Chen
- Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | | | | | - Gustavo Stolovitzky
- IBM Computational Biology Center, T.J.Watson Research Center, Yorktown Heights, NY, USA
| | - David Haussler
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Adam A Margolin
- Computational Biology Program, Oregon Health & Science University, Portland, OR, USA. .,Sage Bionetworks, Seattle, WA, USA.
| | - Joshua M Stuart
- Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA.
| | - Paul C Boutros
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada. .,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada. .,Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada.
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7
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Abstract
Low rates of reproducibility and translatability of data from nonclinical research have been reported. Major causes of irreproducibility include oversights in study design, failure to characterize reagents and protocols, a lack of access to detailed methods and data, and an absence of universally accepted and applied standards and guidelines. Specific areas of concern include uncharacterized antibodies and cell lines, the use of inappropriate sampling and testing protocols, a lack of transparency and access to raw data, and deficiencies in the translatability of findings to the clinic from studies using animal models of disease. All stakeholders—academia, industry, funding agencies, regulators, nonprofit entities, and publishers—are encouraged to play active roles in addressing these challenges by formulating and promoting access to best practices and standard operating procedures and validating data collaboratively at each step of the biomedical research life cycle.
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Affiliation(s)
- Stéphanie Boué
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
| | - Michael Byrne
- Global Biological Standards Institute (GBSI), Washington, DC, USA
| | - A Wallace Hayes
- Michigan State University, Institute for Integrative Toxicology, East Lansing, MI, USA.,University of South Florida, College of Public Health, Tampa, FL, USA
| | - Julia Hoeng
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
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8
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Lavarenne J, Guyomarc'h S, Sallaud C, Gantet P, Lucas M. The Spring of Systems Biology-Driven Breeding. TRENDS IN PLANT SCIENCE 2018; 23:706-720. [PMID: 29764727 DOI: 10.1016/j.tplants.2018.04.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Revised: 04/12/2018] [Accepted: 04/16/2018] [Indexed: 05/08/2023]
Abstract
Genetics and molecular biology have contributed to the development of rationalized plant breeding programs. Recent developments in both high-throughput experimental analyses of biological systems and in silico data processing offer the possibility to address the whole gene regulatory network (GRN) controlling a given trait. GRN models can be applied to identify topological features helping to shortlist potential candidate genes for breeding purposes. Time-series data sets can be used to support dynamic modelling of the network. This will enable a deeper comprehension of network behaviour and the identification of the few elements to be genetically rewired to push the system towards a modified phenotype of interest. This paves the way to design more efficient, systems biology-based breeding strategies.
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Affiliation(s)
- Jérémy Lavarenne
- UMR DIADE, Université de Montpellier, IRD, 911 Avenue Agropolis, 34394 Montpellier cedex 5, France; Biogemma, Centre de Recherches de Chappes, Route d'Ennezat, 63720 Chappes, France
| | - Soazig Guyomarc'h
- UMR DIADE, Université de Montpellier, IRD, 911 Avenue Agropolis, 34394 Montpellier cedex 5, France
| | - Christophe Sallaud
- Biogemma, Centre de Recherches de Chappes, Route d'Ennezat, 63720 Chappes, France
| | - Pascal Gantet
- UMR DIADE, Université de Montpellier, IRD, 911 Avenue Agropolis, 34394 Montpellier cedex 5, France.
| | - Mikaël Lucas
- UMR DIADE, Université de Montpellier, IRD, 911 Avenue Agropolis, 34394 Montpellier cedex 5, France
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9
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Poussin C, Sierro N, Boué S, Battey J, Scotti E, Belcastro V, Peitsch MC, Ivanov NV, Hoeng J. Interrogating the microbiome: experimental and computational considerations in support of study reproducibility. Drug Discov Today 2018; 23:1644-1657. [PMID: 29890228 DOI: 10.1016/j.drudis.2018.06.005] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 05/03/2018] [Accepted: 06/06/2018] [Indexed: 12/12/2022]
Abstract
The microbiome is an important factor in human health and disease and is investigated to develop novel therapeutics. Metagenomics leverages advances in sequencing technologies and computational analysis to identify and quantify the microorganisms present in a sample. This field has, however, not yet reached maturity and the international metagenomics community, aware of the current limitations and of the necessity for standardization, has started investigating sources of variability in experimental and computational workflows. The first studies have already resulted in the identification of crucial steps and factors affecting metagenomics data quality, quantification and interpretation. This review summarizes experimental and computational considerations for interrogating the microbiome and establishing reproducible and robust analysis workflows.
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Affiliation(s)
- Carine Poussin
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland
| | - Nicolas Sierro
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland
| | - Stéphanie Boué
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland
| | - James Battey
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland
| | - Elena Scotti
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland
| | - Vincenzo Belcastro
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland
| | - Manuel C Peitsch
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland
| | - Nikolai V Ivanov
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland
| | - Julia Hoeng
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland.
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10
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Peitsch MC, Polosa R, Proctor C, Hassler T, Gaca M, Hill E, Hoeng J, Hayes AW. Next-generation tobacco and nicotine products. TOXICOLOGY RESEARCH AND APPLICATION 2018. [DOI: 10.1177/2397847318773701] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
A framework for the assessment of novel next-generation tobacco and nicotine products with the potential to reduce health risks compared with cigarettes should integrate scientific studies incorporating nonclinical, clinical, and population studies approaches. These products should have lower emissions and exhibit reduced biological effects compared with those of cigarettes, ideally approaching those of smoking cessation. The products should also be acceptable cigarette alternatives for current smokers, while not appealing to nonsmokers or former smokers. Validating harm reduction and promoting regulatory acceptance of the assessment methods require a collaborative approach by industry, independent reviewers, the public health community, and regulatory agencies. This article summarizes the science and approaches applied and considered to substantiate tobacco harm reduction in the light of regulatory requirements, presented at the 53rd Congress of the European Societies of Toxicology, 2017.
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Affiliation(s)
- Manuel C Peitsch
- Philip Morris International R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
| | - Riccardo Polosa
- Dipartimento di Medicina Clinica e Sperimentale, University of Catania, Catania, Italy
| | - Christopher Proctor
- British American Tobacco (Investments) Ltd., Group Research and Development, Southampton, UK
| | - Thord Hassler
- Swedish Match, European Division, Gothenburg, Sweden
| | - Marianna Gaca
- British American Tobacco (Investments) Ltd., Group Research and Development, Southampton, UK
| | - Erin Hill
- Institute for In Vitro Sciences, Inc., Gaithersburg, MD, USA
| | - Julia Hoeng
- Philip Morris International R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
| | - A Wallace Hayes
- Institute of Integrated Toxicology, Michigan State University, East Lansing, MI, USA
- College of Public Health, University of South Florida, Tampa, FL, USA
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11
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Mueller AJ, Peffers MJ, Proctor CJ, Clegg PD. Systems approaches in osteoarthritis: Identifying routes to novel diagnostic and therapeutic strategies. J Orthop Res 2017; 35:1573-1588. [PMID: 28318047 PMCID: PMC5574007 DOI: 10.1002/jor.23563] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Accepted: 03/06/2017] [Indexed: 02/04/2023]
Abstract
Systems orientated research offers the possibility of identifying novel therapeutic targets and relevant diagnostic markers for complex diseases such as osteoarthritis. This review demonstrates that the osteoarthritis research community has been slow to incorporate systems orientated approaches into research studies, although a number of key studies reveal novel insights into the regulatory mechanisms that contribute both to joint tissue homeostasis and its dysfunction. The review introduces both top-down and bottom-up approaches employed in the study of osteoarthritis. A holistic and multiscale approach, where clinical measurements may predict dysregulation and progression of joint degeneration, should be a key objective in future research. The review concludes with suggestions for further research and emerging trends not least of which is the coupled development of diagnostic tests and therapeutics as part of a concerted effort by the osteoarthritis research community to meet clinical needs. © 2017 The Authors. Journal of Orthopaedic Research Published by Wiley Periodicals, Inc. on behalf of Orthopaedic Research Society. J Orthop Res 35:1573-1588, 2017.
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Affiliation(s)
- Alan J. Mueller
- Faculty of Health and Life SciencesDepartment of Musculoskeletal BiologyInstitute of Ageing and Chronic DiseaseUniversity of LiverpoolWilliam Henry Duncan Building, 6 West Derby StreetLiverpoolL7 8TXUnited Kingdom
| | - Mandy J. Peffers
- Faculty of Health and Life SciencesDepartment of Musculoskeletal BiologyInstitute of Ageing and Chronic DiseaseUniversity of LiverpoolWilliam Henry Duncan Building, 6 West Derby StreetLiverpoolL7 8TXUnited Kingdom,The MRC‐Arthritis Research UK Centre for Integrated Research into Musculoskeletal Ageing (CIMA)LiverpoolUnited Kingdom
| | - Carole J. Proctor
- The MRC‐Arthritis Research UK Centre for Integrated Research into Musculoskeletal Ageing (CIMA)LiverpoolUnited Kingdom,Institute of Cellular MedicineNewcastle UniversityFramlington PlaceNewcastle upon TyneNE2 4HHUnited Kingdom
| | - Peter D. Clegg
- Faculty of Health and Life SciencesDepartment of Musculoskeletal BiologyInstitute of Ageing and Chronic DiseaseUniversity of LiverpoolWilliam Henry Duncan Building, 6 West Derby StreetLiverpoolL7 8TXUnited Kingdom,The MRC‐Arthritis Research UK Centre for Integrated Research into Musculoskeletal Ageing (CIMA)LiverpoolUnited Kingdom
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12
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Tarca AL, Gong X, Romero R, Yang W, Duan Z, Yang H, Zhang C, Wang P. Human blood gene signature as a marker for smoking exposure: computational approaches of the top ranked teams in the sbv IMPROVER Systems Toxicology challenge. ACTA ACUST UNITED AC 2017; 5:31-37. [PMID: 29556588 DOI: 10.1016/j.comtox.2017.07.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Crowdsourcing has emerged as a framework to address methodological challenges in omics data analysis and assess the extent to which omics data are predictive of phenotypes of interest. The sbv IMPROVER Systems Toxicology Challenge was designed to leverage crowdsourcing to determine whether human blood gene expression levels are informative of current and past smoking. Participating teams were invited to use a training gene expression dataset to derive parsimonious models (up to 40 genes) that can accurately classify subjects into exposure groups: smokers, former smokers that quit for at least one year, and never-smokers. Teams were ranked based on two classification performance metrics evaluated on a blinded test dataset. The analytical approaches of the first- and third-ranked teams, that are presented in detail in this article, involved feature selection by moderated t-test or LASSO regression and linear discriminant analysis (LDA) and logistic regression classifiers, respectively. While the 12-gene signature of the top team allowed the classification of current smokers with 100% sensitivity at 93% specificity, discriminating former smokers from never-smokers was much more challenging (65% sensitivity at 57% specificity). Gene ontology molecular functions and KEGG pathways associated with current smoking included G protein-coupled receptor activity, signaling receptor activity, calcium ion binding, and the Neuroactive ligand-receptor interaction pathway. Selection of marker genes by either moderated t-test or multivariate LASSO regression followed by LDA or logistic regression, are robust approaches to classification with omics data, confirming in part findings of previous sbv IMPROVER challenges. While current smoking is accurately identified based on blood mRNA levels, smoking cessation for more than one year is accompanied by a "normalization" of the expression of certain mRNAs, making it difficult to distinguish former smokers from never-smokers.
