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Computational and Structural Biotechnology Reports 2025年

作者： Fred T.G. White Anna Heintz-Buschart Lemeng Dong Harro J. Bouwmeester Johan A. Westerhuis Age K. Smilde Biosystems Data Analysis Group Swammerdam Institute for Life Sciences University of Amsterdam Amsterdam The Netherlands Plant Hormone Biology Group Swammerdam Institute for Life Sciences University of Amsterdam Amsterdam The Netherlands

When omics measurements are employed to discover novel parts of a pathway of interest, experiments are usually designed to induce variation in said pathway. Experimental treatment groups can display differences including beyond the pathway of interest. Pathway discovery by coexpression analysis is therefore hampered by the dominant variance induced by the design. However, minor changes in ambient conditions that could not be controlled for, affect the biological sample. This can lead to small, coordinated differences in gene expression or metabolite concentration. These differences can occur within pathways of interest between replicates in the same controlled experimental condition. These systematic differences are usually not directly considered, but we argue here that they can be used to improve the detection of features that co-express. We introduce a novel framework “MASCARA” which combines ANOVA simultaneous component analysis and partial least squares regression to remove the experimentally induced variance and investigate multivariate relationships in the non-designed variance. Through extensive simulations that mimic the structure of a designed transcriptome study, the benefits of MASCARA are demonstrated and tested against a selection of commonly used methods. In a coexpression analysis of a real dataset MASCARA detects several relevant novel transcripts. The concept is further demonstrated in a designed metabolomics study. Here, distinct groups of features more tightly related within treatment groups or related across experimental conditions can be described. Our results indicate that after adjusting for experimental differences, the remaining variance contains enough useful structure to study coexpression.

关键词： Omics coexpression PLS ASCA Multivariate data analysis Transcriptomics Metabolomics Regression Dimensionality Reduction

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Science Gateway canvas: A business reference model for Science Gateways 1

Science Gateway canvas: A business reference model for Scien...

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1st Workshop on the Science of Cyberinfrastructure: Research, Experience, Applications and Models, SCREAM 2015

作者： Shahand, Shayan Van Kampen, Antoine H. C. Olabarriaga, Sílvia D. e-Science Group Dept. of Clinical Epidemiology Biostatistics and Bioinformatics Academic Medical Center University of Amsterdam Netherlands Bioinformatics Laboratory Dept. of Clinical Epidemiology Biostatistics and Bioinformatics Academic Medical Center University of Amsterdam Netherlands Biosystems Data Analysis Group Swammerdam Institute for Life Sciences University of Amsterdam Netherlands

ISBN: (纸本)9781450335669

Science Gateways (SGs) have emerged as systems that facilitate access to cyberinfrastructures. There is a growing interest in the exploitation and development of SGs. However, it remains challenging to understand and design SG with the required properties because of the complex nature of SGs. Additionally, it is difficult to decide upon frameworks that can be used to build them. In this paper we propose the Science Gateway Canvas, a business reference model for SGs that embodies the common SG functions and their organization into groups and categories. We used the Science Gateway Canvas for a systematic analysis and comparison of the functions offered by a selection of available frameworks that are used to build SGs. This illustrated the applicability of the Science Gateway Canvas as a comprehensive and generic reference model for understanding SGs. The canvas can also be used for systematic analysis by scientists who are searching for an existing SG that supports their research goal or developers who want to determine the functional requirements for a new SG. Copyright © 2015 ACM.

关键词： Business reference model Collaboratory Cyberinfrastructure e-Infrastructure e-Research environment e-Science Problem Solving Environment (PSE) Science Gateway (SG) Science Gateway Canvas (SGC) Science Gateway development Science Gateway frameworks Science Gateway technologies Science Portal Virtual Laboratory (VL) Virtual Research Environment (VRE)

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Gene expression,transcription factor binding and histone modification predict leaf adaxial-abaxial polarity related genes

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Horticultural Plant Journal 2024年第4期10卷 971-982页

作者： Wei Sun Zhicheng Zhang Guusje Bonnema Xiaowu Wang Aalt Dirk Jan van Dijk State Key Laboratory of Vegetable Biobreeding Institute of Vegetables and FlowersChinese Academy of Agricultural SciencesBeijing 100081China Sino-Dutch Joint Lab of Vegetable Genomics Beijing 100081China Bioinformatics Group Wageningen University and Research6708 PB Wageningenthe Netherlands Plant Breeding Wageningen University and Research6708 PB Wageningenthe Netherlands Biosystems Data Analysis Swammerdam Institute for Life SciencesUniversity of Amsterdam1090 GE Amsterdamthe Netherlands

