Pixel: a content management platform for quantitative omics data
In biology, high-throughput experimental technologies, also referred as "omics" technologies, are increasingly used in research laboratories. Several thousands of gene expression measurements can be obtained in a single experiment. Researchers are routinely facing the challenge to annotate...
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| Published in: | PeerJ (San Francisco, CA) Vol. 7; p. e6623 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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27.03.2019
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| ISSN: | 2167-8359, 2167-8359 |
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| Abstract | In biology, high-throughput experimental technologies, also referred as "omics" technologies, are increasingly used in research laboratories. Several thousands of gene expression measurements can be obtained in a single experiment. Researchers are routinely facing the challenge to annotate, store, explore and mine all the biological information they have at their disposal. We present here the Pixel web application (Pixel Web App), an original content management platform to help people involved in a multi-omics biological project.
The Pixel Web App is built with open source technologies and hosted on the collaborative development platform GitHub (https://github.com/Candihub/pixel). It is written in Python using the Django framework and stores all the data in a PostgreSQL database. It is developed in the open and licensed under the BSD 3-clause license. The Pixel Web App is also heavily tested with both unit and functional tests, a strong code coverage and continuous integration provided by CircleCI. To ease the development and the deployment of the Pixel Web App, Docker and Docker Compose are used to bundle the application as well as its dependencies.
The Pixel Web App offers researchers an intuitive way to annotate, store, explore and mine their multi-omics results. It can be installed on a personal computer or on a server to fit the needs of many users. In addition, anyone can enhance the application to better suit their needs, either by contributing directly on GitHub (encouraged) or by extending Pixel on their own. The Pixel Web App does not provide any computational programs to analyze the data. Still, it helps to rapidly explore and mine existing results and holds a strategic position in the management of research data. |
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| AbstractList | Background In biology, high-throughput experimental technologies, also referred as "omics" technologies, are increasingly used in research laboratories. Several thousands of gene expression measurements can be obtained in a single experiment. Researchers are routinely facing the challenge to annotate, store, explore and mine all the biological information they have at their disposal. We present here the Pixel web application (Pixel Web App), an original content management platform to help people involved in a multi-omics biological project. Methods The Pixel Web App is built with open source technologies and hosted on the collaborative development platform GitHub ( Results The Pixel Web App offers researchers an intuitive way to annotate, store, explore and mine their multi-omics results. It can be installed on a personal computer or on a server to fit the needs of many users. In addition, anyone can enhance the application to better suit their needs, either by contributing directly on GitHub (encouraged) or by extending Pixel on their own. The Pixel Web App does not provide any computational programs to analyze the data. Still, it helps to rapidly explore and mine existing results and holds a strategic position in the management of research data. Background In biology, high-throughput experimental technologies, also referred as “omics” technologies, are increasingly used in research laboratories. Several thousands of gene expression measurements can be obtained in a single experiment. Researchers are routinely facing the challenge to annotate, store, explore and mine all the biological information they have at their disposal. We present here the Pixel web application (Pixel Web App), an original content management platform to help people involved in a multi-omics biological project. Methods The Pixel Web App is built with open source technologies and hosted on the collaborative development platform GitHub (https://github.com/Candihub/pixel). It is written in Python using the Django framework and stores all the data in a PostgreSQL database. It is developed in the open and licensed under the BSD 3-clause license. The Pixel Web App is also heavily tested with both unit and functional tests, a strong code coverage and continuous integration provided by CircleCI. To ease the development and the deployment of the Pixel Web App, Docker and Docker Compose are used to bundle the application as well as its dependencies. Results The Pixel Web App offers researchers an intuitive way to annotate, store, explore and mine their multi-omics results. It can be installed