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Affiliation(s)
- Adi L Tarca
- Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI, 48202, USA.,Department of Computer Science, Wayne State University College of Engineering, Detroit, MI, USA
| | - Xiaofeng Gong
- SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China.,Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Roberto Romero
- Perinatology Research Branch, NICHD/NIH/DHHS, Bethesda, MD, and Detroit, MI, 48201, USA.,Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI, 48109, USA.,Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, 48825, USA.,Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, 48201, USA
| | - Wenxin Yang
- SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China.,School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China
| | - Zhongqu Duan
- SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China.,Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Hao Yang
- SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China.,School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China
| | - Chengfang Zhang
- SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China.,Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Peixuan Wang
- SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China.,Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
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13
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Talikka M, Bukharov N, Hayes WS, Hofmann-Apitius M, Alexopoulos L, Peitsch MC, Hoeng J. Novel approaches to develop community-built biological network models for potential drug discovery. Expert Opin Drug Discov 2017; 12:849-857. [PMID: 28585481 DOI: 10.1080/17460441.2017.1335302] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
INTRODUCTION Hundreds of thousands of data points are now routinely generated in clinical trials by molecular profiling and NGS technologies. A true translation of this data into knowledge is not possible without analysis and interpretation in a well-defined biology context. Currently, there are many public and commercial pathway tools and network models that can facilitate such analysis. At the same time, insights and knowledge that can be gained is highly dependent on the underlying biological content of these resources. Crowdsourcing can be employed to guarantee the accuracy and transparency of the biological content underlining the tools used to interpret rich molecular data. Areas covered: In this review, the authors describe crowdsourcing in drug discovery. The focal point is the efforts that have successfully used the crowdsourcing approach to verify and augment pathway tools and biological network models. Technologies that enable the building of biological networks with the community are also described. Expert opinion: A crowd of experts can be leveraged for the entire development process of biological network models, from ontologies to the evaluation of their mechanistic completeness. The ultimate goal is to facilitate biomarker discovery and personalized medicine by mechanistically explaining patients' differences with respect to disease prevention, diagnosis, and therapy outcome.
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Affiliation(s)
- Marja Talikka
- a Philip Morris International R&D , Philip Morris Products S.A. , Neuchâtel , Switzerland
| | - Natalia Bukharov
- b Translational Data Management Services, Clarivate Analytics (Formerly the IP & Science Business of Thomson Reuters) , Boston , MA , USA
| | - William S Hayes
- c Data Sciences , Applied Dynamic Solutions, LLC , Rahway , NJ , USA
| | - Martin Hofmann-Apitius
- d Department of Bioinformatics , Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven , Sankt Augustin , Germany
| | - Leonidas Alexopoulos
- e Systems Bioengineering Lab , National Technical University of Athens , Zografou , Greece.,f Protavio Ltd , Stevenage , UK
| | - Manuel C Peitsch
- a Philip Morris International R&D , Philip Morris Products S.A. , Neuchâtel , Switzerland
| | - Julia Hoeng
- a Philip Morris International R&D , Philip Morris Products S.A. , Neuchâtel , Switzerland
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14
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Abstract
The generation of large-scale biomedical data is creating unprecedented opportunities for basic and translational science. Typically, the data producers perform initial analyses, but it is very likely that the most informative methods may reside with other groups. Crowdsourcing the analysis of complex and massive data has emerged as a framework to find robust methodologies. When the crowdsourcing is done in the form of collaborative scientific competitions, known as Challenges, the validation of the methods is inherently addressed. Challenges also encourage open innovation, create collaborative communities to solve diverse and important biomedical problems, and foster the creation and dissemination of well-curated data repositories.
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15
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Saraç ÖS, Kumar R, Dhanda SK, Balcı AT, Bilgen İ, Romero R, Tarca AL. Species translatable blood gene signature as a marker of exposure to smoking: computational approaches of the top ranked teams in the sbv IMPROVER Systems Toxicology challenge. ACTA ACUST UNITED AC 2017; 5:25-30. [PMID: 29556587 DOI: 10.1016/j.comtox.2017.04.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Crowdsourcing has been used to address computational challenges in systems biology and assess translation of findings across species. Sub-challenge 2 of the sbv IMPROVER Systems Toxicology Challenge was designed to determine whether a common set of genes can be used to identify exposure to cigarette smoke in both human and mouse. Participating teams used a training set of human and mouse blood gene expression data to derive parsimonious models (up to 40 genes) that classify subjects into exposure groups: smokers, former smokers, and never-smokers. Teams were ranked based on two classification performance metrics evaluated on a blinded test dataset. Prediction of current exposure to cigarette smoke in human and mouse by a common prediction model was achieved by the top ranked team (Team 219) with 89% balanced accuracy (BAC), while past exposure was predicted with only 57% BAC. The prediction model of the top ranked team was a random forest classifier trained on sets of genes that appeared best for each species separately with no overlap between species. By contrast, Team 264, ranked second (tied with Team 250), selected genes that were simultaneously predictive in both species and achieved 80% and 59% BAC when predicting current and past exposure, respectively. These performance values were lower than the 96.5% and 61% BAC estimates for current and past exposure, respectively, obtained by Team 264 (top ranked in sub-challenge 1) when using only human data. Unlike past exposure, current exposure to cigarette smoke can be accurately assessed in both human and mouse with a common prediction model based on blood mRNAs. However, requiring a common gene signature to be predictive in both species resulted in a substantial decrease in balanced accuracy for prediction of current exposure to cigarette smoke (from 96.5% to 80%), suggesting species-specific responses exist.
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Affiliation(s)
| | - Rahul Kumar
- Institute of Microbial Technology, Chandigarh, India
| | | | | | | | - Roberto Romero
- Perinatology Research Branch, NICHD/NIH/DHHS, Bethesda, MD, and Detroit, MI, 48201, USA.,Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI, 48109, USA.,Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, 48825, USA.,Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, 48201, USA
| | - Adi L Tarca
- Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI, USA.,Department of Computer Science, Wayne State University College of Engineering, Detroit, MI, 48202, USA
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16
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Poussin C, Belcastro V, Martin F, Boué S, Peitsch MC, Hoeng J. Crowd-Sourced Verification of Computational Methods and Data in Systems Toxicology: A Case Study with a Heat-Not-Burn Candidate Modified Risk Tobacco Product. Chem Res Toxicol 2017; 30:934-945. [PMID: 28085253 DOI: 10.1021/acs.chemrestox.6b00345] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Systems toxicology intends to quantify the effect of toxic molecules in biological systems and unravel their mechanisms of toxicity. The development of advanced computational methods is required for analyzing and integrating high throughput data generated for this purpose as well as for extrapolating predictive toxicological outcomes and risk estimates. To ensure the performance and reliability of the methods and verify conclusions from systems toxicology data analysis, it is important to conduct unbiased evaluations by independent third parties. As a case study, we report here the results of an independent verification of methods and data in systems toxicology by crowdsourcing. The sbv IMPROVER systems toxicology computational challenge aimed to evaluate computational methods for the development of blood-based gene expression signature classification models with the ability to predict smoking exposure status. Participants created/trained models on blood gene expression data sets including smokers/mice exposed to 3R4F (a reference cigarette) or noncurrent smokers/Sham (mice exposed to air). Participants applied their models on unseen data to predict whether subjects classify closer to smoke-exposed or nonsmoke exposed groups. The data sets also included data from subjects that had been exposed to potential modified risk tobacco products (MRTPs) or that had switched to a MRTP after exposure to conventional cigarette smoke. The scoring of anonymized participants' predictions was done using predefined metrics. The top 3 performers' methods predicted class labels with area under the precision recall scores above 0.9. Furthermore, although various computational approaches were used, the crowd's results confirmed our own data analysis outcomes with regards to the classification of MRTP-related samples. Mice exposed directly to a MRTP were classified closer to the Sham group. After switching to a MRTP, the confidence that subjects belonged to the smoke-exposed group decreased significantly. Smoking exposure gene signatures that contributed to the group separation included a core set of genes highly consistent across teams such as AHRR, LRRN3, SASH1, and P2RY6. In conclusion, crowdsourcing constitutes a pertinent approach, in complement to the classical peer review process, to independently and unbiasedly verify computational methods and data for risk assessment using systems toxicology.
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Affiliation(s)
- Carine Poussin
- PMI R&D, Philip Morris Products S.A. , Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland (Part of Philip Morris International group of companies)
| | - Vincenzo Belcastro
- PMI R&D, Philip Morris Products S.A. , Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland (Part of Philip Morris International group of companies)
| | - Florian Martin
- PMI R&D, Philip Morris Products S.A. , Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland (Part of Philip Morris International group of companies)
| | - Stéphanie Boué
- PMI R&D, Philip Morris Products S.A. , Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland (Part of Philip Morris International group of companies)
| | - Manuel C Peitsch
- PMI R&D, Philip Morris Products S.A. , Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland (Part of Philip Morris International group of companies)
| | - Julia Hoeng
- PMI R&D, Philip Morris Products S.A. , Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland (Part of Philip Morris International group of companies)
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17
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Wittwehr C, Aladjov H, Ankley G, Byrne HJ, de Knecht J, Heinzle E, Klambauer G, Landesmann B, Luijten M, MacKay C, Maxwell G, Meek MEB, Paini A, Perkins E, Sobanski T, Villeneuve D, Waters KM, Whelan M. How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology. Toxicol Sci 2017; 155:326-336. [PMID: 27994170 PMCID: PMC5340205 DOI: 10.1093/toxsci/kfw207] [Citation(s) in RCA: 112] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework provides a systematic approach for organizing knowledge that may support such inference. Likewise, computational models of biological systems at various scales provide another means and platform to integrate current biological understanding to facilitate inference and extrapolation. We argue that the systematic organization of knowledge into AOP frameworks can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment. This concept was explored as part of a workshop on AOP-Informed Predictive Modeling Approaches for Regulatory Toxicology held September 24-25, 2015. Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development is described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment.
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Affiliation(s)
| | | | - Gerald Ankley
- US Environmental Protection Agency, Duluth, Minnesota 55804
| | | | - Joop de Knecht
- National Institute for Public Health and the Environment (RIVM), Bilthoven, MA 3721, The Netherlands
| | - Elmar Heinzle
- Universität des Saarlandes, 66123 Saarbrücken, Germany
| | | | | | - Mirjam Luijten
- National Institute for Public Health and the Environment (RIVM), Bilthoven, MA 3721, The Netherlands
| | - Cameron MacKay
- Unilever Safety and Environmenta Assurance Centre, Sharnbrook, MK44 1LQ, UK
| | - Gavin Maxwell
- Unilever Safety and Environmenta Assurance Centre, Sharnbrook, MK44 1LQ, UK
| | | | - Alicia Paini
- European Commission, Joint Research Centre, Ispra 21027, Italy
| | - Edward Perkins
- US Army Engineer Research and Development Center, Vicksburg, Mississippi 39180
| | | | - Dan Villeneuve
- US Environmental Protection Agency, Duluth, Minnesota 55804
| | - Katrina M Waters
- Pacific Northwest National Laboratory, Richland, Washington 99352
| | - Maurice Whelan
- European Commission, Joint Research Centre, Ispra 21027, Italy
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18
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Boué S, Exner T, Ghosh S, Belcastro V, Dokler J, Page D, Boda A, Bonjour F, Hardy B, Vanscheeuwijck P, Hoeng J, Peitsch M. Supporting evidence-based analysis for modified risk tobacco products through a toxicology data-sharing infrastructure. F1000Res 2017; 6:12. [PMID: 29123642 PMCID: PMC5657032 DOI: 10.12688/f1000research.10493.2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/25/2017] [Indexed: 01/24/2023] Open
Abstract
The US FDA defines modified risk tobacco products (MRTPs) as products that aim to reduce harm or the risk of tobacco-related disease associated with commercially marketed tobacco products. Establishing a product’s potential as an MRTP requires scientific substantiation including toxicity studies and measures of disease risk relative to those of cigarette smoking. Best practices encourage verification of the data from such studies through sharing and open standards. Building on the experience gained from the OpenTox project, a proof-of-concept database and website (
INTERVALS) has been developed to share results from both
in vivo inhalation studies and
in vitro studies conducted by Philip Morris International R&D to assess candidate MRTPs. As datasets are often generated by diverse methods and standards, they need to be traceable, curated, and the methods used well described so that knowledge can be gained using data science principles and tools. The data-management framework described here accounts for the latest standards of data sharing and research reproducibility. Curated data and methods descriptions have been prepared in ISA-Tab format and stored in a database accessible via a search portal on the INTERVALS website. The portal allows users to browse the data by study or mechanism (e.g., inflammation, oxidative stress) and obtain information relevant to study design, methods, and the most important results. Given the successful development of the initial infrastructure, the goal is to grow this initiative and establish a public repository for 21
st-century preclinical systems toxicology MRTP assessment data and results that supports open data principles.
