SUMMARY: CRISPR screens are increasingly performed to associate genotypes with genotypes. So far, however, their analysis required specialized computational knowledge to transform high-throughput next-generation sequencing (NGS) data into sequence formats amenable for downstream analysis. We developed ReCo, a stand-alone and user-friendly analytics tool for generating read-count tables of single and combinatorial CRISPR library and screen-based NGS data. Together with cutadapt and bowtie2 for rapid sequence trimming and alignment, ReCo enables the automated generation of read count tables from staggered NGS reads for the downstream identification of gRNA-induced phenotypes. AVAILABILITY AND IMPLEMENTATION: ReCo is published under the MIT license and available at: https://github.com/KaulichLab/ReCo.

The hallmarks of Alzheimer's disease (AD) are characterized by cognitive decline and behavioral changes. The most prominent brain region affected by the progression of AD is the hippocampal formation. The pathogenesis involves a successive loss of hippocampal neurons accompanied by a decline in learning and memory consolidation mainly attributed to an accumulation of senile plaques. The amyloid precursor protein (APP) has been identified as precursor of Aβ-peptides, the main constituents of senile plaques. Until now, little is known about the physiological function of APP within the central nervous system. The allocation of APP to the proteome of the highly dynamic presynaptic active zone (PAZ) highlights APP as a yet unknown player in neuronal communication and signaling. In this study, we analyze the impact of APP deletion on the hippocampal PAZ proteome. The native hippocampal PAZ derived from APP mouse mutants (APP-KOs and NexCreAPP/APLP2-cDKOs) was isolated by subcellular fractionation and immunopurification. Subsequently, an isobaric labeling was performed using TMT6 for protein identification and quantification by high-resolution mass spectrometry. We combine bioinformatics tools and biochemical approaches to address the proteomics dataset and to understand the role of individual proteins. The impact of APP deletion on the hippocampal PAZ proteome was visualized by creating protein-protein interaction (PPI) networks that incorporated APP into the synaptic vesicle cycle, cytoskeletal organization, and calcium-homeostasis. The combination of subcellular fractionation, immunopurification, proteomic analysis, and bioinformatics allowed us to identify APP as structural and functional regulator in a context-sensitive manner within the hippocampal active zone network.

Mobile internet has rapidly evolved in the past years with an ever increasing number of novel technologies and services. Today's mobile devices often support multiple communication technologies for accessing Internet services. However, they all do not tap the full potential of these capabilities as users often have to manually select networks and only one network is used at a time. Different approaches in the area of Always Best Connected propose strategies for user-centric network selection, but most of them focus either on application-oriented or device-centric approaches. The muXer project aims at combining both aspects and increasing the efficiency of network usage. In this paper, we introduce the muXer Global Arbitration Process (GAP) that balances application-oriented ratings of networks while optimizing the usage of communications resources and simultaneously meeting the application and user requirements. Besides deciding which single network an application should use, GAP considers network bundles as well as a proactive delaying of data transfers as additional options to reach a global optimum for all applications running on the device. We compare GAP with other network selection strategies and present benchmarks showing that these aspects bring a clear efficiency gain.