Patient dossier: healthcare queries over distributed resources

As with many other aspects of the modern world, in healthcare, the explosion of data and resources opens new opportunities for the development of added-value services. Still, a number of specific conditions on this domain greatly hinders these developments, including ethical and legal issues, fragmentation of the relevant data in different locations, and a level of (meta)data complexity that requires great expertise across technical, clinical, and biological domains. We propose the Patient Dossier paradigm as a way to organize new innovative healthcare services that sorts the current limitations. The Patient Dossier conceptual framework identifies the different issues and suggests how they can be tackled in a safe, efficient, and responsible way while opening options for independent development for different players in the healthcare sector. An initial implementation of the Patient Dossier concepts in the Rbbt framework is available as open-source at https://github.com/mikisvaz and https://github.com/Rbbt-Workflows.This work has received funding from the Elixir-Excelerate project, from the European Union's Horizon 2020 Research and Innovation Programme, under grant agreement N. 676559, and from Plataforma de Recursos Biomoleculares y Bioinformáticos PT13/0001/0030. Additional support came from the Lenovo - BSC Master Collaboration Agreement (2015) and from the IBM-BSC Deep Learning Centre (2016). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer ReviewedPostprint (published version

Patient dossier: healthcare queries over distributed resources

As with many other aspects of the modern world, in healthcare, the explosion of data and resources opens new opportunities for the development of added-value services. Still, a number of specific conditions on this domain greatly hinders these developments, including ethical and legal issues, fragmentation of the relevant data in different locations, and a level of (meta)data complexity that requires great expertise across technical, clinical, and biological domains. We propose the Patient Dossier paradigm as a way to organize new innovative healthcare services that sorts the current limitations. The Patient Dossier conceptual framework identifies the different issues and suggests how they can be tackled in a safe, efficient, and responsible way while opening options for independent development for different players in the healthcare sector. An initial implementation of the Patient Dossier concepts in the Rbbt framework is available as open-source at https://github.com/mikisvaz and https://github.com/Rbbt-Workflows.This work has received funding from the Elixir-Excelerate project, from the European Union's Horizon 2020 Research and Innovation Programme, under grant agreement N. 676559, and from Plataforma de Recursos Biomoleculares y Bioinformáticos PT13/0001/0030. Additional support came from the Lenovo - BSC Master Collaboration Agreement (2015) and from the IBM-BSC Deep Learning Centre (2016). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer ReviewedPostprint (published version

ExTRI: Extraction of transcription regulation interactions from literature

The regulation of gene transcription by transcription factors is a fundamental biological process, yet the relations between transcription factors (TF) and their target genes (TG) are still only sparsely covered in databases. Text-mining tools can offer broad and complementary solutions to help locate and extract mentions of these biological relationships in articles. We have generated ExTRI, a knowledge graph of TF-TG relationships, by applying a high recall text-mining pipeline to MedLine abstracts identifying over 100,000 candidate sentences with TF-TG relations. Validation procedures indicated that about half of the candidate sentences contain true TF-TG relationships. Post-processing identified 53,000 high confidence sentences containing TF-TG relationships, with a cross-validation F1-score close to 75%. The resulting collection of TF-TG relationships covers 80% of the relations annotated in existing databases. It adds 11,000 other potential interactions, including relationships for ~100 TFs currently not in public TF-TG relation databases. The high confidence abstract sentences contribute 25,000 literature references not available from other resources and offer a wealth of direct pointers to functional aspects of the TF-TG interactions. Our compiled resource encompassing ExTRI together with publicly available resources delivers literature-derived TF-TG interactions for more than 900 of the 1500–1600 proteins considered to function as specific DNA binding TFs. The obtained result can be used by curators, for network analysis and modelling, for causal reasoning or knowledge graph mining approaches, or serve to benchmark text mining strategies.We thank the participants of the COST Action GREEKC (CA15205) for fruitful discussions during workshops supported by COST (European Cooperation in Science and Technology).Peer ReviewedPostprint (published version

ChiPPI: a novel method for mapping chimeric protein–protein interactions uncovers selection principles of protein fusion events in cancer