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Affiliation(s)
- Stéphanie Boué
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
| | | | | | | | - Joh Dokler
- Douglas Connect GmbH, Zeiningen, Switzerland
| | - David Page
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
| | - Akash Boda
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
| | - Filipe Bonjour
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
| | - Barry Hardy
- Douglas Connect GmbH, Zeiningen, Switzerland
| | | | - Julia Hoeng
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
| | - Manuel Peitsch
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
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19
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Boué S, Exner T, Ghosh S, Belcastro V, Dokler J, Page D, Boda A, Bonjour F, Hardy B, Vanscheeuwijck P, Hoeng J, Peitsch M. Supporting evidence-based analysis for modified risk tobacco products through a toxicology data-sharing infrastructure. F1000Res 2017; 6:12. [PMID: 29123642 DOI: 10.12688/f1000research.10493.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/03/2017] [Indexed: 12/11/2022] Open
Abstract
The US FDA defines modified risk tobacco products (MRTPs) as products that aim to reduce harm or the risk of tobacco-related disease associated with commercially marketed tobacco products. Establishing a product's potential as an MRTP requires scientific substantiation including toxicity studies and measures of disease risk relative to those of cigarette smoking. Best practices encourage verification of the data from such studies through sharing and open standards. Building on the experience gained from the OpenTox project, a proof-of-concept database and website ( INTERVALS) has been developed to share results from both in vivo inhalation studies and in vitro studies conducted by Philip Morris International R&D to assess candidate MRTPs. As datasets are often generated by diverse methods and standards, they need to be traceable, curated, and the methods used well described so that knowledge can be gained using data science principles and tools. The data-management framework described here accounts for the latest standards of data sharing and research reproducibility. Curated data and methods descriptions have been prepared in ISA-Tab format and stored in a database accessible via a search portal on the INTERVALS website. The portal allows users to browse the data by study or mechanism (e.g., inflammation, oxidative stress) and obtain information relevant to study design, methods, and the most important results. Given the successful development of the initial infrastructure, the goal is to grow this initiative and establish a public repository for 21 st-century preclinical systems toxicology MRTP assessment data and results that supports open data principles.
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Affiliation(s)
- Stéphanie Boué
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
| | | | | | | | - Joh Dokler
- Douglas Connect GmbH, Zeiningen, Switzerland
| | - David Page
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
| | - Akash Boda
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
| | - Filipe Bonjour
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
| | - Barry Hardy
- Douglas Connect GmbH, Zeiningen, Switzerland
| | | | - Julia Hoeng
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
| | - Manuel Peitsch
- PMI R&D, Philip Morris Products S.A., Neuchâtel, Switzerland
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20
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Abstract
The increasing amount and variety of data in biosciences call for innovative methods of visualization, scientific verification, and pathway analysis. Novel approaches to biological networks and research quality control are important because of their role in development of new products, improvement, and acceleration of existing health policies and research for novel ways of solving scientific challenges. One such approach is sbv IMPROVER. It is a platform that uses crowdsourcing and verification to create biological networks with easy public access. It contains 120 networks built in Biological Expression Language (BEL) to interpret data from PubMed articles with high-quality verification available for free on the CBN database. Computable, human-readable biological networks with a structured syntax are a powerful way of representing biological information generated from high-density data. This article presents sbv IMPROVER, a crowd-verification approach for the visualization and expansion of biological networks.
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Affiliation(s)
- Svetlana Guryanova
- M.M. Shemyakin and Yu.A. Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Miklukho-Maklaya 16\10, 117437, Moscow, Russia.
| | - Anna Guryanova
- Global Health Governance Journal - Seton Hall University, South Orange, 07079, NJ, USA
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21
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Lv Q, Lan Y, Shi Y, Wang H, Pan X, Li P, Shi T. AtPID: a genome-scale resource for genotype-phenotype associations in Arabidopsis. Nucleic Acids Res 2016; 45:D1060-D1063. [PMID: 27899679 PMCID: PMC5210528 DOI: 10.1093/nar/gkw1029] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2016] [Revised: 10/16/2016] [Accepted: 11/08/2016] [Indexed: 01/01/2023] Open
Abstract
AtPID (Arabidopsis thalianaProtein Interactome Database, available at http://www.megabionet.org/atpid) is an integrated database resource for protein interaction network and functional annotation. In the past few years, we collected 5564 mutants with significant morphological alterations and manually curated them to 167 plant ontology (PO) morphology categories. These single/multiple-gene mutants were indexed and linked to 3919 genes. After integrated these genotype–phenotype associations with the comprehensive protein interaction network in AtPID, we developed a Naïve Bayes method and predicted 4457 novel high confidence gene-PO pairs with 1369 genes as the complement. Along with the accumulated novel data for protein interaction and functional annotation, and the updated visualization toolkits, we present a genome-scale resource for genotype–phenotype associations for Arabidopsis in AtPID 5.0. In our updated website, all the new genotype–phenotype associations from mutants, protein network, and the protein annotation information can be vividly displayed in a comprehensive network view, which will greatly enhance plant protein function and genotype–phenotype association studies in a systematical way.
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Affiliation(s)
- Qi Lv
- Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai 200241, China.,School of Finance and Statistics, East China Normal University, Shanghai 200241, China
| | - Yiheng Lan
- Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Yan Shi
- Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Huan Wang
- Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Xia Pan
- Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Peng Li
- Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Tieliu Shi
- Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai 200241, China
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22
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Abstract
Developing improved approaches for diagnosis, treatment, and prevention of diseases is a major goal of biomedical research. Therefore, the discovery of biomarker signatures from high-throughput "omics" data is an active research topic in the field of bioinformatics and systems medicine. A major issue is the low reproducibility and the limited biological interpretability of candidate biomarker signatures identified from high-throughput data. This impedes the use of discovered biomarker signatures into clinical applications. Currently, much focus is placed on developing strategies to improve reproducibility and interpretability. Researchers have fruitfully started to incorporate prior knowledge derived from pathways and molecular networks into the process of biomarker identification. In this chapter, after giving a general introduction to the problem of disease classification and biomarker discovery, we will review two types of network-assisted approaches: (1) approaches inferring activity scores for specific pathways which are subsequently used for classification and (2) approaches identifying subnetworks or modules of molecular networks by differential network analysis which can serve as biomarker signatures.
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23
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Martin F, Talikka M, Hoeng J, Peitsch MC. Identification of gene expression signature for cigarette smoke exposure response--from man to mouse. Hum Exp Toxicol 2016; 34:1200-11. [PMID: 26614807 DOI: 10.1177/0960327115600364] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Gene expression profiling data can be used in toxicology to assess both the level and impact of toxicant exposure, aligned with a vision of 21st century toxicology. Here, we present a whole blood-derived gene signature that can distinguish current smokers from either nonsmokers or former smokers with high specificity and sensitivity. Such a signature that can be measured in a surrogate tissue (whole blood) may help in monitoring smoking exposure as well as discontinuation of exposure when the primarily impacted tissue (e.g., lung) is not readily accessible. The signature consisted of LRRN3, SASH1, PALLD, RGL1, TNFRSF17, CDKN1C, IGJ, RRM2, ID3, SERPING1, and FUCA1. Several members of this signature have been previously described in the context of smoking. The signature translated well across species and could distinguish mice that were exposed to cigarette smoke from ones exposed to air only or had been withdrawn from cigarette smoke exposure. Finally, the small signature of only 11 genes could be converted into a polymerase chain reaction-based assay that could serve as a marker to monitor compliance with a smoking abstinence protocol.
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Affiliation(s)
- F Martin
- Philip Morris International Research and Development, Neuchatel, Switzerland
| | - M Talikka
- Philip Morris International Research and Development, Neuchatel, Switzerland
| | - J Hoeng
- Philip Morris International Research and Development, Neuchatel, Switzerland
| | - M C Peitsch
- Philip Morris International Research and Development, Neuchatel, Switzerland
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24
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Behrsing H, Raabe H, Manuppello J, Bombick B, Curren R, Sullivan K, Sethi S, Phipps R, Tesfaigzi Y, Yan S, D'Ruiz C, Tarran R, Constant S, Phillips G, Gaça M, Hayden P, Cao X, Mathis C, Hoeng J, Braun A, Hill E. Assessment of in vitro COPD models for tobacco regulatory science: Workshop proceedings, conclusions and paths forward for in vitro model use. Altern Lab Anim 2016; 44:129-66. [PMID: 27256455 DOI: 10.1177/026119291604400206] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The Family Smoking Prevention and Tobacco Control Act of 2009 established the Food and Drug Administration Center for Tobacco Products (FDA-CTP), and gave it regulatory authority over the marketing, manufacture and distribution of tobacco products, including those termed 'modified risk'. On 8-10 December 2014, IIVS organised a workshop conference, entitled Assessment of In Vitro COPD Models for Tobacco Regulatory Science, to bring together stakeholders representing regulatory agencies, academia, industry and animal protection, to address the research priorities articulated by the FDA-CTP. Specific topics were covered to assess the status of current in vitro technologies as they are applied to understanding the adverse pulmonary events resulting from tobacco product exposure, and in particular, the progression of chronic obstructive pulmonary disease (COPD). The four topics covered were: a) Inflammation and Oxidative Stress; b) Ciliary Dysfunction and Ion Transport; c) Goblet Cell Hyperplasia and Mucus Production; and d) Parenchymal/Bronchial Tissue Destruction and Remodelling. The 2.5 day workshop included 18 expert speakers, plus poster sessions, networking and breakout sessions, which identified key findings and provided recommendations to advance the in vitro technologies and assays used to evaluate tobacco-induced disease etiologies. The workshop summary was reported at the 2015 Society of Toxicology Annual Meeting, and the recommendations led to an IIVS-organised technical workshop in June 2015, entitled Goblet Cell Hyperplasia, Mucus Production, and Ciliary Beating Assays, to assess these assays and to conduct a proof-of-principle multi-laboratory exercise to determine their suitability for standardisation. Here, we report on the proceedings, recommendations and outcomes of the December 2014 workshop, including paths forward to continue the development of non-animal methods to evaluate tissue responses that model the disease processes that may lead to COPD, a major cause of mortality worldwide.
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Affiliation(s)
- Holger Behrsing
- Institute for In Vitro Sciences, Inc., Gaithersburg, MD, USA
| | - Hans Raabe
- Institute for In Vitro Sciences, Inc., Gaithersburg, MD, USA
| | | | - Betsy Bombick
- R.J. Reynolds Tobacco Company, Winston-Salem, NC, USA
| | - Rodger Curren
- Institute for In Vitro Sciences, Inc., Gaithersburg, MD, USA
| | - Kristie Sullivan
- Physicians Committee for Responsible Medicine, Washington, DC, USA
| | - Sanjay Sethi
- University at Buffalo, State University of New York, Buffalo, NY, USA
| | | | | | - Sherwin Yan
- Lorillard Tobacco Company, Greensboro, NC, USA
| | - Carl D'Ruiz
- Lorillard Tobacco Company, Greensboro, NC, USA
| | | | | | - Gary Phillips
- British American Tobacco (Investments) Ltd, Southampton, UK
| | - Marianna Gaça
- British American Tobacco (Investments) Ltd, Southampton, UK
| | | | - Xuefei Cao
- FDA-National Center for Toxicological Research, Jefferson, AR, USA
| | | | - Julia Hoeng
- Philip Morris Product SA, Neuchâtel, Switzerland
| | - Armin Braun
- Fraunhofer Institute for Toxicology and Experimental Medicine, Hannover, Germany
| | - Erin Hill
- Institute for In Vitro Sciences, Inc., Gaithersburg, MD, USA
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25
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Rinaldi F, Ellendorff TR, Madan S, Clematide S, van der Lek A, Mevissen T, Fluck J. BioCreative V track 4: a shared task for the extraction of causal network information using the Biological Expression Language. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2016; 2016:baw067. [PMID: 27402677 PMCID: PMC4940434 DOI: 10.1093/database/baw067] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2015] [Accepted: 04/11/2016] [Indexed: 12/27/2022]
Abstract
Automatic extraction of biological network information is one of the most desired and most complex tasks in biological and medical text mining. Track 4 at BioCreative V attempts to approach this complexity using fragments of large-scale manually curated biological networks, represented in Biological Expression Language (BEL), as training and test data. BEL is an advanced knowledge representation format which has been designed to be both human readable and machine processable. The specific goal of track 4 was to evaluate text mining systems capable of automatically constructing BEL statements from given evidence text, and of retrieving evidence text for given BEL statements. Given the complexity of the task, we designed an evaluation methodology which gives credit to partially correct statements. We identified various levels of information expressed by BEL statements, such as entities, functions, relations, and introduced an evaluation framework which rewards systems capable of delivering useful BEL fragments at each of these levels. The aim of this evaluation method is to help identify the characteristics of the systems which, if combined, would be most useful for achieving the overall goal of automatically constructing causal biological networks from text.
