OmniReprodubileCellAnalysis: a comprehensive toolbox for the analysis of cellular biology data
Open science and reproducibility are two key pillars of modern scientific research. Open science is making scientific research and data accessible and transparent to the broader scientific community and the public. Reproducibility, on the other hand, is the ability to replicate and confirm research...
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ftuniparissaclay:oai:HAL:hal-04328085v1 2024-09-15T18:28:48+00:00 OmniReprodubileCellAnalysis: a comprehensive toolbox for the analysis of cellular biology data Dora, Tortarolo Simone, Pernice Clapero, Fabiana Valdembri, Donatella Serini, Guido Riccardo, Federica Tarone, Lidia Bena, Chiara Enrico Bosia, Carla Contaldo, Sandro Gepiro Marco, Beccuti Pennisi, Marzio Francesca, Cordero MICrobiologie de l'ALImentation au Service de la Santé (MICALIS) AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) Italian Institute for Genomic Medicine (IIGM) 2023-12-06 https://hal.science/hal-04328085 https://doi.org/10.1101/2023.11.07.565961 en eng HAL CCSD info:eu-repo/semantics/altIdentifier/doi/10.1101/2023.11.07.565961 hal-04328085 https://hal.science/hal-04328085 BIORXIV: 2023.11.07.565961 doi:10.1101/2023.11.07.565961 https://hal.science/hal-04328085 2023 [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM] [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM] info:eu-repo/semantics/preprint Preprints, Working Papers, . 2023 ftuniparissaclay https://doi.org/10.1101/2023.11.07.565961 2024-08-30T01:48:48Z Open science and reproducibility are two key pillars of modern scientific research. Open science is making scientific research and data accessible and transparent to the broader scientific community and the public. Reproducibility, on the other hand, is the ability to replicate and confirm research results by following the same methods and procedures. Reproducibility is thus crucial because it ensures the reliability and validity of scientific findings. The relationship between open science and reproducibility is intertwined; indeed open science practices, such as sharing raw data, detailed methodologies, and code, greatly facilitate the reproducibility of research. In recent years, concerns about the reproducibility of scientific research have gained prominence, and indeed scientists still lament the lack of details in the methods sections of published papers and the unavailability of raw data from the authors. To assist cellular biologists and immunologists and to promote a more transparent, open and reproducible research practice, we developed OmniReproducibleCellAnalysis ( ORCA ), a new Shiny Application based in R, for the semi-automated analysis of Western Blot (WB), Reverse Transcription-quantitative PCR (RT-qPCR), Enzyme-Linked ImmunoSorbent Assay (ELISA), Endocytosis and Cytotoxicity experiments. ORCA is open-source and approachable by scientists without advanced R language knowledge. Our application automatically compiles a report containing the finalized data analysis and all its preliminary and intermediate steps, ensuring data analysis standardization and reproducibility. Furthermore, ORCA allows to upload raw data and results directly on the data repository Harvard Dataverse, a valuable tool for promoting transparency and data accessibility in scientific research. By employing ORCA , scientists will cut down analysis time and human-dependent errors, while taking a step towards a research practice compliant with Open Science and FAIR principle. Report Orca Archives ouvertes de Paris-Saclay |
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Archives ouvertes de Paris-Saclay |
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ftuniparissaclay |
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English |
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[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM] [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM] |
spellingShingle |
[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM] [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM] Dora, Tortarolo Simone, Pernice Clapero, Fabiana Valdembri, Donatella Serini, Guido Riccardo, Federica Tarone, Lidia Bena, Chiara Enrico Bosia, Carla Contaldo, Sandro Gepiro Marco, Beccuti Pennisi, Marzio Francesca, Cordero OmniReprodubileCellAnalysis: a comprehensive toolbox for the analysis of cellular biology data |
topic_facet |
[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM] [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM] |
description |
Open science and reproducibility are two key pillars of modern scientific research. Open science is making scientific research and data accessible and transparent to the broader scientific community and the public. Reproducibility, on the other hand, is the ability to replicate and confirm research results by following the same methods and procedures. Reproducibility is thus crucial because it ensures the reliability and validity of scientific findings. The relationship between open science and reproducibility is intertwined; indeed open science practices, such as sharing raw data, detailed methodologies, and code, greatly facilitate the reproducibility of research. In recent years, concerns about the reproducibility of scientific research have gained prominence, and indeed scientists still lament the lack of details in the methods sections of published papers and the unavailability of raw data from the authors. To assist cellular biologists and immunologists and to promote a more transparent, open and reproducible research practice, we developed OmniReproducibleCellAnalysis ( ORCA ), a new Shiny Application based in R, for the semi-automated analysis of Western Blot (WB), Reverse Transcription-quantitative PCR (RT-qPCR), Enzyme-Linked ImmunoSorbent Assay (ELISA), Endocytosis and Cytotoxicity experiments. ORCA is open-source and approachable by scientists without advanced R language knowledge. Our application automatically compiles a report containing the finalized data analysis and all its preliminary and intermediate steps, ensuring data analysis standardization and reproducibility. Furthermore, ORCA allows to upload raw data and results directly on the data repository Harvard Dataverse, a valuable tool for promoting transparency and data accessibility in scientific research. By employing ORCA , scientists will cut down analysis time and human-dependent errors, while taking a step towards a research practice compliant with Open Science and FAIR principle. |
author2 |
MICrobiologie de l'ALImentation au Service de la Santé (MICALIS) AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) Italian Institute for Genomic Medicine (IIGM) |
format |
Report |
author |
Dora, Tortarolo Simone, Pernice Clapero, Fabiana Valdembri, Donatella Serini, Guido Riccardo, Federica Tarone, Lidia Bena, Chiara Enrico Bosia, Carla Contaldo, Sandro Gepiro Marco, Beccuti Pennisi, Marzio Francesca, Cordero |
author_facet |
Dora, Tortarolo Simone, Pernice Clapero, Fabiana Valdembri, Donatella Serini, Guido Riccardo, Federica Tarone, Lidia Bena, Chiara Enrico Bosia, Carla Contaldo, Sandro Gepiro Marco, Beccuti Pennisi, Marzio Francesca, Cordero |
author_sort |
Dora, Tortarolo |
title |
OmniReprodubileCellAnalysis: a comprehensive toolbox for the analysis of cellular biology data |
title_short |
OmniReprodubileCellAnalysis: a comprehensive toolbox for the analysis of cellular biology data |
title_full |
OmniReprodubileCellAnalysis: a comprehensive toolbox for the analysis of cellular biology data |
title_fullStr |
OmniReprodubileCellAnalysis: a comprehensive toolbox for the analysis of cellular biology data |
title_full_unstemmed |
OmniReprodubileCellAnalysis: a comprehensive toolbox for the analysis of cellular biology data |
title_sort |
omnireprodubilecellanalysis: a comprehensive toolbox for the analysis of cellular biology data |
publisher |
HAL CCSD |
publishDate |
2023 |
url |
https://hal.science/hal-04328085 https://doi.org/10.1101/2023.11.07.565961 |
genre |
Orca |
genre_facet |
Orca |
op_source |
https://hal.science/hal-04328085 2023 |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1101/2023.11.07.565961 hal-04328085 https://hal.science/hal-04328085 BIORXIV: 2023.11.07.565961 doi:10.1101/2023.11.07.565961 |
op_doi |
https://doi.org/10.1101/2023.11.07.565961 |
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1810470247532068864 |