Vasodilatory prostaglandins play a key role in neurovascular coupling (NVC),

Cerebellar activity supported by PKC-dependent long-term depression in Purkinje cells (PCs) is involved in the stabilization of self-motion based hippocampal representation, but the existence of cerebellar processes underlying integration of allocentric cues remains unclear. Using mutant-mice lacking PP2B in PCs (L7-PP2B mice) we here assess the role of PP2B-dependent PC potentiation in hippocampal representation and spatial navigation. L7-PP2B mice display higher susceptibility to spatial map instability relative to the allocentric cue and impaired allocentric as well as self-motion goal-directed navigation. These results indicate that PP2B-dependent potentiation in PCs contributes to maintain a stable hippocampal representation of a familiar environment in an allocentric reference frame as well as to support optimal trajectory toward a goal during navigation.

Learning a new goal-directed behavioral task often requires the improvement of at least two processes, including an enhanced stimulusresponse association and an optimization of the execution of the motor response. The cerebellum has recently been shown to play a role in acquiring goal-directed behavior, but it is unclear to what extent it contributes to a change in the stimulus-response association and/or the optimization of the execution of the motor response. We therefore designed the stimulus-dependent water Y-maze conditioning task, which allows discrimination between both processes, and we subsequently subjected Purkinje cell-specific mutant mice to this new task. The mouse mutants L7-PKCi, which suffer from impaired PKC-dependent processes such as parallel fiber to Purkinje cell long-term depression (PF-PC LTD), were able to acquire the stimulus-response association, but exhibited a reduced optimization of their motor performance. These data show that PF-PC LTD is not required for learning a stimulus-response association, but they do suggest that a PKC-dependent process in cerebellar Purkinje cells is required for optimization of motor responses.

No abstract

Life science has entered the so-called 'big data era' where biologists, clinicians and bioinformaticians are overwhelmed with high-throughput sequencing data. While they offer new insights to decipher the genome structure they also raise major challenges to use them for daily clinical practice care and diagnosis purposes as they are bigger and bigger. Therefore, we implemented a software to reduce the time to delivery for the alignment and the sorting of high-throughput sequencing data.  Our solution is implemented using Message Passing Interface and is intended for high-performance computing architecture. The software scales linearly with respect to the size of the data and ensures a total reproducibility with the traditional tools. For example, a 300X whole genome can be aligned and sorted within less than 9 hours with 128 cores. The software offers significant speed-up using multi-cores and multi-nodes parallelization.

Life science has entered the so-called 'big data era' where biologists, clinicians and bioinformaticians are overwhelmed with high-throughput sequencing data. While they offer new insights to decipher the genome structure they also raise major challenges to use them for daily clinical practice care and diagnosis purposes as they are bigger and bigger. Therefore, we implemented a software to reduce the time to delivery for the alignment and the sorting of high-throughput sequencing data.  Our solution is implemented using Message Passing Interface and is intended for high-performance computing architecture. The software scales linearly with respect to the size of the data and ensures a total reproducibility with the traditional tools. For example, a 300X whole genome can be aligned and sorted within less than 9 hours with 128 cores. The software offers significant speed-up using multi-cores and multi-nodes parallelization.

With the advent of high-throughput biotechnological platforms and their ever-growing capacity, life science has turned into a digitized, computational and data-intensive discipline. As a consequence, standard analysis with a bioinformatics pipeline in the context of routine production has become a challenge such that the data can be processed in real-time and delivered to the end-users as fast as possible. The usage of workflow management systems along with packaging systems and containerization technologies offer an opportunity to tackle this challenge. While very powerful, they can be used and combined in many multiple ways which may differ from one developer to another. Therefore, promoting the homogeneity of the workflow implementation requires guidelines and protocols which detail how the source code of the bioinformatics pipeline should be written and organized to ensure its usability, maintainability, interoperability, sustainability, portability, reproducibility, scalability and efficiency. Capitalizing on Nextflow, Conda, Docker, Singularity and the nf-core initiative, we propose a set of best practices along the development life cycle of the bioinformatics pipeline and deployment for production operations which target different expert communities including i) the bioinformaticians and statisticians ii) the software engineers and iii) the data managers and core facility engineers. We implemented Geniac (Automatic Configuration GENerator and Installer for nextflow pipelines) which consists of a toolbox with three components: i) a technical documentation available at https://geniac.readthedocs.io to detail coding guidelines for the bioinformatics pipeline with Nextflow, ii) a command line interface with a linter to check that the code respects the guidelines, and iii) an add-on to generate configuration files, build the containers and deploy the pipeline. The Geniac toolbox aims at the harmonization of development practices across developers and automation of the generation of configuration files and containers by parsing the source code of the Nextflow pipeline.

With the advent of high-throughput biotechnological platforms and their ever-growing capacity, life science has turned into a digitized, computational and data-intensive discipline. As a consequence, standard analysis with a bioinformatics pipeline in the context of routine production has become a challenge such that the data can be processed in real-time and delivered to the end-users as fast as possible. The usage of workflow management systems along with packaging systems and containerization technologies offer an opportunity to tackle this challenge. While very powerful, they can be used and combined in multiple ways thus increasing their usage complexity. Therefore, guidelines and protocols are required in order to detail how the source code of the bioinformatics pipeline should be written and organized to ensure its usability, maintainability, interoperability, sustainability, portability, reproducibility, scalability and efficiency. Capitalizing on Nextflow, Conda, Docker, Singularity and the nf-core initiative, we propose a set of best practices along the development life cycle of the bioinformatics pipeline and deployment for production operations which address different expert communities including i) the bioinformaticians and statisticians ii) the software engineers and iii) the data managers and core facility engineers. We implemented Geniac (Automatic Configuration GENerator and Installer for nextflow pipelines) which consists of a toolbox with three components: i) a technical documentation available at https://geniac.readthedocs.io to detail coding guidelines for the bioinformatics pipeline with Nextflow, ii) a linter to check that the code respects the guidelines, and iii) an add-on to generate configuration files, build the containers and deploy the pipeline. The Geniac toolbox aims at the harmonization of development practices across developers and automation of the generation of configuration files and containers by parsing the source code of the Nextflow pipeline. The Geniac toolbox and two demo pipelines are available on GitHub. This article presents the main functionalities of Geniac.