Fusion proteins, comprising peptides deriving from the translation of two parental genes, are produced in cancer by chromosomal aberrations. The expressed fusion protein incorporates domains of both parental proteins. Using a methodology that treats discrete protein domains as binding sites for specific domains of interacting proteins, we have cataloged the protein interaction networks for 11 528 cancer fusions (ChiTaRS-3.1). Here, we present our novel method, chimeric protein–protein interactions (ChiPPI) that uses the domain–domain co-occurrence scores in order to identify preserved interactors of chimeric proteins. Mapping the influence of fusion proteins on cell metabolism and pathways reveals that ChiPPI networks often lose tumor suppressor proteins and gain oncoproteins. Furthermore, fusions often induce novel connections between non-interactors skewing interaction networks and signaling pathways. We compared fusion protein PPI networks in leukemia/lymphoma, sarcoma and solid tumors finding distinct enrichment patterns for each disease type. While certain pathways are enriched in all three diseases (Wnt, Notch and TGF β), there are distinct patterns for leukemia (EGFR signaling, DNA replication and CCKR signaling), for sarcoma (p53 pathway and CCKR signaling) and solid tumors (FGFR and EGFR signaling). Thus, the ChiPPI method represents a comprehensive tool for studying the anomaly of skewed cellular networks produced by fusion proteins in cancer.This work is funded by the Project Retos BFU2015-71241-R of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC), co-funded by European Regional Development Fund (ERDF) and by the Project PT13/0001/0030, Instituto de Salud Carlos III (ISCIII), Strategic Action in Health, co-funded by European Regional Development Fund (ERDF). The work of MFM is supported by the Israel Cancer Association (ICA) fund, the work of ST is supported by the VaTaT Postdoctoral Fellowship for excellent students [22351, 20027, 26912]. AV is supported by the Joint BSC-CRG-IRB Programme in Computational Biology. Funding for open access charge: ICA [e-cancer-diagnosis].Peer ReviewedPostprint (published version

Sustainable acetylation of biopolymers mediated by a naturally occurring ahydroxy acid

La organocatálisis es una rama de la catálisis que utiliza moléculas orgánicas de bajo peso molecular como catalizadores, entre ellos, los ácidos a-hidroxicarboxílicos. En la presente contribución se demuestra la aplicabilidad de la ruta catalizada por un ácido a-hidroxicarboxílico de origen natural y de producción industrial nacional como es el ácido L-(+)-tartárico para la acetilación no convencional de dos biopolímeros: almidón y celulosa bacterial. Algunas ventajas inherentes a la metodología de acetilación propuesta son su sencillez, el uso de un catalizador orgánico no metálico de origen natural, no tóxico y biodegradable; y la operación con alta eficiencia en ausencia de solventes y bajo condiciones moderadas de reacción. En el caso del almidón, la manipulación de las condiciones de reacción permitió obtener almidones acetilados con valores de grado de sustitución (GS) en el rango de 0.06 a 2.93 en 3 horas de reacción, con aplicación potencial en la industria de alimentos, medicina y plásticos. En el caso de la celulosa bacterial (BC), la acetilación organocatalítica permitió alcanzar valores de GS en el rango de 0.35-0.60, de utilidad en la regulación de la polaridad de las nanofibras de celulosa para su potencial compatibilización con matrices/medios no polares. Los productos de la acetilación fueron caracterizados en términos de estructura química y cristalinidadOrganocatalysis refers to a form of catalysis which uses small organic molecules as catalysts, among which a-hydroxy acids are found. In the current contribution the feasibility of a route for the non-conventional acetylation of starch and cellulose catalyzed by a naturally occurring a-hydroxy acid such as L-(+)-tartaric acid is demonstrated. Some inner advantages of the acetylation methodology proposed are its simplicity, the use of a naturally occurring non-toxic and biodegradable organic catalyst, and the operation with high efficiency in absence of solvents and under moderate reaction conditions. In the case of starch, the proper manipulation of reaction conditions allowed obtaining acetylated starches with substitution degree values in the 0.06-2.93 within 3 hours of reaction, with potential use in the food industry, medicine and plastics. In reference to bacterial cellulose, the organocatalytic acetylation proposed allowed reaching GS values in the 0.35-0.60 range, useful for the modulation of microfibrils polarity and compatibilization with non polar media/matrices. Acetylation products were characterized in terms of chemical structure and crystallinity.Fil: Foresti, María Laura. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Houssay. Instituto de Tecnologia En Polimeros y Nanotecnologia; ArgentinaFil: Tupa Valencia, Maribel Victoria. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Houssay. Instituto de Tecnologia En Polimeros y Nanotecnologia; ArgentinaFil: Avila Ramirez, Jhon Alejandro. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Houssay. Instituto de Tecnologia En Polimeros y Nanotecnologia; ArgentinaFil: Cerrutti, Patricia. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Houssay. Instituto de Tecnologia En Polimeros y Nanotecnologia; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Vazquez, Analia. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Houssay. Instituto de Tecnologia En Polimeros y Nanotecnologia; Argentin

KinMutRF: a random forest classifier of sequence variants in the human protein kinase superfamily

