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Denecker, T.

Publications and source records attributed to Denecker, T..

2 recordsLinked to original sources

New, easy, quick and efficient DNA replication timing analysis by high-throughput approaches.

DNA replication must be faithful and follow a well-defined spatio-temporal program closely linked to transcriptional activity, epigenomic marks, intra-nuclear structures, mutation rate and cell fate determination. Among the readouts of the DNA replication spatio-temporal program, replication timing (RT) analyses require complex, precise and time-consuming experimental procedures, and the study of large-size computer files. We improved the RT protocol to speed it up and increase its quality and reproducibility. Also, we partly automated the RT protocol and developed a user-friendly software: the START-R suite (Simple Tool for the Analysis of the Replication Timing based on R). START-R suite is an open source web application using an R script and an HTML interface to analyze DNA replication timing in a given cell line with microarray or deep-sequencing results. This novel approach can be used by every biologist without requiring specific knowledge in bioinformatics. It also reduces the time required for generating and analyzing simultaneously data from several samples. START-R suite detects constant timing regions (CTR) but also, and this is a novelty, it identifies temporal transition regions (TTR) and detects significant differences between two experimental conditions. The informatic global analysis requires less than 10 minutes.

genomics↗

Pixel: a digital lab assistant to integrate biological data in multi-omics projects

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. 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. 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.

bioinformatics↗