@article {726, title = {A comprehensive database for integrated analysis of omics data in autoimmune diseases.}, journal = {BMC Bioinformatics}, volume = {22}, year = {2021}, month = {2021 Jun 24}, pages = {343}, abstract = {

BACKGROUND: Autoimmune diseases are heterogeneous pathologies with difficult diagnosis and few therapeutic options. In the last decade, several omics studies have provided significant insights into the molecular mechanisms of these diseases. Nevertheless, data from different cohorts and pathologies are stored independently in public repositories and a unified resource is imperative to assist researchers in this field.

RESULTS: Here, we present Autoimmune Diseases Explorer ( https://adex.genyo.es ), a database that integrates 82 curated transcriptomics and methylation studies covering 5609 samples for some of the most common autoimmune diseases. The database provides, in an easy-to-use environment, advanced data analysis and statistical methods for exploring omics datasets, including meta-analysis, differential expression or pathway analysis.

CONCLUSIONS: This is the first omics database focused on autoimmune diseases. This resource incorporates homogeneously processed data to facilitate integrative analyses among studies.

}, keywords = {Autoimmune Diseases, Computational Biology, Databases, Factual, Humans}, issn = {1471-2105}, doi = {10.1186/s12859-021-04268-4}, author = {Martorell-Marug{\'a}n, Jordi and L{\'o}pez-Dom{\'\i}nguez, Ra{\'u}l and Garc{\'\i}a-Moreno, Adri{\'a}n and Toro-Dom{\'\i}nguez, Daniel and Villatoro-Garc{\'\i}a, Juan Antonio and Barturen, Guillermo and Mart{\'\i}n-G{\'o}mez, Adoraci{\'o}n and Troule, Kevin and G{\'o}mez-L{\'o}pez, Gonzalo and Al-Shahrour, F{\'a}tima and Gonz{\'a}lez-Rumayor, V{\'\i}ctor and Pe{\~n}a-Chilet, Maria and Dopazo, Joaquin and Saez-Rodriguez, Julio and Alarc{\'o}n-Riquelme, Marta E and Carmona-S{\'a}ez, Pedro} } @article {736, title = {COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.}, journal = {Mol Syst Biol}, volume = {17}, year = {2021}, month = {2021 10}, pages = {e10387}, 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.

}, keywords = {Antiviral Agents, Computational Biology, Computer Graphics, COVID-19, Cytokines, Data Mining, Databases, Factual, Gene Expression Regulation, Host Microbial Interactions, Humans, Immunity, Cellular, Immunity, Humoral, Immunity, Innate, Lymphocytes, Metabolic Networks and Pathways, Myeloid Cells, Protein Interaction Mapping, SARS-CoV-2, Signal Transduction, Software, Transcription Factors, Viral Proteins}, issn = {1744-4292}, doi = {10.15252/msb.202110387}, author = {Ostaszewski, Marek and Niarakis, Anna and Mazein, Alexander and Kuperstein, Inna and Phair, Robert and Orta-Resendiz, Aurelio and Singh, Vidisha and Aghamiri, Sara Sadat and Acencio, Marcio Luis and Glaab, Enrico and Ruepp, Andreas and Fobo, Gisela and Montrone, Corinna and Brauner, Barbara and Frishman, Goar and Monraz G{\'o}mez, Luis Crist{\'o}bal and Somers, Julia and Hoch, Matti and Kumar Gupta, Shailendra and Scheel, Julia and Borlinghaus, Hanna and Czauderna, Tobias and Schreiber, Falk and Montagud, Arnau and Ponce de Leon, Miguel and Funahashi, Akira and Hiki, Yusuke and Hiroi, Noriko and Yamada, Takahiro G and Dr{\"a}ger, Andreas and Renz, Alina and Naveez, Muhammad and Bocskei, Zsolt and Messina, Francesco and B{\"o}rnigen, Daniela and Fergusson, Liam and Conti, Marta and Rameil, Marius and Nakonecnij, Vanessa and Vanhoefer, Jakob and Schmiester, Leonard and Wang, Muying and Ackerman, Emily E and Shoemaker, Jason E and Zucker, Jeremy and Oxford, Kristie and Teuton, Jeremy and Kocakaya, Ebru and Summak, G{\"o}k{\c c}e Ya{\u g}mur and Hanspers, Kristina and Kutmon, Martina and Coort, Susan and Eijssen, Lars and Ehrhart, Friederike and Rex, Devasahayam Arokia Balaya and Slenter, Denise and Martens, Marvin and Pham, Nhung and Haw, Robin and Jassal, Bijay and Matthews, Lisa and Orlic-Milacic, Marija and Senff Ribeiro, Andrea and Rothfels, Karen and Shamovsky, Veronica and Stephan, Ralf and Sevilla, Cristoffer and Varusai, Thawfeek and Ravel, Jean-Marie and Fraser, Rupsha and Ortseifen, Vera and Marchesi, Silvia and Gawron, Piotr and Smula, Ewa and Heirendt, Laurent and Satagopam, Venkata and Wu, Guanming and Riutta, Anders and Golebiewski, Martin and Owen, Stuart and Goble, Carole and Hu, Xiaoming and Overall, Rupert W and Maier, Dieter and Bauch, Angela and Gyori, Benjamin M and Bachman, John A and Vega, Carlos and Grou{\`e}s, Valentin and Vazquez, Miguel and Porras, Pablo and Licata, Luana and Iannuccelli, Marta and Sacco, Francesca and Nesterova, Anastasia and Yuryev, Anton and de Waard, Anita and Turei, Denes and Luna, Augustin and Babur, Ozgun and Soliman, Sylvain and Valdeolivas, Alberto and Esteban-Medina, Marina and Pe{\~n}a-Chilet, Maria and Rian, Kinza and Helikar, Tom{\'a}{\v s} and Puniya, Bhanwar Lal and Modos, Dezso and Treveil, Agatha and Olbei, Marton and De Meulder, Bertrand and Ballereau, Stephane and Dugourd, Aur{\'e}lien and Naldi, Aur{\'e}lien and No{\"e}l, Vincent and Calzone, Laurence and Sander, Chris and Demir, Emek and Korcsmaros, Tamas and Freeman, Tom C and Aug{\'e}, Franck and Beckmann, Jacques S and Hasenauer, Jan and Wolkenhauer, Olaf and Wilighagen, Egon L and Pico, Alexander R and Evelo, Chris T and Gillespie, Marc E and Stein, Lincoln D and Hermjakob, Henning and D{\textquoteright}Eustachio, Peter and Saez-Rodriguez, Julio and Dopazo, Joaquin and Valencia, Alfonso and Kitano, Hiroaki and Barillot, Emmanuel and Auffray, Charles and Balling, Rudi and Schneider, Reinhard} } @article {712, title = {A versatile workflow to integrate RNA-seq genomic and transcriptomic data into mechanistic models of signaling pathways.}, journal = {PLoS Comput Biol}, volume = {17}, year = {2021}, month = {2021 02}, pages = {e1008748}, abstract = {

