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1. Bioinformatic interrogation of expression array data to identify nutritionally regulated genes potentially modulated by DNA methylation

Author

Chris T. Evelo, John C. Mathers, Jill A. McKay, Caroline L Relton, Dianne Ford, Michiel E. Adriaens, Bioinformatica, RS: CARIM School for Cardiovascular Diseases, and RS: NUTRIM - R4 - Gene-environment interaction

Subjects
Regulation of gene expression, Genetics, DNA methylation, Bioinformatics, Endocrinology, Diabetes and Metabolism, Pioneer factor, Biology, CpG islands, Epigenetics of physical exercise, Differentially methylated regions, Histone methylation, Illumina Methylation Assay, In silico promoter analysis, Gene expression, RNA-Directed DNA Methylation, Research Paper, Transcription factor binding sites
Abstract

DNA methylation occurs at CpG dinucleotide sites within the genome and is recognised as one of the mechanisms involved in regulation of gene expression. CpG sites are relatively underrepresented in the mammalian genome, but occur densely in regions called CpG islands (CGIs). CGIs located in the promoters of genes inhibit transcription when methylated by impeding transcription factor binding. Due to the malleable nature of DNA methylation, environmental factors are able to influence promoter CGI methylation patterns and thus influence gene expression. Recent studies have provided evidence that nutrition (and other environmental exposures) can cause altered CGI methylation but, with a few exceptions, the genes influenced by these exposures remain largely unknown. Here we describe a novel bioinformatics approach for the analysis of gene expression microarray data designed to identify regulatory sites within promoters of differentially expressed genes that may be influenced by changes in DNA methylation.

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2. An integrated bioinformatics approach to improve two-color microarray quality-control: impact on biological conclusions

Author

Cristina Luceri, Chris T. Evelo, Arie van Erk, Rachel I. M. van Haaften, Farmacologie & Toxicologie, Bioinformatica, and RS: NUTRIM - R4 - Gene-environment interaction

Subjects
business.industry, Microarray analysis techniques, media_common.quotation_subject, Endocrinology, Diabetes and Metabolism, Polyphenols, Normalisation, Quality control, Artifact (software development), Microarray, Two Color Microarray, Brief Communication, computer.software_genre, Bioinformatics, Pathway analysis, Red wine polyphenols, Data visualization, Genetics, Medicine, Quality (business), Data mining, business, Cluster analysis, computer, media_common
Abstract

Omics technology used for large-scale measurements of gene expression is rapidly evolving. This work pointed out the need of an extensive bioinformatics analyses for array quality assessment before and after gene expression clustering and pathway analysis. A study focused on the effect of red wine polyphenols on rat colon mucosa was used to test the impact of quality control and normalisation steps on the biological conclusions. The integration of data visualization, pathway analysis and clustering revealed an artifact problem that was solved with an adapted normalisation. We propose a possible point to point standard analysis procedure, based on a combination of clustering and data visualization for the analysis of microarray data.

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3. The role of bioinformatics in pathway curation

Author

Thomas Kelder, Chris T. Evelo, Andra Waagmeester, Dept. of Advanced Computing Sciences, Bioinformatica, RS: CARIM School for Cardiovascular Diseases, and RS: NUTRIM - R4 - Gene-environment interaction

Subjects
Bioinformatics, Genomic data, Endocrinology, Diabetes and Metabolism, Data analysis, Biological pathways, Genomics, Mini-Review, Biology, Data science, Variety (cybernetics), Visualization, Community curation, ComputingMethodologies_PATTERNRECOGNITION, Genetics, Biological models
Abstract

Diagrams and models of biological pathways are useful tools in biology. Pathway diagrams are mainly used for illustrative purposes for instance in textbooks and in presentations. Pathway models are used in the analysis of genomic data. Bridging the gap between diagrams and models allows not only the analysis of genomics data and interactions but also the visualisation of the results in a variety of different ways. The knowledge needed for pathway creation and curation is available from three distinct sources: databases, literature and experts. We describe the role of bioinformatics in facilitating the creation and curation of pathway.