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Affiliation(s)
- Fabio Rinaldi
- Institute of Computational Linguistics, University of Zurich, Zurich, Switzerland
| | | | - Sumit Madan
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, Sankt Augustin, Germany
| | - Simon Clematide
- Institute of Computational Linguistics, University of Zurich, Zurich, Switzerland
| | - Adrian van der Lek
- Institute of Computational Linguistics, University of Zurich, Zurich, Switzerland
| | - Theo Mevissen
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, Sankt Augustin, Germany
| | - Juliane Fluck
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, Sankt Augustin, Germany
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26
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Abstract
Systems medicine promotes a range of approaches and strategies to study human health and disease at a systems level with the aim of improving the overall well-being of (healthy) individuals, and preventing, diagnosing, or curing disease. In this chapter we discuss how bioinformatics critically contributes to systems medicine. First, we explain the role of bioinformatics in the management and analysis of data. In particular we show the importance of publicly available biological and clinical repositories to support systems medicine studies. Second, we discuss how the integration and analysis of multiple types of omics data through integrative bioinformatics may facilitate the determination of more predictive and robust disease signatures, lead to a better understanding of (patho)physiological molecular mechanisms, and facilitate personalized medicine. Third, we focus on network analysis and discuss how gene networks can be constructed from omics data and how these networks can be decomposed into smaller modules. We discuss how the resulting modules can be used to generate experimentally testable hypotheses, provide insight into disease mechanisms, and lead to predictive models. Throughout, we provide several examples demonstrating how bioinformatics contributes to systems medicine and discuss future challenges in bioinformatics that need to be addressed to enable the advancement of systems medicine.
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Affiliation(s)
- Ulf Schmitz
- Dept of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany
| | - Olaf Wolkenhauer
- Dept of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany
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27
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Abstract
Gene coexpression networks inferred by correlation from high-throughput profiling such as microarray data represent simple but effective structures for discovering and interpreting linear gene relationships. In recent years, several approaches have been proposed to tackle the problem of deciding when the resulting correlation values are statistically significant. This is most crucial when the number of samples is small, yielding a non-negligible chance that even high correlation values are due to random effects. Here we introduce a novel hard thresholding solution based on the assumption that a coexpression network inferred by randomly generated data is expected to be empty. The threshold is theoretically derived by means of an analytic approach and, as a deterministic independent null model, it depends only on the dimensions of the starting data matrix, with assumptions on the skewness of the data distribution compatible with the structure of gene expression levels data. We show, on synthetic and array datasets, that the proposed threshold is effective in eliminating all false positive links, with an offsetting cost in terms of false negative detected edges.
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28
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Boué S, Talikka M, Westra JW, Hayes W, Di Fabio A, Park J, Schlage WK, Sewer A, Fields B, Ansari S, Martin F, Veljkovic E, Kenney R, Peitsch MC, Hoeng J. Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2015; 2015:bav030. [PMID: 25887162 PMCID: PMC4401337 DOI: 10.1093/database/bav030] [Citation(s) in RCA: 82] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Accepted: 03/09/2015] [Indexed: 01/28/2023]
Abstract
With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed. Moreover, networks may be downloaded for further visualization and evaluation. Database URL:http://causalbionet.com
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Affiliation(s)
- Stéphanie Boué
- Philip Morris International R&D, Philip Morris Products S.A. Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland, Selventa, One Alewife Center, Cambridge, MA 02140, USA and Applied Dynamic Solutions, LLC, 220 Davidson Avenue, Suite 100, Somerset, NJ 08873, USA
| | - Marja Talikka
- Philip Morris International R&D, Philip Morris Products S.A. Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland, Selventa, One Alewife Center, Cambridge, MA 02140, USA and Applied Dynamic Solutions, LLC, 220 Davidson Avenue, Suite 100, Somerset, NJ 08873, USA
| | - Jurjen Willem Westra
- Philip Morris International R&D, Philip Morris Products S.A. Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland, Selventa, One Alewife Center, Cambridge, MA 02140, USA and Applied Dynamic Solutions, LLC, 220 Davidson Avenue, Suite 100, Somerset, NJ 08873, USA
| | - William Hayes
- Philip Morris International R&D, Philip Morris Products S.A. Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland, Selventa, One Alewife Center, Cambridge, MA 02140, USA and Applied Dynamic Solutions, LLC, 220 Davidson Avenue, Suite 100, Somerset, NJ 08873, USA
| | - Anselmo Di Fabio
- Philip Morris International R&D, Philip Morris Products S.A. Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland, Selventa, One Alewife Center, Cambridge, MA 02140, USA and Applied Dynamic Solutions, LLC, 220 Davidson Avenue, Suite 100, Somerset, NJ 08873, USA
| | - Jennifer Park
- Philip Morris International R&D, Philip Morris Products S.A. Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland, Selventa, One Alewife Center, Cambridge, MA 02140, USA and Applied Dynamic Solutions, LLC, 220 Davidson Avenue, Suite 100, Somerset, NJ 08873, USA
| | - Walter K Schlage
- Philip Morris International R&D, Philip Morris Products S.A. Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland, Selventa, One Alewife Center, Cambridge, MA 02140, USA and Applied Dynamic Solutions, LLC, 220 Davidson Avenue, Suite 100, Somerset, NJ 08873, USA
| | - Alain Sewer
- Philip Morris International R&D, Philip Morris Products S.A. Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland, Selventa, One Alewife Center, Cambridge, MA 02140, USA and Applied Dynamic Solutions, LLC, 220 Davidson Avenue, Suite 100, Somerset, NJ 08873, USA
| | - Brett Fields
- Philip Morris International R&D, Philip Morris Products S.A. Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland, Selventa, One Alewife Center, Cambridge, MA 02140, USA and Applied Dynamic Solutions, LLC, 220 Davidson Avenue, Suite 100, Somerset, NJ 08873, USA
| | - Sam Ansari
- Philip Morris International R&D, Philip Morris Products S.A. Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland, Selventa, One Alewife Center, Cambridge, MA 02140, USA and Applied Dynamic Solutions, LLC, 220 Davidson Avenue, Suite 100, Somerset, NJ 08873, USA
| | - Florian Martin
- Philip Morris International R&D, Philip Morris Products S.A. Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland, Selventa, One Alewife Center, Cambridge, MA 02140, USA and Applied Dynamic Solutions, LLC, 220 Davidson Avenue, Suite 100, Somerset, NJ 08873, USA
| | - Emilija Veljkovic
- Philip Morris International R&D, Philip Morris Products S.A. Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland, Selventa, One Alewife Center, Cambridge, MA 02140, USA and Applied Dynamic Solutions, LLC, 220 Davidson Avenue, Suite 100, Somerset, NJ 08873, USA
| | - Renee Kenney
- Philip Morris International R&D, Philip Morris Products S.A. Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland, Selventa, One Alewife Center, Cambridge, MA 02140, USA and Applied Dynamic Solutions, LLC, 220 Davidson Avenue, Suite 100, Somerset, NJ 08873, USA
| | - Manuel C Peitsch
- Philip Morris International R&D, Philip Morris Products S.A. Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland, Selventa, One Alewife Center, Cambridge, MA 02140, USA and Applied Dynamic Solutions, LLC, 220 Davidson Avenue, Suite 100, Somerset, NJ 08873, USA
| | - Julia Hoeng
- Philip Morris International R&D, Philip Morris Products S.A. Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland, Selventa, One Alewife Center, Cambridge, MA 02140, USA and Applied Dynamic Solutions, LLC, 220 Davidson Avenue, Suite 100, Somerset, NJ 08873, USA
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29
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Boue S, Fields B, Hoeng J, Park J, Peitsch MC, Schlage WK, Talikka M, Binenbaum I, Bondarenko V, Bulgakov OV, Cherkasova V, Diaz-Diaz N, Fedorova L, Guryanova S, Guzova J, Igorevna Koroleva G, Kozhemyakina E, Kumar R, Lavid N, Lu Q, Menon S, Ouliel Y, Peterson SC, Prokhorov A, Sanders E, Schrier S, Schwaitzer Neta G, Shvydchenko I, Tallam A, Villa-Fombuena G, Wu J, Yudkevich I, Zelikman M. Enhancement of COPD biological networks using a web-based collaboration interface. F1000Res 2015; 4:32. [PMID: 25767696 PMCID: PMC4350443 DOI: 10.12688/f1000research.5984.2] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/14/2015] [Indexed: 01/06/2023] Open
Abstract
The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website ( https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks.