Leaf adaxial-abaxial(ad-abaxial)polarity is crucial for leaf morphology and function,but the genetic machinery governing this process remains *** uncover critical genes involved in leaf ad-abaxial patterning,we applied a combination of in silico prediction using machine learning(ML)and experimental analysis.A Random Forest model was trained using genes known to influence ad-abaxial polarity as ground *** expression data from various tissues and conditions as well as promoter regulation data derived from transcription factor chromatin immunoprecipitation sequencing(ChIP-seq)was used as input,enabling the prediction of novel ad-abaxial polarity-related genes and additional transcription *** to this,available and newly-obtained transcriptome data enabled us to identify genes differentially expressed across leaf ad-abaxial *** on these analyses,we obtained a set of 111 novel genes which are involved in leaf ad-abaxial *** explore implications for vegetable crop breeding,we examined the conservation of expression patterns between Arabidopsis and Brassica rapa using single-cell *** results demonstrated the utility of our computational approach for predicting candidate genes in crop *** findings expand the understanding of the genetic networks governing leaf ad-abaxial differentiation in agriculturally important vegetables,enhancing comprehension of natural variation impacting leaf morphology and development,with demonstrable breeding applications.

关键词： Machine learning Leaf polarity Arabidopsis thaliana Brassica rapa Transcription factor

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Endocrine pulse identification using penalized methods and a minimum set of assumptions. (vol 298, pg E146, 2010)

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AMERICAN JOURNAL OF PHYSIOLOGY-ENDOCRINOLOGY AND METABOLISM 2010年第4期298卷 E894-E894页

作者： Vis, D. J. Westerhuis, J. A. Hoefsloot, H. C. Pijl, H. Roelfsema, F. van der Greef, J. Smilde, A. K. BioSystems Data Analysis group Swammerdam Institute for Life Science University of Amsterdam Amsterdam Leiden University Medical Center Department of Endocrinology and Metabolic Diseases Leiden The Netherlands TNO Quality of Life Zeist

The detection of hormone secretion episodes is important for understanding normal and abnormal endocrine functioning, but pulse identification from hormones measured with short interval sampling is challenging. Furthermore, to obtain useable results, the model underlying hormone secretion and clearance must be augmented with restrictions based on biologically acceptable assumptions. Here, using the assumption that there are only a few time points at which a hormone is secreted, we used a modern penalized nonlinear least-squares setup to select the number of secretion events. We did not assume a particular shape or frequency distribution for the secretion pulses. Our pulse identfication method, VisPulse, worked well with luteinizing hormone (LH), cortisol, growth hormone, or testosterone. In particular, applying our modeling strategy to previous LH data revealed a good correlation between the modeled and measured LH hormone concentrations, the estimated secretion pattern was sparse, and the small and structureless residuals indicated a proper model with a good fit. We benchmarked our method to AutoDecon, a commonly used hormone secretion model, and performed releasing hormone infusion experiments. The results of these experiments confirmed that our method is accurate and outperforms AutoDecon, especially for detecting silent periods and small secretion events, suggesting a high-secretion event resolution. Method validation using (releasing hormone) infusion data revealed sensitivities and selectivities of 0.88 and 0.95 and of 0.69 and 0.91 for VisPulse and AutoDecon, respectively.

关键词： AutoDecon Infusion study Model complexity Model selection Nonlinear fitting Pulsatility Secretion Simulation study

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Parameter selection for peak alignment in chromatographic sample profiling: Objective quality indicators and use of control samples

Parameter selection for peak alignment in chromatographic sa...