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4. New insights in Rett syndrome using pathway analysis for transcriptomics data

Author

Susan Steinbusch Coort, Chris T. Evelo, Leopold M. G. Curfs, E. Smeets, Elisa Cirillo, Friederike Ehrhart, Genetica & Celbiologie, Bioinformatica, RS: NUTRIM - R4 - Gene-environment interaction, Promovendi NTM, MUMC+: DA KG Polikliniek (9), RS: FHML MaCSBio, RS: GROW - R4 - Reproductive and Perinatal Medicine, and Complexe Genetica

Subjects
0301 basic medicine, Pathway analysis, Methyl-CpG-Binding Protein 2, Bioinformatics, Systems biology, Rett syndrome, Computational biology, medicine.disease_cause, Rett-Syndrom, MECP2, Biological pathway, 03 medical and health sciences, Mice, 0302 clinical medicine, Systembiologie, Intellectual disability, medicine, Animals, Humans, Child, Gene, Seltene Krankheit, Medicine(all), Mutation, business.industry, Gene Expression Profiling, Infant, Newborn, Bioinformatik, Computational Biology, Infant, Main Topic, General Medicine, medicine.disease, Disease Models, Animal, 030104 developmental biology, Phenotype, Child, Preschool, Female, business, Rare disease, 030217 neurology & neurosurgery, Function (biology), Pathway-Analyse
Abstract

Summary The analysis of transcriptomics data is able to give an overview of cellular processes, but requires sophisticated bioinformatics tools and methods to identify the changes. Pathway analysis software, like PathVisio, captures the information about biological pathways from databases and brings this together with the experimental data to enable visualization and understanding of the underlying processes. Rett syndrome is a rare disease, but still one of the most abundant causes of intellectual disability in females. Cause of this neurological disorder is mutation of one single gene, the methyl-CpG-binding protein 2 (MECP2) gene. This gene is responsible for many steps in neuronal development and function. Although the genetic mutation and the clinical phenotype are well described, the molecular pathways linking them are not yet fully elucidated. In this study we demonstrate a workflow for the analysis of transcriptomics data to identify biological pathways and processes which are changed in a Mecp2-/y mouse model. Electronic supplementary material The online version of this article (doi: 10.1007/s10354-016-0488-4) contains supplementary material, which is available to authorized users.

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5. COVID‐19 Disease Map, a computational knowledge repository of virus‐host interaction mechanisms

Author

Marek Ostaszewski, Anna Niarakis, Alexander Mazein, Inna Kuperstein, Robert Phair, Aurelio Orta‐Resendiz, Vidisha Singh, Sara Sadat Aghamiri, Marcio Luis Acencio, Enrico Glaab, Andreas Ruepp, Gisela Fobo, Corinna Montrone, Barbara Brauner, Goar Frishman, Luis Cristóbal Monraz Gómez, Julia Somers, Matti Hoch, Shailendra Kumar Gupta, Julia Scheel, Hanna Borlinghaus, Tobias Czauderna, Falk Schreiber, Arnau Montagud, Miguel Ponce de Leon, Akira Funahashi, Yusuke Hiki, Noriko Hiroi, Takahiro G Yamada, Andreas Dräger, Alina Renz, Muhammad Naveez, Zsolt Bocskei, Francesco Messina, Daniela Börnigen, Liam Fergusson, Marta Conti, Marius Rameil, Vanessa Nakonecnij, Jakob Vanhoefer, Leonard Schmiester, Muying Wang, Emily E Ackerman, Jason E Shoemaker, Jeremy Zucker, Kristie Oxford, Jeremy Teuton, Ebru Kocakaya, Gökçe Yağmur Summak, Kristina Hanspers, Martina Kutmon, Susan Coort, Lars Eijssen, Friederike Ehrhart, D A B Rex, Denise Slenter, Marvin Martens, Nhung Pham, Robin Haw, Bijay Jassal, Lisa Matthews, Marija Orlic‐Milacic, Andrea Senff‐Ribeiro, Karen Rothfels, Veronica Shamovsky, Ralf Stephan, Cristoffer Sevilla, Thawfeek Varusai, Jean‐Marie Ravel, Rupsha Fraser, Vera Ortseifen, Silvia Marchesi, Piotr Gawron, Ewa Smula, Laurent Heirendt, Venkata Satagopam, Guanming Wu, Anders Riutta, Martin Golebiewski, Stuart Owen, Carole Goble, Xiaoming Hu, Rupert W Overall, Dieter Maier, Angela Bauch, Benjamin M Gyori, John A Bachman, Carlos Vega, Valentin Grouès, Miguel Vazquez, Pablo Porras, Luana Licata, Marta Iannuccelli, Francesca Sacco, Anastasia Nesterova, Anton Yuryev, Anita de Waard, Denes Turei, Augustin Luna, Ozgun Babur, Sylvain Soliman, Alberto Valdeolivas, Marina Esteban‐Medina, Maria Peña‐Chilet, Kinza Rian, Tomáš Helikar, Bhanwar Lal Puniya, Dezso Modos, Agatha Treveil, Marton Olbei, Bertrand De Meulder, Stephane Ballereau, Aurélien Dugourd, Aurélien Naldi, Vincent Noël, Laurence Calzone, Chris Sander, Emek Demir, Tamas Korcsmaros, Tom C Freeman, Franck Augé, Jacques S Beckmann, Jan Hasenauer, Olaf Wolkenhauer, Egon L Willighagen, Alexander R Pico, Chris T Evelo, Marc E Gillespie, Lincoln D Stein, Henning Hermjakob, Peter D'Eustachio, Julio Saez‐Rodriguez, Joaquin Dopazo, Alfonso Valencia, Hiroaki Kitano, Emmanuel Barillot, Charles Auffray, Rudi Balling, Reinhard Schneider, and the COVID‐19 Disease Map Community