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Affiliation(s)
- The sbv IMPROVER project team (in alphabetical order)
- Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
- Selventa, One Alewife Center, Cambridge, MA, 02140, USA
- Systems Bioengineering Group - National Technical University of Athens, Ethniko Metsovio Politechnio, , 28is Oktovriou 42, Athina, 106 82, Greece
- Touro University Nevada, 874 American Pacific Drive, Henderson, NV, 89052, USA
- University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA
- Intelligent Data Analysis Group (DATAi), School of Engineering, Pablo de Olavide University, Ctra. de Utrera, km. 1 41013, Sevilla, Spain
- University of Toledo, 2801 W Bancroft St, Toledo, OH, 43606, USA
- Shemyakin & Ovchinnikov Institute of Bioorganic Chemistry, 16/10, Miklukho-Maklay str., Moscow, 117997, Russian Federation
- Private, Washington DC, USA
- USAMRIID, Attn: MCMR-UIZ-R, 1425 Porter Street, Frederick, MD, 21702-5011, USA
- Private, Boston, MA, USA
- Institute of Microbial Technology, Chandigarh, 160036, India
- Technion - Israel Institute of Technology, Technion City, Haifa, 3200003, Israel
- Louisville University, 301 E. Muhammad Ali Blvd, Louisville, KY, 40202, USA
- AnalyzeDat Consulting Services, Ernakulam, India
- Northeastern University, 360 Huntington Ave, Boston, MA, 02115, USA
- Edward Sanders Scientific Consulting, Rue du Clos 33, 2034 Peseux, Switzerland
- Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA
- Kuban State University of Physical Education, Sport and Tourism, 161, Budennogo Str., Krasnodar City, 350015, Russian Federation
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, avenue des Hauts-Fourneaux, 4362 Esch sur Alzette, Luxembourg
- Pablo de Olavide University, Ctra. de Utrera, km. 1 41013, Sevilla, Spain
- Cal Biopharma, 710 Somerset Ln, Foster Cit, CA, 94404-3728, USA
- University of Manchester, Oxford Rd, Manchester, M13 9PL, UK
- University of Washington, 1959 NE Pacific Street, HSB T-466, Seattle, WA, USA
| | - Stephanie Boue
- Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
| | - Brett Fields
- Selventa, One Alewife Center, Cambridge, MA, 02140, USA
| | - Julia Hoeng
- Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
| | - Jennifer Park
- Selventa, One Alewife Center, Cambridge, MA, 02140, USA
| | - Manuel C. Peitsch
- Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
| | - Walter K. Schlage
- Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
| | - Marja Talikka
- Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
| | - The Challenge Best Performers (in alphabetical order)
- Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
- Selventa, One Alewife Center, Cambridge, MA, 02140, USA
- Systems Bioengineering Group - National Technical University of Athens, Ethniko Metsovio Politechnio, , 28is Oktovriou 42, Athina, 106 82, Greece
- Touro University Nevada, 874 American Pacific Drive, Henderson, NV, 89052, USA
- University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA
- Intelligent Data Analysis Group (DATAi), School of Engineering, Pablo de Olavide University, Ctra. de Utrera, km. 1 41013, Sevilla, Spain
- University of Toledo, 2801 W Bancroft St, Toledo, OH, 43606, USA
- Shemyakin & Ovchinnikov Institute of Bioorganic Chemistry, 16/10, Miklukho-Maklay str., Moscow, 117997, Russian Federation
- Private, Washington DC, USA
- USAMRIID, Attn: MCMR-UIZ-R, 1425 Porter Street, Frederick, MD, 21702-5011, USA
- Private, Boston, MA, USA
- Institute of Microbial Technology, Chandigarh, 160036, India
- Technion - Israel Institute of Technology, Technion City, Haifa, 3200003, Israel
- Louisville University, 301 E. Muhammad Ali Blvd, Louisville, KY, 40202, USA
- AnalyzeDat Consulting Services, Ernakulam, India
- Northeastern University, 360 Huntington Ave, Boston, MA, 02115, USA
- Edward Sanders Scientific Consulting, Rue du Clos 33, 2034 Peseux, Switzerland
- Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA
- Kuban State University of Physical Education, Sport and Tourism, 161, Budennogo Str., Krasnodar City, 350015, Russian Federation
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, avenue des Hauts-Fourneaux, 4362 Esch sur Alzette, Luxembourg
- Pablo de Olavide University, Ctra. de Utrera, km. 1 41013, Sevilla, Spain
- Cal Biopharma, 710 Somerset Ln, Foster Cit, CA, 94404-3728, USA
- University of Manchester, Oxford Rd, Manchester, M13 9PL, UK
- University of Washington, 1959 NE Pacific Street, HSB T-466, Seattle, WA, USA
| | - Ilona Binenbaum
- Systems Bioengineering Group - National Technical University of Athens, Ethniko Metsovio Politechnio, , 28is Oktovriou 42, Athina, 106 82, Greece
| | - Vladimir Bondarenko
- Touro University Nevada, 874 American Pacific Drive, Henderson, NV, 89052, USA
| | - Oleg V. Bulgakov
- University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA
| | | | - Norberto Diaz-Diaz
- Intelligent Data Analysis Group (DATAi), School of Engineering, Pablo de Olavide University, Ctra. de Utrera, km. 1 41013, Sevilla, Spain
| | - Larisa Fedorova
- University of Toledo, 2801 W Bancroft St, Toledo, OH, 43606, USA
| | - Svetlana Guryanova
- Shemyakin & Ovchinnikov Institute of Bioorganic Chemistry, 16/10, Miklukho-Maklay str., Moscow, 117997, Russian Federation
| | | | | | | | - Rahul Kumar
- Institute of Microbial Technology, Chandigarh, 160036, India
| | - Noa Lavid
- Technion - Israel Institute of Technology, Technion City, Haifa, 3200003, Israel
| | - Qingxian Lu
- Louisville University, 301 E. Muhammad Ali Blvd, Louisville, KY, 40202, USA
| | - Swapna Menon
- AnalyzeDat Consulting Services, Ernakulam, India
| | - Yael Ouliel
- Technion - Israel Institute of Technology, Technion City, Haifa, 3200003, Israel
| | | | - Alexander Prokhorov
- Shemyakin & Ovchinnikov Institute of Bioorganic Chemistry, 16/10, Miklukho-Maklay str., Moscow, 117997, Russian Federation
| | - Edward Sanders
- Edward Sanders Scientific Consulting, Rue du Clos 33, 2034 Peseux, Switzerland
| | - Sarah Schrier
- Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA
| | | | - Irina Shvydchenko
- Kuban State University of Physical Education, Sport and Tourism, 161, Budennogo Str., Krasnodar City, 350015, Russian Federation
| | - Aravind Tallam
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, avenue des Hauts-Fourneaux, 4362 Esch sur Alzette, Luxembourg
| | | | - John Wu
- Cal Biopharma, 710 Somerset Ln, Foster Cit, CA, 94404-3728, USA
| | - Ilya Yudkevich
- University of Manchester, Oxford Rd, Manchester, M13 9PL, UK
| | - Mariya Zelikman
- University of Washington, 1959 NE Pacific Street, HSB T-466, Seattle, WA, USA
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30
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Boue S, Fields B, Hoeng J, Park J, Peitsch MC, Schlage WK, Talikka M, Binenbaum I, Bondarenko V, Bulgakov OV, Cherkasova V, Diaz-Diaz N, Fedorova L, Guryanova S, Guzova J, Igorevna Koroleva G, Kozhemyakina E, Kumar R, Lavid N, Lu Q, Menon S, Ouliel Y, Peterson SC, Prokhorov A, Sanders E, Schrier S, Schwaitzer Neta G, Shvydchenko I, Tallam A, Villa-Fombuena G, Wu J, Yudkevich I, Zelikman M. Enhancement of COPD biological networks using a web-based collaboration interface. F1000Res 2015; 4:32. [PMID: 25767696 PMCID: PMC4350443 DOI: 10.12688/f1000research.5984.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/12/2015] [Indexed: 11/20/2022] Open
Abstract
The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website ( https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks.
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Affiliation(s)
- The sbv IMPROVER project team (in alphabetical order)
- Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
- Selventa, One Alewife Center, Cambridge, MA, 02140, USA
- Systems Bioengineering Group - National Technical University of Athens, Ethniko Metsovio Politechnio, , 28is Oktovriou 42, Athina, 106 82, Greece
- Touro University Nevada, 874 American Pacific Drive, Henderson, NV, 89052, USA
- University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA
- Intelligent Data Analysis Group (DATAi), School of Engineering, Pablo de Olavide University, Ctra. de Utrera, km. 1 41013, Sevilla, Spain
- University of Toledo, 2801 W Bancroft St, Toledo, OH, 43606, USA
- Shemyakin & Ovchinnikov Institute of Bioorganic Chemistry, 16/10, Miklukho-Maklay str., Moscow, 117997, Russian Federation
- Private, Washington DC, USA
- USAMRIID, Attn: MCMR-UIZ-R, 1425 Porter Street, Frederick, MD, 21702-5011, USA
- Private, Boston, MA, USA
- Institute of Microbial Technology, Chandigarh, 160036, India
- Technion - Israel Institute of Technology, Technion City, Haifa, 3200003, Israel
- Louisville University, 301 E. Muhammad Ali Blvd, Louisville, KY, 40202, USA
- AnalyzeDat Consulting Services, Ernakulam, India
- Northeastern University, 360 Huntington Ave, Boston, MA, 02115, USA
- Edward Sanders Scientific Consulting, Rue du Clos 33, 2034 Peseux, Switzerland
- Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA
- Kuban State University of Physical Education, Sport and Tourism, 161, Budennogo Str., Krasnodar City, 350015, Russian Federation
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, avenue des Hauts-Fourneaux, 4362 Esch sur Alzette, Luxembourg
- Pablo de Olavide University, Ctra. de Utrera, km. 1 41013, Sevilla, Spain
- Cal Biopharma, 710 Somerset Ln, Foster Cit, CA, 94404-3728, USA
- University of Manchester, Oxford Rd, Manchester, M13 9PL, UK
- University of Washington, 1959 NE Pacific Street, HSB T-466, Seattle, WA, USA
| | - Stephanie Boue
- Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
| | - Brett Fields
- Selventa, One Alewife Center, Cambridge, MA, 02140, USA
| | - Julia Hoeng
- Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
| | - Jennifer Park
- Selventa, One Alewife Center, Cambridge, MA, 02140, USA
| | - Manuel C. Peitsch
- Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
| | - Walter K. Schlage
- Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
| | - Marja Talikka
- Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
| | - The Challenge Best Performers (in alphabetical order)
- Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
- Selventa, One Alewife Center, Cambridge, MA, 02140, USA
- Systems Bioengineering Group - National Technical University of Athens, Ethniko Metsovio Politechnio, , 28is Oktovriou 42, Athina, 106 82, Greece
- Touro University Nevada, 874 American Pacific Drive, Henderson, NV, 89052, USA
- University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA
- Intelligent Data Analysis Group (DATAi), School of Engineering, Pablo de Olavide University, Ctra. de Utrera, km. 1 41013, Sevilla, Spain
- University of Toledo, 2801 W Bancroft St, Toledo, OH, 43606, USA
- Shemyakin & Ovchinnikov Institute of Bioorganic Chemistry, 16/10, Miklukho-Maklay str., Moscow, 117997, Russian Federation
- Private, Washington DC, USA
- USAMRIID, Attn: MCMR-UIZ-R, 1425 Porter Street, Frederick, MD, 21702-5011, USA
- Private, Boston, MA, USA
- Institute of Microbial Technology, Chandigarh, 160036, India
- Technion - Israel Institute of Technology, Technion City, Haifa, 3200003, Israel
- Louisville University, 301 E. Muhammad Ali Blvd, Louisville, KY, 40202, USA
- AnalyzeDat Consulting Services, Ernakulam, India
- Northeastern University, 360 Huntington Ave, Boston, MA, 02115, USA
- Edward Sanders Scientific Consulting, Rue du Clos 33, 2034 Peseux, Switzerland
- Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA
- Kuban State University of Physical Education, Sport and Tourism, 161, Budennogo Str., Krasnodar City, 350015, Russian Federation
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, avenue des Hauts-Fourneaux, 4362 Esch sur Alzette, Luxembourg
- Pablo de Olavide University, Ctra. de Utrera, km. 1 41013, Sevilla, Spain
- Cal Biopharma, 710 Somerset Ln, Foster Cit, CA, 94404-3728, USA
- University of Manchester, Oxford Rd, Manchester, M13 9PL, UK
- University of Washington, 1959 NE Pacific Street, HSB T-466, Seattle, WA, USA
| | - Ilona Binenbaum
- Systems Bioengineering Group - National Technical University of Athens, Ethniko Metsovio Politechnio, , 28is Oktovriou 42, Athina, 106 82, Greece
| | - Vladimir Bondarenko
- Touro University Nevada, 874 American Pacific Drive, Henderson, NV, 89052, USA
| | - Oleg V. Bulgakov
- University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA
| | | | - Norberto Diaz-Diaz
- Intelligent Data Analysis Group (DATAi), School of Engineering, Pablo de Olavide University, Ctra. de Utrera, km. 1 41013, Sevilla, Spain
| | - Larisa Fedorova
- University of Toledo, 2801 W Bancroft St, Toledo, OH, 43606, USA
| | - Svetlana Guryanova
- Shemyakin & Ovchinnikov Institute of Bioorganic Chemistry, 16/10, Miklukho-Maklay str., Moscow, 117997, Russian Federation
| | | | | | | | - Rahul Kumar
- Institute of Microbial Technology, Chandigarh, 160036, India
| | - Noa Lavid
- Technion - Israel Institute of Technology, Technion City, Haifa, 3200003, Israel
| | - Qingxian Lu
- Louisville University, 301 E. Muhammad Ali Blvd, Louisville, KY, 40202, USA
| | - Swapna Menon
- AnalyzeDat Consulting Services, Ernakulam, India
| | - Yael Ouliel
- Technion - Israel Institute of Technology, Technion City, Haifa, 3200003, Israel
| | | | - Alexander Prokhorov
- Shemyakin & Ovchinnikov Institute of Bioorganic Chemistry, 16/10, Miklukho-Maklay str., Moscow, 117997, Russian Federation
| | - Edward Sanders
- Edward Sanders Scientific Consulting, Rue du Clos 33, 2034 Peseux, Switzerland
| | - Sarah Schrier
- Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA
| | | | - Irina Shvydchenko
- Kuban State University of Physical Education, Sport and Tourism, 161, Budennogo Str., Krasnodar City, 350015, Russian Federation
| | - Aravind Tallam
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, avenue des Hauts-Fourneaux, 4362 Esch sur Alzette, Luxembourg
| | | | - John Wu
- Cal Biopharma, 710 Somerset Ln, Foster Cit, CA, 94404-3728, USA
| | - Ilya Yudkevich
- University of Manchester, Oxford Rd, Manchester, M13 9PL, UK
| | - Mariya Zelikman
- University of Washington, 1959 NE Pacific Street, HSB T-466, Seattle, WA, USA
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da Silva SRM, Perrone GC, Dinis JM, de Almeida RMC. Reproducibility enhancement and differential expression of non predefined functional gene sets in human genome. BMC Genomics 2014; 15:1181. [PMID: 25539829 PMCID: PMC4326474 DOI: 10.1186/1471-2164-15-1181] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2014] [Accepted: 12/11/2014] [Indexed: 12/27/2022] Open
Abstract
Background Transcriptogram profiling is a method to present and analyze transcription data in a genome-wide scale that reduces noise and facilitates biological interpretation. An ordered gene list is produced, such that the probability that the genes are functionally associated exponentially decays with their distance on the list. This list presents a biological logic, evinced by the selective enrichment of successive intervals with Gene Ontology terms or KEGG pathways. Transcriptograms are expression profiles obtained by taking the average of gene expression over neighboring genes on this list. Transcriptograms enhance reproducibility and precision for expression measurements of functionally correlated gene sets. Results Here we present an ordering list for Homo sapiens and apply the transcriptogram profiling method to different datasets. We show that this method enhances experiment reproducibility and enhances signal. We applied the method to a diabetes study by Hwang and collaborators, which focused on expression differences between cybrids produced by the hybridization of mitochondria of diabetes mellitus donors with osteosarcoma cell lines, depleted of mitochondria. We found that the transcriptogram method revealed significant differential expression in gene sets linked to blood coagulation and wound healing pathways, and also to gene sets that do not represent any metabolic pathway or Gene Ontology term. These gene sets are connected to ECM-receptor interaction and secreted proteins. Conclusion The transcriptogram profiling method provided an automatic way to define sets of genes with correlated expression, reduce noise in genome-wide transcription profiles, and enhance measure reproducibility and sensitivity. These advantages enabled biologic interpretation and pointed to differentially expressed gene sets in diabetes mellitus which were not previously defined. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-1181) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | | | | | - Rita M C de Almeida
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Av, Bento Gonçalves, 9500, 91501-970 Porto Alegre, RS, Brazil.
