Institution
Pompeu Fabra University
Education•Barcelona, Spain•
About: Pompeu Fabra University is a education organization based out in Barcelona, Spain. It is known for research contribution in the topics: Population & Gene. The organization has 8093 authors who have published 23570 publications receiving 858431 citations. The organization is also known as: Universitat Pompeu Fabra & UPF.
Topics: Population, Gene, European union, Genome, Context (language use)
Papers published on a yearly basis
Papers
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TL;DR: The nf-core framework is introduced as a means for the development of collaborative, peerreviewed, best-practice analysis pipelines that can be used across all institutions and research facilities and introduces a higher degree of portability as compared to custom in-house scripts.
Abstract: To the Editor — The standardization, portability and reproducibility of analysis pipelines are key issues within the bioinformatics community. Most bioinformatics pipelines are designed for use on-premises; as a result, the associated software dependencies and execution logic are likely to be tightly coupled with proprietary computing environments. This can make it difficult or even impossible for others to reproduce the ensuing results, which is a fundamental requirement for the validation of scientific findings. Here, we introduce the nf-core framework as a means for the development of collaborative, peerreviewed, best-practice analysis pipelines (Fig. 1). All nf-core pipelines are written in Nextflow and so inherit the ability to be executed on most computational infrastructures, as well as having native support for container technologies such as Docker and Singularity. The nf-core community (Supplementary Fig. 1) has developed a suite of tools that automate pipeline creation, testing, deployment and synchronization. Our goal is to provide a framework for high-quality bioinformatics pipelines that can be used across all institutions and research facilities. Being able to reproduce scientific results is the central tenet of the scientific method. However, moving toward FAIR (findable, accessible, interoperable and reusable) research methods1 in data-driven science is complex2,3. Central repositories, such as bio. tools4, omictools5 and the Galaxy toolshed6, make it possible to find existing pipelines and their associated tools. However, it is still notoriously challenging to develop analysis pipelines that are fully reproducible and interoperable across multiple systems and institutions — primarily because of differences in hardware, operating systems and software versions. Although the recommended guidelines for some analysis pipelines have become standardized (for example, GATK best practices7), the actual implementations are usually developed on a case-by-case basis. As such, there is often little incentive to test, document and implement pipelines in a way that permits their reuse by other researchers. This can hamper sustainable sharing of data and tools, and results in a proliferation of heterogeneous analysis pipelines, making it difficult for newcomers to find what they need to address a specific analysis question. As the scale of -omics data and their associated analytical tools has grown, the scientific community is increasingly moving toward the use of specialized workflow management systems to build analysis pipelines8. They separate the requirements of the underlying compute infrastructure from the analysis and workflow description, introducing a higher degree of portability as compared to custom in-house scripts. One such popular tool is Nextflow9. Using Nextflow, software packages can be bundled with analysis pipelines using built-in integration for package managers, such as Conda, and containerization platforms, such as Docker and Singularity. Moreover, support for most common highperformance-computing batch schedulers and cloud providers allows simple deployment of analysis pipelines on almost any infrastructure. The opportunity to run pipelines locally during initial development and then to proceed seamlessly to largescale computational resources in highperformance-computing or cloud settings provides users and developers with great flexibility. The nf-core community project collects a curated set of best-practice analysis pipelines built using Nextflow. Similar projects Participate
663 citations
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TL;DR: The muscle provides a useful model for the regulation of tissue repair by the local microenvironment, showing interplay among muscle-specific stem cells, inflammatory cells, fibroblasts and extracellular matrix components of the mammalian wound-healing response.
Abstract: The repair process of damaged tissue involves the coordinated activities of several cell types in response to local and systemic signals. Following acute tissue injury, infiltrating inflammatory cells and resident stem cells orchestrate their activities to restore tissue homeostasis. However, during chronic tissue damage, such as in muscular dystrophies, the inflammatory-cell infiltration and fibroblast activation persists, while the reparative capacity of stem cells (satellite cells) is attenuated. Abnormal dystrophic muscle repair and its end stage, fibrosis, represent the final common pathway of virtually all chronic neurodegenerative muscular diseases. As our understanding of the pathogenesis of muscle fibrosis has progressed, it has become evident that the muscle provides a useful model for the regulation of tissue repair by the local microenvironment, showing interplay among muscle-specific stem cells, inflammatory cells, fibroblasts and extracellular matrix components of the mammalian wound-healing response. This article reviews the emerging findings of the mechanisms that underlie normal versus aberrant muscle-tissue repair.
661 citations
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TL;DR: Nitric oxide is a well-known vasorelaxant agent, but it works as a neurotransmitter when produced by neurons and is also involved in defense functions when it is produced by immune and glial cells.
659 citations
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Alvaro N. Barbeira1, Scott P. Dickinson1, Rodrigo Bonazzola1, Jiamao Zheng1 +260 more•Institutions (43)
TL;DR: A mathematical expression is derived to compute PrediXcan results using summary data, and the effects of gene expression variation on human phenotypes in 44 GTEx tissues and >100 phenotypes are investigated.
Abstract: Scalable, integrative methods to understand mechanisms that link genetic variants with phenotypes are needed. Here we derive a mathematical expression to compute PrediXcan (a gene mapping approach) results using summary data (S-PrediXcan) and show its accuracy and general robustness to misspecified reference sets. We apply this framework to 44 GTEx tissues and 100+ phenotypes from GWAS and meta-analysis studies, creating a growing public catalog of associations that seeks to capture the effects of gene expression variation on human phenotypes. Replication in an independent cohort is shown. Most of the associations are tissue specific, suggesting context specificity of the trait etiology. Colocalized significant associations in unexpected tissues underscore the need for an agnostic scanning of multiple contexts to improve our ability to detect causal regulatory mechanisms. Monogenic disease genes are enriched among significant associations for related traits, suggesting that smaller alterations of these genes may cause a spectrum of milder phenotypes.
657 citations
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TL;DR: The authors analyzes the role that different indices and dimensions of ethnicity play in the process of economic development and finds that ethnic polarization has a large and negative effect on economic development through the reduction of investment and the increase of government consumption and the probability of civil conflict.
654 citations
Authors
Showing all 8248 results
Name | H-index | Papers | Citations |
---|---|---|---|
Andrei Shleifer | 171 | 514 | 271880 |
Paul Elliott | 153 | 773 | 103839 |
Bert Brunekreef | 124 | 806 | 81938 |
Philippe Aghion | 122 | 507 | 73438 |
Anjana Rao | 118 | 337 | 61395 |
Jordi Sunyer | 115 | 798 | 57211 |
Kenneth J. Arrow | 113 | 411 | 111221 |
Xavier Estivill | 110 | 673 | 59568 |
Roderic Guigó | 108 | 304 | 106914 |
Mark J. Nieuwenhuijsen | 107 | 647 | 49080 |
Jordi Alonso | 107 | 523 | 64058 |
Alfonso Valencia | 106 | 542 | 55192 |
Luis Serrano | 105 | 452 | 42515 |
Vadim N. Gladyshev | 102 | 490 | 34148 |
Josep M. Antó | 100 | 493 | 38663 |