MIGNON is a workflow for the analysis of RNA-Seq experiments, which not only efficiently manages the estimation of gene expression levels from raw sequencing reads, but also calls genomic variants present in the transcripts analyzed. Moreover, this is the first workflow that provides a framework for the integration of transcriptomic and genomic data based on a mechanistic model of signaling pathway activities that allows a detailed biological interpretation of the results, including a comprehensive functional profiling of cell activity. MIGNON covers the whole process, from reads to signaling circuit activity estimations, using state-of-the-art tools, it is easy to use and it is deployable in different computational environments, allowing an optimized use of the resources available.

}, keywords = {Algorithms, Cell Line, Tumor, Computational Biology, Databases, Factual, Gene Expression Profiling, Genomics, High-Throughput Nucleotide Sequencing, Humans, Models, Theoretical, mutation, RNA-seq, Signal Transduction, Software, Transcriptome, whole exome sequencing, Workflow}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1008748}, author = {Garrido-Rodriguez, Mart{\'\i}n and L{\'o}pez-L{\'o}pez, Daniel and Ortuno, Francisco M and Pe{\~n}a-Chilet, Maria and Mu{\~n}oz, Eduardo and Calzado, Marco A and Dopazo, Joaquin} } @article {689, title = {COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms.}, journal = {Sci Data}, volume = {7}, year = {2020}, month = {2020 05 05}, pages = {136}, keywords = {Betacoronavirus, Computational Biology, Coronavirus Infections, COVID-19, Databases, Factual, Host Microbial Interactions, Host-Pathogen Interactions, Humans, International Cooperation, Models, Biological, Pandemics, Pneumonia, Viral, SARS-CoV-2}, issn = {2052-4463}, doi = {10.1038/s41597-020-0477-8}, author = {Ostaszewski, Marek and Mazein, Alexander and Gillespie, Marc E and Kuperstein, Inna and Niarakis, Anna and Hermjakob, Henning and Pico, Alexander R and Willighagen, Egon L and Evelo, Chris T and Hasenauer, Jan and Schreiber, Falk and Dr{\"a}ger, Andreas and Demir, Emek and Wolkenhauer, Olaf and Furlong, Laura I and Barillot, Emmanuel and Dopazo, Joaquin and Orta-Resendiz, Aurelio and Messina, Francesco and Valencia, Alfonso and Funahashi, Akira and Kitano, Hiroaki and Auffray, Charles and Balling, Rudi and Schneider, Reinhard} } @article {610, title = {Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models.}, journal = {BMC Bioinformatics}, volume = {20}, year = {2019}, month = {2019 Jul 02}, pages = {370}, abstract = {

BACKGROUND: In spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance between samples and candidate genes. And this is especially dramatic in scenarios in which the availability of samples is difficult, such as the case of rare diseases.

RESULTS: The application of multi-output regression machine learning methodologies to predict the potential effect of external proteins over the signaling circuits that trigger Fanconi anemia related cell functionalities, inferred with a mechanistic model, allowed us to detect over 20 potential therapeutic targets.

CONCLUSIONS: The use of artificial intelligence methods for the prediction of potentially causal relationships between proteins of interest and cell activities related with disease-related phenotypes opens promising avenues for the systematic search of new targets in rare diseases.