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Quintero A, Ramírez J, Leal LG, López-Kleine L. Algorithm for the Construction of a Global Enzymatic Network to be Used for Gene Network Reconstruction. Curr Genomics 2014; 15:400-7. [PMID: 25435802 PMCID: PMC4245699 DOI: 10.2174/1389202915666140807004909] [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: 05/19/2014] [Revised: 07/28/2014] [Accepted: 08/05/2014] [Indexed: 11/22/2022] Open
Abstract
Relationships between genes are best represented using networks constructed from information of different types, with metabolic information being the most valuable and widely used for genetic network reconstruction. Other types of information are usually also available, and it would be desirable to systematically include them in algorithms for network reconstruction. Here, we present an algorithm to construct a global metabolic network that uses all available enzymatic and metabolic information about the organism. We construct a global enzymatic network (GEN) with a total of 4226 nodes (EC numbers) and 42723 edges representing all known metabolic reactions. As an example we use microarray data for Arabidopsis thaliana and combine it with the metabolic network constructing a final gene interaction network for this organism with 8212 nodes (genes) and 4606,901 edges. All scripts are available to be used for any organism for which genomic data is available.
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Affiliation(s)
- Andrés Quintero
- Universidad Nacional de Colombia, Department of Biology, Master Student, Universidad Nacional de Colombia-Sede Bogotá
| | - Jorge Ramírez
- Universidad Nacional de Colombia, School of Mathematics, Associate Professor, Universidad Nacional de Colombia-Sede Medellín
| | - Luis Guillermo Leal
- Universidad Nacional de Colombia, Department of Statistics, Master Student, Universidad Nacional de Colombia-Sede Bogotá
| | - Liliana López-Kleine
- Universidad Nacional de Colombia, Department of Statistics, Associate Professor, Universidad Nacional de Colombia-Sede Bogotá, Colombia
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Tarca AL, Than NG, Romero R. Methodological approach from the Best Overall Team in the sbv IMPROVER Diagnostic Signature Challenge. ACTA ACUST UNITED AC 2014. [DOI: 10.4161/sysb.25980] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Zhang A, Wang C, Wang S, Li L, Liu Z, Tian S. Visualization-aided classification ensembles discriminate lung adenocarcinoma and squamous cell carcinoma samples using their gene expression profiles. PLoS One 2014; 9:e110052. [PMID: 25335090 PMCID: PMC4198193 DOI: 10.1371/journal.pone.0110052] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2014] [Accepted: 09/08/2014] [Indexed: 11/19/2022] Open
Abstract
INTRODUCTION The widespread application of microarray experiments to cancer research is astounding including lung cancer, one of the most common fatal human tumors. Among non-small cell lung carcinoma (NSCLC), there are two major histological types of NSCLC, adenocarcinoma (AC) and squamous cell carcinoma (SCC). RESULTS In this paper, we proposed to integrate a visualization method called Radial Coordinate Visualization (Radviz) with a suitable classifier, aiming at discriminating two NSCLC subtypes using patients' gene expression profiles. Our analyses on simulated data and a real microarray dataset show that combining with a classification method, Radviz may play a role in selecting relevant features and ameliorating parsimony, while the final model suffers no or least loss of accuracy. Most importantly, a graphic representation is more easily understandable and implementable for a clinician than statistical methods and/or mathematic equations. CONCLUSION To conclude, using the NSCLC microarray data presented here as a benchmark, the comprehensive understanding of the underlying mechanism associated with NSCLC and of the mechanisms with its subtypes and respective stages will become reality in the near future.
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Affiliation(s)
- Ao Zhang
- Intensive Care Unit (ICU), First Hospital of Jilin University, Changchun, Jilin, China
| | - Chi Wang
- Department of Biostatistics and Markey Cancer Center, University of Kentucky, Lexington, Kentucky, United States of America
| | - Shiji Wang
- Intensive Care Unit (ICU), First Hospital of Jilin University, Changchun, Jilin, China
| | - Liang Li
- LLX Solutions LLC., Waltham, Massachusetts, United States of America
| | - Zhongmin Liu
- Intensive Care Unit (ICU), First Hospital of Jilin University, Changchun, Jilin, China
- * E-mail: (ST); (ZL)
| | - Suyan Tian
- Division of Clinical Epidemiology, First Hospital of Jilin University, Changchun, Jilin, China
- * E-mail: (ST); (ZL)
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Boutros PC, Margolin AA, Stuart JM, Califano A, Stolovitzky G. Toward better benchmarking: challenge-based methods assessment in cancer genomics. Genome Biol 2014; 15:462. [PMID: 25314947 PMCID: PMC4318527 DOI: 10.1186/s13059-014-0462-7] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Rapid technological development has created an urgent need for improved evaluation of algorithms for the analysis of cancer genomics data. We outline how challenge-based assessment may help fill this gap by leveraging crowd-sourcing to distribute effort and reduce bias.
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Titz B, Elamin A, Martin F, Schneider T, Dijon S, Ivanov NV, Hoeng J, Peitsch MC. Proteomics for systems toxicology. Comput Struct Biotechnol J 2014; 11:73-90. [PMID: 25379146 PMCID: PMC4212285 DOI: 10.1016/j.csbj.2014.08.004] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Current toxicology studies frequently lack measurements at molecular resolution to enable a more mechanism-based and predictive toxicological assessment. Recently, a systems toxicology assessment framework has been proposed, which combines conventional toxicological assessment strategies with system-wide measurement methods and computational analysis approaches from the field of systems biology. Proteomic measurements are an integral component of this integrative strategy because protein alterations closely mirror biological effects, such as biological stress responses or global tissue alterations. Here, we provide an overview of the technical foundations and highlight select applications of proteomics for systems toxicology studies. With a focus on mass spectrometry-based proteomics, we summarize the experimental methods for quantitative proteomics and describe the computational approaches used to derive biological/mechanistic insights from these datasets. To illustrate how proteomics has been successfully employed to address mechanistic questions in toxicology, we summarized several case studies. Overall, we provide the technical and conceptual foundation for the integration of proteomic measurements in a more comprehensive systems toxicology assessment framework. We conclude that, owing to the critical importance of protein-level measurements and recent technological advances, proteomics will be an integral part of integrative systems toxicology approaches in the future.
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Poussin C, Mathis C, Alexopoulos LG, Messinis DE, Dulize RHJ, Belcastro V, Melas IN, Sakellaropoulos T, Rhrissorrakrai K, Bilal E, Meyer P, Talikka M, Boué S, Norel R, Rice JJ, Stolovitzky G, Ivanov NV, Peitsch MC, Hoeng J. The species translation challenge-a systems biology perspective on human and rat bronchial epithelial cells. Sci Data 2014; 1:140009. [PMID: 25977767 PMCID: PMC4322580 DOI: 10.1038/sdata.2014.9] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2013] [Accepted: 04/25/2014] [Indexed: 11/19/2022] Open
Abstract
The biological responses to external cues such as drugs, chemicals, viruses and hormones, is an essential question in biomedicine and in the field of toxicology, and cannot be easily studied in humans. Thus, biomedical research has continuously relied on animal models for studying the impact of these compounds and attempted to 'translate' the results to humans. In this context, the SBV IMPROVER (Systems Biology Verification for Industrial Methodology for PROcess VErification in Research) collaborative initiative, which uses crowd-sourcing techniques to address fundamental questions in systems biology, invited scientists to deploy their own computational methodologies to make predictions on species translatability. A multi-layer systems biology dataset was generated that was comprised of phosphoproteomics, transcriptomics and cytokine data derived from normal human (NHBE) and rat (NRBE) bronchial epithelial cells exposed in parallel to more than 50 different stimuli under identical conditions. The present manuscript describes in detail the experimental settings, generation, processing and quality control analysis of the multi-layer omics dataset accessible in public repositories for further intra- and inter-species translation studies.
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Affiliation(s)
- Carine Poussin
- Philip Morris International R&D, Philip Morris Products S. A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
- These authors contributed equally to this work
| | - Carole Mathis
- Philip Morris International R&D, Philip Morris Products S. A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
- These authors contributed equally to this work
| | - Leonidas G Alexopoulos
- ProtATonce Ltd, Scientific Park Lefkippos, Patriarchou Grigoriou & Neapoleos, 15343 Ag. Paraskevi, Attiki, Greece
- National Technical University of Athens, Heroon Polytechniou 9, Zografou 15780, Greece
- These authors contributed equally to this work
| | - Dimitris E Messinis
- ProtATonce Ltd, Scientific Park Lefkippos, Patriarchou Grigoriou & Neapoleos, 15343 Ag. Paraskevi, Attiki, Greece
- National Technical University of Athens, Heroon Polytechniou 9, Zografou 15780, Greece
| | - Rémi H J Dulize
- Philip Morris International R&D, Philip Morris Products S. A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
| | - Vincenzo Belcastro
- Philip Morris International R&D, Philip Morris Products S. A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
| | - Ioannis N Melas
- ProtATonce Ltd, Scientific Park Lefkippos, Patriarchou Grigoriou & Neapoleos, 15343 Ag. Paraskevi, Attiki, Greece
- National Technical University of Athens, Heroon Polytechniou 9, Zografou 15780, Greece
| | | | | | - Erhan Bilal
- IBM Computational Biology Center, Yorktown Heights, NY 10598, USA
| | - Pablo Meyer
- IBM Computational Biology Center, Yorktown Heights, NY 10598, USA
| | - Marja Talikka
- Philip Morris International R&D, Philip Morris Products S. A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
| | - Stéphanie Boué
- Philip Morris International R&D, Philip Morris Products S. A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
| | - Raquel Norel
- IBM Computational Biology Center, Yorktown Heights, NY 10598, USA
| | - John J Rice
- IBM Computational Biology Center, Yorktown Heights, NY 10598, USA
| | | | - Nikolai V Ivanov
- Philip Morris International R&D, Philip Morris Products S. A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
| | - Manuel C Peitsch
- Philip Morris International R&D, Philip Morris Products S. A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
| | - Julia Hoeng
- Philip Morris International R&D, Philip Morris Products S. A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
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Filosi M, Visintainer R, Riccadonna S, Jurman G, Furlanello C. Stability indicators in network reconstruction. PLoS One 2014; 9:e89815. [PMID: 24587057 PMCID: PMC3937450 DOI: 10.1371/journal.pone.0089815] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2013] [Accepted: 01/27/2014] [Indexed: 11/20/2022] Open
Abstract
The number of available algorithms to infer a biological network from a dataset of high-throughput measurements is overwhelming and keeps growing. However, evaluating their performance is unfeasible unless a ‘gold standard’ is available to measure how close the reconstructed network is to the ground truth. One measure of this is the stability of these predictions to data resampling approaches. We introduce NetSI, a family of Network Stability Indicators, to assess quantitatively the stability of a reconstructed network in terms of inference variability due to data subsampling. In order to evaluate network stability, the main NetSI methods use a global/local network metric in combination with a resampling (bootstrap or cross-validation) procedure. In addition, we provide two normalized variability scores over data resampling to measure edge weight stability and node degree stability, and then introduce a stability ranking for edges and nodes. A complete implementation of the NetSI indicators, including the Hamming-Ipsen-Mikhailov (HIM) network distance adopted in this paper is available with the R package nettools. We demonstrate the use of the NetSI family by measuring network stability on four datasets against alternative network reconstruction methods. First, the effect of sample size on stability of inferred networks is studied in a gold standard framework on yeast-like data from the Gene Net Weaver simulator. We also consider the impact of varying modularity on a set of structurally different networks (50 nodes, from 2 to 10 modules), and then of complex feature covariance structure, showing the different behaviours of standard reconstruction methods based on Pearson correlation, Maximum Information Coefficient (MIC) and False Discovery Rate (FDR) strategy. Finally, we demonstrate a strong combined effect of different reconstruction methods and phenotype subgroups on a hepatocellular carcinoma miRNA microarray dataset (240 subjects), and we validate the analysis on a second dataset (166 subjects) with good reproducibility.