Subjects
Biology (General), QH301-705.5, Medicine (General), R5-920
Published
2021
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6. COVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms

Author

Marek Ostaszewski, Anna Niarakis, Alexander Mazein, Inna Kuperstein, Robert Phair, Aurelio Orta‐Resendiz, Vidisha Singh, Sara Sadat Aghamiri, Marcio Luis Acencio, Enrico Glaab, Andreas Ruepp, Gisela Fobo, Corinna Montrone, Barbara Brauner, Goar Frishman, Luis Cristóbal Monraz Gómez, Julia Somers, Matti Hoch, Shailendra Kumar Gupta, Julia Scheel, Hanna Borlinghaus, Tobias Czauderna, Falk Schreiber, Arnau Montagud, Miguel Ponce de Leon, Akira Funahashi, Yusuke Hiki, Noriko Hiroi, Takahiro G Yamada, Andreas Dräger, Alina Renz, Muhammad Naveez, Zsolt Bocskei, Francesco Messina, Daniela Börnigen, Liam Fergusson, Marta Conti, Marius Rameil, Vanessa Nakonecnij, Jakob Vanhoefer, Leonard Schmiester, Muying Wang, Emily E Ackerman, Jason E Shoemaker, Jeremy Zucker, Kristie Oxford, Jeremy Teuton, Ebru Kocakaya, Gökçe Yağmur Summak, Kristina Hanspers, Martina Kutmon, Susan Coort, Lars Eijssen, Friederike Ehrhart, Devasahayam Arokia Balaya Rex, Denise Slenter, Marvin Martens, Nhung Pham, Robin Haw, Bijay Jassal, Lisa Matthews, Marija Orlic‐Milacic, Andrea Senff-Ribeiro, Karen Rothfels, Veronica Shamovsky, Ralf Stephan, Cristoffer Sevilla, Thawfeek Varusai, Jean‐Marie Ravel, Rupsha Fraser, Vera Ortseifen, Silvia Marchesi, Piotr Gawron, Ewa Smula, Laurent Heirendt, Venkata Satagopam, Guanming Wu, Anders Riutta, Martin Golebiewski, Stuart Owen, Carole Goble, Xiaoming Hu, Rupert W Overall, Dieter Maier, Angela Bauch, Benjamin M Gyori, John A Bachman, Carlos Vega, Valentin Grouès, Miguel Vazquez, Pablo Porras, Luana Licata, Marta Iannuccelli, Francesca Sacco, Anastasia Nesterova, Anton Yuryev, Anita de Waard, Denes Turei, Augustin Luna, Ozgun Babur, Sylvain Soliman, Alberto Valdeolivas, Marina Esteban‐Medina, Maria Peña‐Chilet, Kinza Rian, Tomáš Helikar, Bhanwar Lal Puniya, Dezso Modos, Agatha Treveil, Marton Olbei, Bertrand De Meulder, Stephane Ballereau, Aurélien Dugourd, Aurélien Naldi, Vincent Noël, Laurence Calzone, Chris Sander, Emek Demir, Tamas Korcsmaros, Tom C Freeman, Franck Augé, Jacques S Beckmann, Jan Hasenauer, Olaf Wolkenhauer, Egon L Willighagen, Alexander R Pico, Chris T Evelo, Marc E Gillespie, Lincoln D Stein, Henning Hermjakob, Peter D'Eustachio, Julio Saez‐Rodriguez, Joaquin Dopazo, Alfonso Valencia, Hiroaki Kitano, Emmanuel Barillot, Charles Auffray, Rudi Balling, Reinhard Schneider, and the COVID‐19 Disease Map Community

Subjects
computable knowledge repository, large‐scale biocuration, omics data analysis, open access community effort, systems biomedicine, Biology (General), QH301-705.5, Medicine (General), R5-920
Abstract

Abstract We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS‐CoV‐2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large‐scale community effort to build an open access, interoperable and computable repository of COVID‐19 molecular mechanisms. The COVID‐19 Disease Map (C19DMap) is a graphical, interactive representation of disease‐relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph‐based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS‐CoV‐2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID‐19 or similar pandemics in the long‐term perspective.

Published
2021
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