Background: The association between aberrant signal processing by protein kinases and human diseases such as cancer was established long time ago. However, understanding the link between sequence variants in the protein kinase superfamily and the mechanistic complex traits at the molecular level remains challenging: cells tolerate most genomic alterations and only a minor fraction disrupt molecular function sufficiently and drive disease. Results: KinMutRF is a novel random-forest method to automatically identify pathogenic variants in human kinases. Twenty six decision trees implemented as a random forest ponder a battery of features that characterize the variants: a) at the gene level, including membership to a Kinbase group and Gene Ontology terms; b) at the PFAM domain level; and c) at the residue level, the types of amino acids involved, changes in biochemical properties, functional annotations from UniProt, Phospho.ELM and FireDB. KinMutRF identifies disease-associated variants satisfactorily (Acc: 0.88, Prec:0.82, Rec:0.75, F-score:0.78, MCC:0.68) when trained and cross-validated with the 3689 human kinase variants from UniProt that have been annotated as neutral or pathogenic. All unclassified variants were excluded from the training set. Furthermore, KinMutRF is discussed with respect to two independent kinase-specific sets of mutations no included in the training and testing, Kin-Driver (643 variants) and Pon-BTK (1495 variants). Moreover, we provide predictions for the 848 protein kinase variants in UniProt that remained unclassified. A public implementation of KinMutRF, including documentation and examples, is available online (http://kinmut2.bioinfo.cnio.es). The source code for local installation is released under a GPL version 3 license, and can be downloaded from https://github.com/Rbbt-Workflows/KinMut2. Conclusions: KinMutRF is capable of classifying kinase variation with good performance. Predictions by KinMutRF compare favorably in a benchmark with other state-of-the-art methods (i.e. SIFT, Polyphen-2, MutationAssesor, MutationTaster, LRT, CADD, FATHMM, and VEST). Kinase-specific features rank as the most elucidatory in terms of information gain and are likely the improvement in prediction performance. This advocates for the development of family-specific classifiers able to exploit the discriminatory power of features unique to individual protein families

Characterization of Knots and Links Arising From Site-specific Recombination on Twist Knots

We develop a model characterizing all possible knots and links arising from recombination starting with a twist knot substrate, extending previous work of Buck and Flapan. We show that all knot or link products fall into three well-understood families of knots and links, and prove that given a positive integer $n$, the number of product knots and links with minimal crossing number equal to $n$ grows proportionally to $n^5$. In the (common) case of twist knot substrates whose products have minimal crossing number one more than the substrate, we prove that the types of products are tightly prescribed. Finally, we give two simple examples to illustrate how this model can help determine previously uncharacterized experimental data.Comment: 32 pages, 7 tables, 27 figures, revised: figures re-arranged, and minor corrections. To appear in Journal of Physics

Plasmacytoid myoepithelioma of the palate : report of one case and review of the literature

Introduction: Myoepithelioma is a benign neoplasm of salivary glands, represents 1.5 % of all salivary glands neoplasm. The plasmacytoid myoepithelioma from palate salivary glands is considered as a rare entity, at date it has been reported 14 cases. It is present one case of plasmacytoid myoepithelioma of palate. Case report: A Hispanic female of 28 years old presented a not-ulcerate, painless ovoid swelling at left side of hard palate with a one year and a half of evolution. An excisional biopsy was done. The sample was fixed at 10% buffer formalin, embedded in paraffin, cuts at 5 µ and stained with H-E. Microscopically, the lesion was composed by myoepithelial neoplastic cells characterized by a round ovoid silhouette, an eccentric nuclei of dense chromatin and eosinophilic cytoplasm. In some myoepithelial neoplastic cells were identifies intranuclear cytoplasmatic inclusions. The lesion was analysed with immunohistochemical technique using the follow antibodies: vimentin, citokeratin AE1/AE3, S100 protein and actin muscle specific. We observe positive immunoreactivity against vimentin, citokeratin, S100 protein and actin muscle specific. A diagnosis of plasmacytoid myoephitelioma of palate salivary glands was done. Our findings supports the suggestion about plasmacytoid myoepithelioma is an independent entity. The histological diagnostic parameters of plasmacytoid myoepithelioma versus pleomorphic adenoma are discussed

Evaluation of chemical and gene/protein entity recognition systems at BioCreative V.5: the CEMP and GPRO patents tracks

This paper presents the results of the BioCreative V.5 offline tasks related to the evaluation of the performance as well as assess progress made by strategies used for the automatic recognition of mentions of chemical names and gene in running text of medicinal chemistry patent abstracts. A total of 21 teams submitted results for at least one of these tasks. The CEMP (chemical entity mention in patents) task entailed the detection of chemical named entity mentions. A total of 14 teams submitted 56 runs. The top performing team reached an F-score of 0.90 with a precision of 0.88 and a recall of 0.93. The GPRO (gene and protein related object) task focused on the detection of mentions of gene and protein related objects. The 7 participating teams (30 runs) had to detect gene/protein mentions that could be linked to at least one biological database, such as SwissProt or EntrezGene. The best F-score, recall and precision in this task were of 0.79, 0.83 and 0.77, respectively. The CEMP and GPRO gold standard corpora included training sets of 21,000 records and test sets of 9,000 records. Similar to the previous BioCreative CHEMDNER tasks, evaluation was based on micro-averaged F-score. The BeCalm platform supported prediction submission and evaluation (http://www.becalm.eu).We acknowledge the OpenMinted (654021) and the ELIXIREXCELERATE (676559) H2020 projects, and the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology for funding. The Spanish National Bioinformatics Institute (INB) unit at the Spanish National Cancer Research Centre (CNIO) is a member of the INB, PRB2-ISCIII and is supported by grant PT13/0001/0030, of the PE I+D+i 2013-2016, funded by ISCIII and ERDF.info:eu-repo/semantics/publishedVersio