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Affiliation(s)
- Michele Filosi
- MPBA/Center for Information and Communication Technology, Fondazione Bruno Kessler, Trento, Italy
- CIBIO, University of Trento, Trento, Italy
| | - Roberto Visintainer
- MPBA/Center for Information and Communication Technology, Fondazione Bruno Kessler, Trento, Italy
| | - Samantha Riccadonna
- Department of Computational Biology, Research and Innovation Centre, Fondazione Edmund Mach (FEM), San Michele all'Adige, Italy
| | - Giuseppe Jurman
- MPBA/Center for Information and Communication Technology, Fondazione Bruno Kessler, Trento, Italy
- * E-mail:
| | - Cesare Furlanello
- MPBA/Center for Information and Communication Technology, Fondazione Bruno Kessler, Trento, Italy
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Fujita KA, Ostaszewski M, Matsuoka Y, Ghosh S, Glaab E, Trefois C, Crespo I, Perumal TM, Jurkowski W, Antony PMA, Diederich N, Buttini M, Kodama A, Satagopam VP, Eifes S, del Sol A, Schneider R, Kitano H, Balling R. Integrating pathways of Parkinson's disease in a molecular interaction map. Mol Neurobiol 2014; 49:88-102. [PMID: 23832570 PMCID: PMC4153395 DOI: 10.1007/s12035-013-8489-4] [Citation(s) in RCA: 164] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2013] [Accepted: 06/13/2013] [Indexed: 12/12/2022]
Abstract
Parkinson's disease (PD) is a major neurodegenerative chronic disease, most likely caused by a complex interplay of genetic and environmental factors. Information on various aspects of PD pathogenesis is rapidly increasing and needs to be efficiently organized, so that the resulting data is available for exploration and analysis. Here we introduce a computationally tractable, comprehensive molecular interaction map of PD. This map integrates pathways implicated in PD pathogenesis such as synaptic and mitochondrial dysfunction, impaired protein degradation, alpha-synuclein pathobiology and neuroinflammation. We also present bioinformatics tools for the analysis, enrichment and annotation of the map, allowing the research community to open new avenues in PD research. The PD map is accessible at http://minerva.uni.lu/pd_map .
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Affiliation(s)
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
- Integrated Biobank of Luxembourg, Luxembourg City, Luxembourg
| | | | - Samik Ghosh
- The Systems Biology Institute, Minato-ku, Tokyo, Japan
| | - Enrico Glaab
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
| | - Christophe Trefois
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
| | - Isaac Crespo
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
| | - Thanneer M. Perumal
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
| | - Wiktor Jurkowski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
| | - Paul M. A. Antony
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
| | - Nico Diederich
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
- Department of Neuroscience, Centre Hospitalier Luxembourg, Luxembourg City, Luxembourg
| | - Manuel Buttini
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
| | - Akihiko Kodama
- Faculty of Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Venkata P. Satagopam
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
- Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Serge Eifes
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
| | - Antonio del Sol
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
- Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Hiroaki Kitano
- The Systems Biology Institute, Minato-ku, Tokyo, Japan
- Sony Computer Science Laboratories, Shinagawa-ku, Tokyo, Japan
- Division of Systems Biology, Cancer Institute, Tokyo, Japan
- Open Biology Unit, Okinawa Institute of Science and Technology, Kunigami, Okinawa Japan
| | - Rudi Balling
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
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40
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Ansari S, Binder J, Boue S, Di Fabio A, Hayes W, Hoeng J, Iskandar A, Kleiman R, Norel R, O'Neel B, Peitsch MC, Poussin C, Pratt D, Rhrissorrakrai K, Schlage WK, Stolovitzky G, Talikka M. On Crowd-verification of Biological Networks. Bioinform Biol Insights 2013; 7:307-25. [PMID: 24151423 PMCID: PMC3798292 DOI: 10.4137/bbi.s12932] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Biological networks with a structured syntax are a powerful way of representing biological information generated from high density data; however, they can become unwieldy to manage as their size and complexity increase. This article presents a crowd-verification approach for the visualization and expansion of biological networks. Web-based graphical interfaces allow visualization of causal and correlative biological relationships represented using Biological Expression Language (BEL). Crowdsourcing principles enable participants to communally annotate these relationships based on literature evidences. Gamification principles are incorporated to further engage domain experts throughout biology to gather robust peer-reviewed information from which relationships can be identified and verified. The resulting network models will represent the current status of biological knowledge within the defined boundaries, here processes related to human lung disease. These models are amenable to computational analysis. For some period following conclusion of the challenge, the published models will remain available for continuous use and expansion by the scientific community.
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Crommelin DJA, Sindelar RD, Meibohm B. Genomics, Other “Omic” Technologies, Personalized Medicine, and Additional Biotechnology-Related Techniques. PHARMACEUTICAL BIOTECHNOLOGY 2013. [PMCID: PMC7122419 DOI: 10.1007/978-1-4614-6486-0_8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
The products resulting for biotechnologies continue to grow at an exponential rate, and the expectations are that an even greater percentage of drug development will be in the area of the biologics. In 2011, worldwide there were over 800 new biotech drugs and treatments in development including 23 antisense, 64 cell therapy, 50 gene therapy, 300 monoclonal antibodies, 78 recombinant proteins, and 298 vaccines (PhRMA 2012). Pharmaceutical biotechnology techniques are at the core of most methodologies used today for drug discovery and development of both biologics and small molecules. While recombinant DNA technology and hybridoma techniques were the major methods utilized in pharmaceutical biotechnology through most of its historical timeline, our ever-widening understanding of human cellular function and disease processes and a wealth of additional and innovative biotechnologies have been, and will continue to be, developed in order to harvest the information found in the human genome. These technological advances will provide a better understanding of the relationship between genetics and biological function, unravel the underlying causes of disease, explore the association of genomic variation and drug response, enhance pharmaceutical research, and fuel the discovery and development of new and novel biopharmaceuticals. These revolutionary technologies and additional biotechnology-related techniques are improving the very competitive and costly process of drug development of new medicinal agents, diagnostics, and medical devices. Some of the technologies and techniques described in this chapter are both well established and commonly used applications of biotechnology producing potential therapeutic products now in development including clinical trials. New techniques are emerging at a rapid and unprecedented pace and their full impact on the future of molecular medicine has yet to be imagined.
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Affiliation(s)
- Daan J. A. Crommelin
- Department of Pharmaceutical Sciences, Utrecht University, Utrecht, Utrecht The Netherlands
| | - Robert D. Sindelar
- Department of Pharmaceutical Sciences and Department of Medicine, The University of British Columbia, Vancouver, British Columbia Canada
| | - Bernd Meibohm
- Department of Pharmaceutical Sciences, University of Tennessee Health Science Center, College of Pharmacy, Memphis, Tennessee USA
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42
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Tarca AL, Lauria M, Unger M, Bilal E, Boue S, Kumar Dey K, Hoeng J, Koeppl H, Martin F, Meyer P, Nandy P, Norel R, Peitsch M, Rice JJ, Romero R, Stolovitzky G, Talikka M, Xiang Y, Zechner C. Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge. ACTA ACUST UNITED AC 2013; 29:2892-9. [PMID: 23966112 DOI: 10.1093/bioinformatics/btt492] [Citation(s) in RCA: 101] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
MOTIVATION After more than a decade since microarrays were used to predict phenotype of biological samples, real-life applications for disease screening and identification of patients who would best benefit from treatment are still emerging. The interest of the scientific community in identifying best approaches to develop such prediction models was reaffirmed in a competition style international collaboration called IMPROVER Diagnostic Signature Challenge whose results we describe herein. RESULTS Fifty-four teams used public data to develop prediction models in four disease areas including multiple sclerosis, lung cancer, psoriasis and chronic obstructive pulmonary disease, and made predictions on blinded new data that we generated. Teams were scored using three metrics that captured various aspects of the quality of predictions, and best performers were awarded. This article presents the challenge results and introduces to the community the approaches of the best overall three performers, as well as an R package that implements the approach of the best overall team. The analyses of model performance data submitted in the challenge as well as additional simulations that we have performed revealed that (i) the quality of predictions depends more on the disease endpoint than on the particular approaches used in the challenge; (ii) the most important modeling factor (e.g. data preprocessing, feature selection and classifier type) is problem dependent; and (iii) for optimal results datasets and methods have to be carefully matched. Biomedical factors such as the disease severity and confidence in diagnostic were found to be associated with the misclassification rates across the different teams. AVAILABILITY The lung cancer dataset is available from Gene Expression Omnibus (accession, GSE43580). The maPredictDSC R package implementing the approach of the best overall team is available at www.bioconductor.org or http://bioinformaticsprb.med.wayne.edu/.
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Affiliation(s)
- Adi L Tarca
- Department of Computer Science, Wayne State University, Perinatology Research Branch, NICHD/NIH, Detroit, MI 48201, USA, The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto 38068, Italy, ETH Zurich, Zurich 8092, Switzerland, IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA and Philip Morris International, Research & Development, Neuchâtel CH-2000, Switzerland
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Hoeng J, Talikka M, Martin F, Sewer A, Yang X, Iskandar A, Schlage WK, Peitsch MC. Case study: the role of mechanistic network models in systems toxicology. Drug Discov Today 2013; 19:183-92. [PMID: 23933191 DOI: 10.1016/j.drudis.2013.07.023] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2013] [Revised: 07/14/2013] [Accepted: 07/25/2013] [Indexed: 10/26/2022]
Abstract
Twenty first century systems toxicology approaches enable the discovery of biological pathways affected in response to active substances. Here, we briefly summarize current network approaches that facilitate the detailed mechanistic understanding of the impact of a given stimulus on a biological system. We also introduce our network-based method with two use cases and show how causal biological network models combined with computational methods provide quantitative mechanistic insights. Our approach provides a robust comparison of the transcriptional responses in different experimental systems and enables the identification of network-based biomarkers modulated in response to exposure. These advances can also be applied to pharmacology, where the understanding of disease mechanisms and adverse drug effects is imperative for the development of efficient and safe treatment options.
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Affiliation(s)
- Julia Hoeng
- Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
| | - Marja Talikka
- Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
| | - Florian Martin
- Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
| | - Alain Sewer
- Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
| | - Xiang Yang
- Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
| | - Anita Iskandar
- Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
| | - Walter K Schlage
- Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland
| | - Manuel C Peitsch
- Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuchâtel, Switzerland.