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作者： Peters, Sonja Van Velzen, Ewoud Janssen, Hans-Gerd Advanced Measurement and Imaging Unilever Research and Development Unilever Food and Health Research Institute P. O. Box 114 Vlaardingen 3130 AC Netherlands Polymer Analysis Group Van't Hoff Institute for Molecular Sciences University of Amsterdam Nieuwe Achtergracht 166 Amsterdam 1018 WV Netherlands Biosystems Data Analysis Swammerdam Institute for Life Sciences University of Amsterdam Nieuwe Achtergracht 166 Amsterdam 1018 WV Netherlands

In chromatographic profiling applications, peak alignment is often essential as most chromatographic systems exhibit small peak shifts over time. When using currently available alignment algorithms, there are several parameters that determine the outcome of the alignment process. Selecting the optimum set of parameters, however, is not straightforward, and the quality of an alignment result is at least partly determined by subjective decisions. Here, we demonstrate a new strategy to objectively determine the quality of an alignment result. This strategy makes use of a set of control samples that are analysed both spiked and non-spiked. With this set, not only the system and the method can be checked but also the quality of the peak alignment can be evaluated. The developed strategy was tested on a representative metabolomics data set using three software packages, namely Markerlynx, MZmine and MetAlign. The results indicate that the method was able to assess and define the quality of an alignment process without any subjective interference of the analyst, making the method a valuable contribution to the data handling process of chromatography-based metabolomics data.

关键词： Quality control

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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Leveraging implicit knowledge in neural networks for functional dissection and engineering of proteins (vol 1, pg 225, 2019)

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NATURE MACHINE INTELLIGENCE 2019年第6期1卷 290-290页

作者： zu Belzen, Julius Upmeier Buergel, Thore Holderbach, Stefan Bubeck, Felix Adam, Lukas Gandor, Catharina Klein, Marita Mathony, Jan Pfuderer, Pauline Platz, Lukas Przybilla, Moritz Schwendemann, Max Heid, Daniel Hoffmann, Mareike Daniela Jendrusch, Michael Schmelas, Carolin Waldhauer, Max Lehmann, Irina Niopek, Dominik Eils, Roland Synthetic Biology Group Institute for Pharmacy and Molecular Biotechnology and Center for Quantitative Analysis of Molecular and Cellular Biosystems University of Heidelberg Heidelberg Germany Digital Health Center Berlin Institute of Health (BIH) and Charité University Medicine Berlin Germany Department of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Department of Infectious Diseases Virology University Hospital Heidelberg Heidelberg Germany BioQuant Center and Cluster of Excellence CellNetworks at Heidelberg University Heidelberg Germany Molecular Epidemiology Unit Berlin Institute of Health (BIH) and Charité University Medicine Berlin Germany Health Data Science Unit University Hospital Heidelberg Heidelberg Germany

Proteins are nature’s most versatile molecular machines. Deep neural networks trained on large protein datasets have recently been used to tackle the unmet complexity of protein sequence–function relationships. The implicit knowledge contained in these networks represents a powerful, but thus far inaccessible, resource for understanding protein biology. Here, we show that occlusion-based sensitivity analysis can leverage the knowledge present in deep-neural-network-based protein sequence classifiers to identify functionally relevant parts of proteins. We first validated our approach by successfully predicting positions that mediate small molecule binding or catalytic activity across different protein classes. Next, we inferred the impact of point mutations on the activity of ERK and HRas, signalling factors frequently deregulated in cancer. Finally, we used our approach to identify engineering hotspots in CRISPR–Cas9 and anti-CRISPR protein AcrIIA4. Our work demonstrates how implicit knowledge in neural networks can be harnessed for protein functional dissection and protein engineering.

关键词： Proteins

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Corrigendum to “Quantification of lipoprotein profiles by nuclear magnetic resonance spectroscopy and multivariate data analysis” [Trends Anal Chem 94 (2017) 210–219]

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TrAC Trends in Analytical Chemistry 2019年 119卷

作者： Violetta Aru Chloie Lam Bekzod Khakimov Huub C.J. Hoefsloot Gooitzen Zwanenburg Mads Vendelbo Lind Hartmut Schäfer John van Duynhoven Doris M. Jacobs Age K. Smilde Søren B. Engelsen Department of Food Science University of Copenhagen Rolighedsvej 26 1958 Frederiksberg Denmark Biosystems Data Analysis Group University of Amsterdam 1090 GE Amsterdam the Netherlands Laboratory of Biophysics Wageningen University Wageningen 6703 HA the Netherlands Department of Nutrition Exercise and Sports University of Copenhagen Rolighedsvej 26 1958 Frederiksberg Denmark Bruker BioSpin GmbH 76287 Rheinstetten Germany Unilever R&D Vlaardingen Olivier van Noortlaan 120 3130AC Vlaardingen the Netherlands

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