on a personal computer or on a server to fit the needs of many users. In addition, anyone can enhance the application to better suit their needs, either by contributing directly on GitHub (encouraged) or by extending Pixel on their own. The Pixel Web App does not provide any computational programs to analyze the data. Still, it helps to rapidly explore and mine existing results and holds a strategic position in the management of research data. In biology, high-throughput experimental technologies, also referred as "omics" technologies, are increasingly used in research laboratories. Several thousands of gene expression measurements can be obtained in a single experiment. Researchers are routinely facing the challenge to annotate, store, explore and mine all the biological information they have at their disposal. We present here the Pixel web application (Pixel Web App), an original content management platform to help people involved in a multi-omics biological project. The Pixel Web App is built with open source technologies and hosted on the collaborative development platform GitHub (https://github.com/Candihub/pixel). It is written in Python using the Django framework and stores all the data in a PostgreSQL database. It is developed in the open and licensed under the BSD 3-clause license. The Pixel Web App is also heavily tested with both unit and functional tests, a strong code coverage and continuous integration provided by CircleCI. To ease the development and the deployment of the Pixel Web App, Docker and Docker Compose are used to bundle the application as well as its dependencies. The Pixel Web App offers researchers an intuitive way to annotate, store, explore and mine their multi-omics results. It can be installed on a personal computer or on a server to fit the needs of many users. In addition, anyone can enhance the application to better suit their needs, either by contributing directly on GitHub (encouraged) or by extending Pixel on their own. The Pixel Web App does not provide any computational programs to analyze the data. Still, it helps to rapidly explore and mine existing results and holds a strategic position in the management of research data. In biology, high-throughput experimental technologies, also referred as "omics" technologies, are increasingly used in research laboratories. Several thousands of gene expression measurements can be obtained in a single experiment. Researchers are routinely facing the challenge to annotate, store, explore and mine all the biological information they have at their disposal. We present here the Pixel web application (Pixel Web App), an original content management platform to help people involved in a multi-omics biological project. The Pixel Web App is built with open source technologies and hosted on the collaborative development platform GitHub (https://github.com/Candihub/pixel). It is written in Python using the Django framework and stores all the data in a PostgreSQL database. It is developed in the open and licensed under the BSD 3-clause license. The Pixel Web App is also heavily tested with both unit and functional tests, a strong code coverage and continuous integration provided by CircleCI. To ease the development and the deployment of the Pixel Web App, Docker and Docker Compose are used to bundle the application as well as its dependencies. The Pixel Web App offers researchers an intuitive way to annotate, store, explore and mine their multi-omics results. It can be installed on a personal computer or on a server to fit the needs of many users. In addition, anyone can enhance the application to better suit their needs, either by contributing directly on GitHub (encouraged) or by extending Pixel on their own. The Pixel Web App does not provide any computational programs to analyze the data. Still, it helps to rapidly explore and mine existing results and holds a strategic position in the management of research data. In biology, high-throughput experimental technologies, also referred as "omics" technologies, are increasingly used in research laboratories. Several thousands of gene expression measurements can be obtained in a single experiment. Researchers are routinely facing the challenge to annotate, store, explore and mine all the biological information they have at their disposal. We present here the Pixel web application (Pixel Web App), an original content management platform to help people involved in a multi-omics biological project.BACKGROUNDIn biology, high-throughput experimental technologies, also referred as "omics" technologies, are increasingly used in research laboratories. Several thousands of gene expression measurements can be obtained in a single experiment. Researchers are routinely facing the challenge to annotate, store, explore and mine all the biological information they have at their disposal. We present here the Pixel web application (Pixel Web App), an original content management platform to help people involved in a multi-omics biological project.The Pixel Web App is built with open source technologies and hosted on the collaborative development platform GitHub (https://github.com/Candihub/pixel). It is written in Python using the Django framework and stores all the data in a PostgreSQL database. It is developed in the open and licensed under the BSD 3-clause license. The Pixel Web App is also heavily tested with both unit and functional tests, a strong code coverage and continuous integration provided by CircleCI. To ease the development and the deployment of the Pixel Web App, Docker and Docker Compose are used to bundle the application as well as its dependencies.METHODSThe Pixel Web App is built with open source technologies and hosted on the collaborative development platform GitHub (https://github.com/Candihub/pixel). It is written in Python using the Django framework and stores all the data in a PostgreSQL database. It is developed in the open and licensed under the BSD 3-clause license. The Pixel Web App is also heavily tested with both unit and functional tests, a strong code coverage and continuous integration provided by CircleCI. To ease the development and the deployment of the Pixel Web App, Docker and Docker Compose are used to bundle the application as well as its dependencies.The Pixel Web App offers researchers an intuitive way to annotate, store, explore and mine their multi-omics results. It can be installed on a personal computer or on a server to fit the needs of many users. In addition, anyone can enhance the application to better suit their needs, either by contributing directly on GitHub (encouraged) or by extending Pixel on their own. The Pixel Web App does not provide any computational programs to analyze the data. Still, it helps to rapidly explore and mine existing results and holds a strategic position in the management of research data.RESULTSThe Pixel Web App offers researchers an intuitive way to annotate, store, explore and mine their multi-omics results. It can be installed on a personal computer or on a server to fit the needs of many users. In addition, anyone can enhance the application to better suit their needs, either by contributing directly on GitHub (encouraged) or by extending Pixel on their own. The Pixel Web App does not provide any computational programs to analyze the data. Still, it helps to rapidly explore and mine existing results and holds a strategic position in the management of research data. |
| ArticleNumber | e6623 |
| Audience | Academic |
| Author | Durand, William Hébert, Charles Poulain, Pierre Camadro, Jean-Michel Lelandais, Gaëlle Denecker, Thomas Maupetit, Julien |
| Author_xml | – sequence: 1 givenname: Thomas surname: Denecker fullname: Denecker, Thomas organization: CEA, CNRS, Univ. Paris-Sud, Institute for Integrative Biology of the Cell (I2BC), Gif-sur-Yvette, France – sequence: 2 givenname: William surname: Durand fullname: Durand, William organization: TailorDev SAS, Clermont-Ferrand, France – sequence: 3 givenname: Julien surname: Maupetit fullname: Maupetit, Julien organization: TailorDev SAS, Clermont-Ferrand, France – sequence: 4 givenname: Charles surname: Hébert fullname: Hébert, Charles organization: BIOROSETICS, Houilles, France – sequence: 5 givenname: Jean-Michel surname: Camadro fullname: Camadro, Jean-Michel organization: CNRS, Univ. Paris Diderot, Institut Jacques Monod (IJM), Paris, France – sequence: 6 givenname: Pierre surname: Poulain fullname: Poulain, Pierre organization: CNRS, Univ. Paris Diderot, Institut Jacques Monod (IJM), Paris, France – sequence: 7 givenname: Gaëlle surname: Lelandais fullname: Lelandais, Gaëlle organization: CEA, CNRS, Univ. Paris-Sud, Institute for Integrative Biology of the Cell (I2BC), Gif-sur-Yvette, France |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30944779$$D View this record in MEDLINE/PubMed https://cnrs.hal.science/hal-03100146$$DView record in HAL |
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| Cites_doi | 10.1038/nmeth.4558 10.1093/nar/gkq1019 10.1371/journal.pone.0020924 10.7717/peerj-cs.142 10.1038/nature02579 10.1086/599039 10.1111/cmi.12091 10.1093/nar/gkp1137 10.1002/yea.3031 10.1371/journal.pone.0067111 10.1186/gb-2008-9-11-r164 10.1186/1471-2105-15-S7-S9 10.1038/s41598-017-03750-5 10.1073/pnas.1708290115 10.1093/nar/gkw343 10.1109/MCSE.2017.3151254 10.1126/science.1213847 10.1016/j.diagmicrobio.2012.10.003 10.1002/yea.3019 10.1002/pmic.200401303 10.1007/978-1-4939-3578-9_5 10.1074/mcp.R110.000133 10.2144/000114107 10.1016/j.molcel.2015.05.004 10.1111/mmi.12983 10.3389/fmicb.2016.00645 10.7717/peerj.3208 10.1093/nar/gku1357 10.1038/533452a 10.1038/507294a 10.3389/fgene.2017.00084 10.1093/nar/gkw924 10.1371/journal.pcbi.1005412 10.1093/bib/bbt086 10.1093/nar/gkj040 10.1038/ncb2558 10.1099/mic.0.063610-0 |
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| Copyright | COPYRIGHT 2019 PeerJ. Ltd. 2019 Denecker et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Attribution 2019 Denecker et al. 2019 Denecker et al. |
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| Keywords | Data cycle analyses Open source Omics Pixel Web App Subjects Computational Biology Genomics Data Science Data cycle analyses |
| Language | English |
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