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Pinna A, Heise S, Flassig RJ, de la Fuente A, Klamt S. Reconstruction of large-scale regulatory networks based on perturbation graphs and transitive reduction: improved methods and their evaluation. BMC SYSTEMS BIOLOGY 2013; 7:73. [PMID: 23924435 PMCID: PMC4231426 DOI: 10.1186/1752-0509-7-73] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2013] [Accepted: 08/05/2013] [Indexed: 02/08/2023]
Abstract
Background The data-driven inference of intracellular networks is one of the key challenges of computational and systems biology. As suggested by recent works, a simple yet effective approach for reconstructing regulatory networks comprises the following two steps. First, the observed effects induced by directed perturbations are collected in a signed and directed perturbation graph (PG). In a second step, Transitive Reduction (TR) is used to identify and eliminate those edges in the PG that can be explained by paths and are therefore likely to reflect indirect effects. Results In this work we introduce novel variants for PG generation and TR, leading to significantly improved performances. The key modifications concern: (i) use of novel statistical criteria for deriving a high-quality PG from experimental data; (ii) the application of local TR which allows only short paths to explain (and remove) a given edge; and (iii) a novel strategy to rank the edges with respect to their confidence. To compare the new methods with existing ones we not only apply them to a recent DREAM network inference challenge but also to a novel and unprecedented synthetic compendium consisting of 30 5000-gene networks simulated with varying biological and measurement error variances resulting in a total of 270 datasets. The benchmarks clearly demonstrate the superior reconstruction performance of the novel PG and TR variants compared to existing approaches. Moreover, the benchmark enabled us to draw some general conclusions. For example, it turns out that local TR restricted to paths with a length of only two is often sufficient or even favorable. We also demonstrate that considering edge weights is highly beneficial for TR whereas consideration of edge signs is of minor importance. We explain these observations from a graph-theoretical perspective and discuss the consequences with respect to a greatly reduced computational demand to conduct TR. Finally, as a realistic application scenario, we use our framework for inferring gene interactions in yeast based on a library of gene expression data measured in mutants with single knockouts of transcription factors. The reconstructed network shows a significant enrichment of known interactions, especially within the 100 most confident (and for experimental validation most relevant) edges. Conclusions This paper presents several major achievements. The novel methods introduced herein can be seen as state of the art for inference techniques relying on perturbation graphs and transitive reduction. Another key result of the study is the generation of a new and unprecedented large-scale in silico benchmark dataset accounting for different noise levels and providing a solid basis for unbiased testing of network inference methodologies. Finally, applying our approach to Saccharomyces cerevisiae suggested several new gene interactions with high confidence awaiting experimental validation.
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Affiliation(s)
- Andrea Pinna
- Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.
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Kirk P, Thorne T, Stumpf MPH. Model selection in systems and synthetic biology. Curr Opin Biotechnol 2013; 24:767-74. [PMID: 23578462 DOI: 10.1016/j.copbio.2013.03.012] [Citation(s) in RCA: 87] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2012] [Revised: 03/07/2013] [Accepted: 03/14/2013] [Indexed: 11/17/2022]
Abstract
Developing mechanistic models has become an integral aspect of systems biology, as has the need to differentiate between alternative models. Parameterizing mathematical models has been widely perceived as a formidable challenge, which has spurred the development of statistical and optimisation routines for parameter inference. But now focus is increasingly shifting to problems that require us to choose from among a set of different models to determine which one offers the best description of a given biological system. We will here provide an overview of recent developments in the area of model selection. We will focus on approaches that are both practical as well as build on solid statistical principles and outline the conceptual foundations and the scope for application of such methods in systems biology.
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Affiliation(s)
- Paul Kirk
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK
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Bilal E, Dutkowski J, Guinney J, Jang IS, Logsdon BA, Pandey G, Sauerwine BA, Shimoni Y, Moen Vollan HK, Mecham BH, Rueda OM, Tost J, Curtis C, Alvarez MJ, Kristensen VN, Aparicio S, Børresen-Dale AL, Caldas C, Califano A, Friend SH, Ideker T, Schadt EE, Stolovitzky GA, Margolin AA. Improving breast cancer survival analysis through competition-based multidimensional modeling. PLoS Comput Biol 2013; 9:e1003047. [PMID: 23671412 PMCID: PMC3649990 DOI: 10.1371/journal.pcbi.1003047] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2012] [Accepted: 03/18/2013] [Indexed: 01/09/2023] Open
Abstract
Breast cancer is the most common malignancy in women and is responsible for hundreds of thousands of deaths annually. As with most cancers, it is a heterogeneous disease and different breast cancer subtypes are treated differently. Understanding the difference in prognosis for breast cancer based on its molecular and phenotypic features is one avenue for improving treatment by matching the proper treatment with molecular subtypes of the disease. In this work, we employed a competition-based approach to modeling breast cancer prognosis using large datasets containing genomic and clinical information and an online real-time leaderboard program used to speed feedback to the modeling team and to encourage each modeler to work towards achieving a higher ranked submission. We find that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble. We also find that model scores are highly consistent across multiple independent evaluations. This study serves as the pilot phase of a much larger competition open to the whole research community, with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective, transparent system for assessing prognostic models.
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Affiliation(s)
- Erhan Bilal
- IBM TJ Watson Research, Yorktown Heights, New York, United States of America
| | - Janusz Dutkowski
- Departments of Medicine and Bioengineering, University of California San Diego, La Jolla, California, United States of America
| | - Justin Guinney
- Sage Bionetworks, Seattle, Washington, United States of America
| | - In Sock Jang
- Sage Bionetworks, Seattle, Washington, United States of America
| | - Benjamin A. Logsdon
- Sage Bionetworks, Seattle, Washington, United States of America
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Gaurav Pandey
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | | | - Yishai Shimoni
- Columbia Initiative in Systems Biology, Columbia University, New York, New York, United States of America
- Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, United States of America
| | - Hans Kristian Moen Vollan
- Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- The K. G. Jebsen Center for Breast Cancer Research, Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Cambridge Research Institute, Cancer Research UK, Cambridge, United Kingdom
- Department of Oncology, University of Cambridge, Cambridge, United Kingdom
- Department of Oncology, Division of Cancer Medicine, Surgery and Transplantation, Oslo University Hospital, Oslo, Norway
| | | | - Oscar M. Rueda
- Cambridge Research Institute, Cancer Research UK, Cambridge, United Kingdom
- Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Jorg Tost
- Laboratory for Epigenetics and Environment, Centre National de Génotypage, CEA, Institut de Génomique, Evry, France
| | - Christina Curtis
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Mariano J. Alvarez
- Columbia Initiative in Systems Biology, Columbia University, New York, New York, United States of America
- Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, United States of America
| | - Vessela N. Kristensen
- Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- The K. G. Jebsen Center for Breast Cancer Research, Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Clinical Molecular Biology, Division of Medicine, Akershus University Hospital, Ahus, Norway
| | - Samuel Aparicio
- Department of Pathology and Laboratory Medicine, University of British Colombia, Vancouver, British Colombia, Canada
- Molecular Oncology, British Colombia Cancer Research Center, Vancouver, British Colombia, Canada
| | - Anne-Lise Børresen-Dale
- Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- The K. G. Jebsen Center for Breast Cancer Research, Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Carlos Caldas
- Cambridge Research Institute, Cancer Research UK, Cambridge, United Kingdom
- Department of Oncology, University of Cambridge, Cambridge, United Kingdom
- Cambridge Experimental Cancer Medicine Centre, Cambridge, United Kingdom
- Cambridge Breast Unit, Cambridge University Hospital NHS Foundation Trust and NIHR Cambridge Biomedical Research Centre, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Andrea Califano
- Columbia Initiative in Systems Biology, Columbia University, New York, New York, United States of America
- Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, United States of America
- Department of Biomedical Informatics, Columbia University, New York, New York, United States of America
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York, United States of America
- Institute for Cancer Genetics, Columbia University, Columbia University, New York, New York, United States of America
- Herbert Irving Comprehensive Cancer Center, Columbia University, New York, New York, United States of America
| | | | - Trey Ideker
- Departments of Medicine and Bioengineering, University of California San Diego, La Jolla, California, United States of America
| | - Eric E. Schadt
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
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Creixell P, Schoof EM, Erler JT, Linding R. Navigating cancer network attractors for tumor-specific therapy. Nat Biotechnol 2013; 30:842-8. [PMID: 22965061 DOI: 10.1038/nbt.2345] [Citation(s) in RCA: 95] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Cells employ highly dynamic signaling networks to drive biological decision processes. Perturbations to these signaling networks may attract cells to new malignant signaling and phenotypic states, termed cancer network attractors, that result in cancer development. As different cancer cells reach these malignant states by accumulating different molecular alterations, uncovering these mechanisms represents a grand challenge in cancer biology. Addressing this challenge will require new systems-based strategies that capture the intrinsic properties of cancer signaling networks and provide deeper understanding of the processes by which genetic lesions perturb these networks and lead to disease phenotypes. Network biology will help circumvent fundamental obstacles in cancer treatment, such as drug resistance and metastasis, empowering personalized and tumor-specific cancer therapies.
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Affiliation(s)
- Pau Creixell
- Cellular Signal Integration Group (C-SIG), Center for Biological Sequence Analysis (CBS), Department of Systems Biology, Technical University of Denmark (DTU), Lyngby, Denmark
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Aghaeepour N, Finak G, Hoos H, Mosmann TR, Brinkman R, Gottardo R, Scheuermann RH. Critical assessment of automated flow cytometry data analysis techniques. Nat Methods 2013; 10:228-38. [PMID: 23396282 PMCID: PMC3906045 DOI: 10.1038/nmeth.2365] [Citation(s) in RCA: 350] [Impact Index Per Article: 31.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2012] [Accepted: 01/14/2013] [Indexed: 12/14/2022]
Abstract
In this analysis, the authors directly compared the performance of flow cytometry data processing algorithms to manual gating approaches. The results offer information of practical utility about the performance of the algorithms as applied to different data sets and challenges. Traditional methods for flow cytometry (FCM) data processing rely on subjective manual gating. Recently, several groups have developed computational methods for identifying cell populations in multidimensional FCM data. The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of these methods on two tasks: (i) mammalian cell population identification, to determine whether automated algorithms can reproduce expert manual gating and (ii) sample classification, to determine whether analysis pipelines can identify characteristics that correlate with external variables (such as clinical outcome). This analysis presents the results of the first FlowCAP challenges. Several methods performed well as compared to manual gating or external variables using statistical performance measures, which suggests that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis.
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Affiliation(s)
- Nima Aghaeepour
- Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, British Columbia, Canada
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Martin SF, Falkenberg H, Dyrlund TF, Khoudoli GA, Mageean CJ, Linding R. PROTEINCHALLENGE: crowd sourcing in proteomics analysis and software development. J Proteomics 2012; 88:41-6. [PMID: 23220569 DOI: 10.1016/j.jprot.2012.11.014] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2012] [Revised: 11/08/2012] [Accepted: 11/13/2012] [Indexed: 10/27/2022]
Abstract
In large-scale proteomics studies there is a temptation, after months of experimental work, to plug resulting data into a convenient-if poorly implemented-set of tools, which may neither do the data justice nor help answer the scientific question. In this paper we have captured key concerns, including arguments for community-wide open source software development and "big data" compatible solutions for the future. For the meantime, we have laid out ten top tips for data processing. With these at hand, a first large-scale proteomics analysis hopefully becomes less daunting to navigate. However there is clearly a real need for robust tools, standard operating procedures and general acceptance of best practises. Thus we submit to the proteomics community a call for a community-wide open set of proteomics analysis challenges--PROTEINCHALLENGE--that directly target and compare data analysis workflows, with the aim of setting a community-driven gold standard for data handling, reporting and sharing.
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Affiliation(s)
- Sarah F Martin
- Kinetic Parameter Facility, Centre for Synthetic and Systems Biology-SynthSys, University of Edinburgh, UK
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Mei H, Xia T, Feng G, Zhu J, Lin SM, Qiu Y. Opportunities in systems biology to discover mechanisms and repurpose drugs for CNS diseases. Drug Discov Today 2012; 17:1208-16. [DOI: 10.1016/j.drudis.2012.06.015] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2012] [Revised: 06/04/2012] [Accepted: 06/25/2012] [Indexed: 01/07/2023]
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