}, keywords = {Databases, Factual, Fanconi Anemia, Genomics, Humans, Machine Learning, Phenotype, Proteins, Signal Transduction}, issn = {1471-2105}, doi = {10.1186/s12859-019-2969-0}, author = {Esteban-Medina, Marina and Pe{\~n}a-Chilet, Maria and Loucera, Carlos and Dopazo, Joaquin} } @article {555, title = {PyCellBase, an efficient python package for easy retrieval of biological data from heterogeneous sources.}, journal = {BMC Bioinformatics}, volume = {20}, year = {2019}, month = {2019 Mar 28}, pages = {159}, abstract = {

BACKGROUND: Biological databases and repositories are incrementing in diversity and complexity over the years. This rapid expansion of current and new sources of biological knowledge raises serious problems of data accessibility and integration. To handle the growing necessity of unification, CellBase was created as an integrative solution. CellBase provides a centralized NoSQL database containing biological information from different and heterogeneous sources. Access to this information is done through a RESTful web service API, which provides an efficient interface to the data.

RESULTS: In this work we present PyCellBase, a Python package that provides programmatic access to the rich RESTful web service API offered by CellBase. This package offers a fast and user-friendly access to biological information without the need of installing any local database. In addition, a series of command-line tools are provided to perform common bioinformatic tasks, such as variant annotation. CellBase data is always available by a high-availability cluster and queries have been tuned to ensure a real-time performance.

CONCLUSION: PyCellBase is an open-source Python package that provides an efficient access to heterogeneous biological information. It allows to perform tasks that require a comprehensive set of knowledge resources, as for example variant annotation. Queries can be easily fine-tuned to retrieve the desired information of particular biological features. PyCellBase offers the convenience of an object-oriented scripting language and provides the ability to integrate the obtained results into other Python applications and pipelines.

}, keywords = {Computational Biology, Databases, Factual, Software, User-Computer Interface}, issn = {1471-2105}, doi = {10.1186/s12859-019-2726-4}, author = {Perez-Gil, Daniel and Lopez, Francisco J and Dopazo, Joaquin and Marin-Garcia, Pablo and Rendon, Augusto and Medina, Ignacio} } @article {595, title = {Interoperability with Moby 1.0--it{\textquoteright}s better than sharing your toothbrush!}, journal = {Brief Bioinform}, volume = {9}, year = {2008}, month = {2008 May}, pages = {220-31}, abstract = {

The BioMoby project was initiated in 2001 from within the model organism database community. It aimed to standardize methodologies to facilitate information exchange and access to analytical resources, using a consensus driven approach. Six years later, the BioMoby development community is pleased to announce the release of the 1.0 version of the interoperability framework, registry Application Programming Interface and supporting Perl and Java code-bases. Together, these provide interoperable access to over 1400 bioinformatics resources worldwide through the BioMoby platform, and this number continues to grow. Here we highlight and discuss the features of BioMoby that make it distinct from other Semantic Web Service and interoperability initiatives, and that have been instrumental to its deployment and use by a wide community of bioinformatics service providers. The standard, client software, and supporting code libraries are all freely available at http://www.biomoby.org/.

}, keywords = {Computational Biology, Database Management Systems, Databases, Factual, Information Storage and Retrieval, Internet, Programming Languages, Systems Integration}, issn = {1477-4054}, doi = {10.1093/bib/bbn003}, author = {Wilkinson, Mark D and Senger, Martin and Kawas, Edward and Bruskiewich, Richard and Gouzy, Jerome and Noirot, Celine and Bardou, Philippe and Ng, Ambrose and Haase, Dirk and Saiz, Enrique de Andres and Wang, Dennis and Gibbons, Frank and Gordon, Paul M K and Sensen, Christoph W and Carrasco, Jose Manuel Rodriguez and Fern{\'a}ndez, Jos{\'e} M and Shen, Lixin and Links, Matthew and Ng, Michael and Opushneva, Nina and Neerincx, Pieter B T and Leunissen, Jack A M and Ernst, Rebecca and Twigger, Simon and Usadel, Bjorn and Good, Benjamin and Wong, Yan and Stein, Lincoln and Crosby, William and Karlsson, Johan and Royo, Romina and P{\'a}rraga, Iv{\'a}n and Ram{\'\i}rez, Sergio and Gelpi, Josep Lluis and Trelles, Oswaldo and Pisano, David G and Jimenez, Natalia and Kerhornou, Arnaud and Rosset, Roman and Zamacola, Leire and T{\'a}rraga, Joaqu{\'\i}n and Huerta-Cepas, Jaime and Carazo, Jose Mar{\'\i}a and Dopazo, Joaquin and Guig{\'o}, Roderic and Navarro, Arcadi and Orozco, Modesto and Valencia, Alfonso and Claros, M Gonzalo and P{\'e}rez, Antonio J and Aldana, Jose and Rojano, M Mar and Fernandez-Santa Cruz, Raul and Navas, Ismael and Schiltz, Gary and Farmer, Andrew and Gessler, Damian and Schoof, Heiko and Groscurth, Andreas} }