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The growing appetite of behavioral neuroscience for automated data production is prompting the need for new computational standards allowing improved interoperability, reproducibility, and shareability. We show here how these issues can be solved by repurposing existing genomic formats whose structure perfectly supports the handling of time series. This allows existing genomic analysis and visualization tools to be deployed onto behavioral data. As a proof of principle, we implemented the conversion procedure in Pergola, an open source software, and used genomics tools to reproduce results obtained in mouse, fly, and worm. We also show how common genomics techniques such as principal component analysis, hidden Markov modeling, and volcano plots can be deployed on the reformatted behavioral data. These analyses are easy to share because they depend on the scripting of public software. They are also easy to reproduce thanks to their integration within Nextflow, a workflow manager using containerized software. ; We acknowledge the support of the "Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement de la Generalitat i del Fons Social Europeu," Spanish Ministry of Economy, Industry and Competitiveness (MEIC) under grant numbers BFU2014-55062-P and BFU2017-88264-P, the EMBL partnership, FEDER, the "Centro de Excelencia Severo Ochoa" Programme, and the CERCA Programme/Generalitat de Catalunya. This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 635290-PanCanRisk and OpenRiskNet_731076. This reflects only the author's view and the funder is not responsible for any use that may be made of the information it contains. M.D. is supported by DIUE de la Generalitat de Catalunya (Grups consolidats SGR 2017/926), the Fondation Jérôme Lejeune (Paris, France), MINECO (SAF2013-49129-C2-1-R; SAF2016-79956-R), CDTI ("Smartfoods") and EU (EraNet Neuron PCIN-2013-060 and JPND AC17/00006), and the Catalan foundation "La Marató de TV3" (#2016/20-30). The CIBER of Rare Diseases is an initiative of the ISCIII.

Several bioinformatic tools have been developed for genome-wide identification of orthologous and paralogous genes. However, no corresponding tool allows the detection of exon homology relationships. Here, we present ExOrthist, a fully reproducible Nextflow-based software enabling inference of exon homologs and orthogroups, visualization of evolution of exon-intron structures, and assessment of conservation of alternative splicing patterns. ExOrthist evaluates exon sequence conservation and considers the surrounding exon-intron context to derive genome-wide multi-species exon homologies at any evolutionary distance. We demonstrate its use in different evolutionary scenarios: whole genome duplication in frogs and convergence of Nova-regulated splicing networks ( https://github.com/biocorecrg/ExOrthist). ; The research has been funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (ERC-StG-LS2-637591 to MI), the Spanish Ministerio de Ciencia (BFU2017-89201-P to MI, including and FPI PhD fellowship to FM), the "Centro de Excelencia Severo Ochoa 2013-2017" (SEV-2012-0208), EMBO Long Term postdoctoral fellowship (ALTF 1505-2015 to YM), and Marie Skłodowska-Curie actions (MSCA) grant (705938 to YM). We acknowledge the support of the CERCA Programme/Generalitat de Catalunya and of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership

The direct RNA sequencing platform offered by Oxford Nanopore Technologies allows for direct measurement of RNA molecules without the need of conversion to complementary DNA, fragmentation or amplification. As such, it is virtually capable of detecting any given RNA modification present in the molecule that is being sequenced, as well as provide polyA tail length estimations at the level of individual RNA molecules. Although this technology has been publicly available since 2017, the complexity of the raw Nanopore data, together with the lack of systematic and reproducible pipelines, have greatly hindered the access of this technology to the general user. Here we address this problem by providing a fully benchmarked workflow for the analysis of direct RNA sequencing reads, termed MasterOfPores. The pipeline starts with a pre-processing module, which converts raw current intensities into multiple types of processed data including FASTQ and BAM, providing metrics of the quality of the run, quality-filtering, demultiplexing, base-calling and mapping. In a second step, the pipeline performs downstream analyses of the mapped reads, including prediction of RNA modifications and estimation of polyA tail lengths. Four direct RNA MinION sequencing runs can be fully processed and analyzed in 10 h on 100 CPUs. The pipeline can also be executed in GPU locally or in the cloud, decreasing the run time fourfold. The software is written using the NextFlow framework for parallelization and portability, and relies on Linux containers such as Docker and Singularity for achieving better reproducibility. The MasterOfPores workflow can be executed on any Unix-compatible OS on a computer, cluster or cloud without the need of installing any additional software or dependencies, and is freely available in Github (https://github.com/biocorecrg/master_of_pores). This workflow simplifies direct RNA sequencing data analyses, facilitating the study of the (epi)transcriptome at single molecule resolution. ; This work was partly supported by the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) (PGC2018-098152-A-100 to EN) and by the Australian Research Council (DP180103571 to EN). LP was supported by funding from the European Union's H2020 Research and Innovation Programme under Marie Skłodowska-Curie grant agreement no. 754422

QCloud is a cloud-based system to support proteomics laboratories in daily quality assessment using a user-friendly interface, easy setup, and automated data processing. Since its release, QCloud has facilitated automated quality control for proteomics experiments in many laboratories. QCloud provides a quick and effortless evaluation of instrument performance that helps to overcome many analytical challenges derived from clinical and translational research. Here we present an improved version of the system, QCloud2. This new version includes enhancements in the scalability and reproducibility of the quality-control pipelines, and it features an improved front end for data visualization, user management, and chart annotation. The QCloud2 system also includes programmatic access and a standalone local version. ; The CRG/UPF Proteomics Unit is part of the Spanish Infrastructure for Omics Technologies (ICTS OmicsTech), and it is a member of the ProteoRed PRB3 consortium, which is supported by grant PT17/0019 of the PE I+D+i 2013−2016 from the Instituto de Salud Carlos III (ISCIII), ERDF, and "Secretaria d'Universitats i Recerca del Departament d'Econòmia i Coneixement de la Generalitat de Catalunya" (2017SGR595). This project was supported by EPIC-XS, project number 823839, funded by the Horizon 2020 Programme of the European Union. We also acknowledge support of the Spanish Ministry of Science and Innovation to the EMBL partnership, the Centro de Excelencia Severo Ochoa and the CERCA Programme from Generalitat de Catalunya

Opitz trigonocephaly C syndrome (OTCS) is a rare genetic disorder characterized by craniofacial anomalies, variable intellectual and psychomotor disability, and variable cardiac defects with a high mortality rate. Different patterns of inheritance and genetic heterogeneity are known in this syndrome. Whole exome and genome sequencing of a 19-year-old girl (P7), initially diagnosed with OTCS, revealed a de novo nonsense mutation, p.Q638*, in the MAGEL2 gene. MAGEL2 is an imprinted, maternally silenced, gene located at 15q11-13, within the Prader-Willi region. Patient P7 carried the mutation in the paternal chromosome. Recently, mutations in MAGEL2 have been described in Schaaf-Yang syndrome (SHFYNG) and in severe arthrogryposis. Patient P7 bears resemblances with SHFYNG cases but has other findings not described in this syndrome and common in OTCS. We sequenced MAGEL2 in nine additional OTCS patients and no mutations were found. This study provides the first clear molecular genetic basis for an OTCS case, indicates that there is overlap between OTCS and SHFYNG syndromes, and confirms that OTCS is genetically heterogeneous. Genes encoding MAGEL2 partners, either in the retrograde transport or in the ubiquitination-deubiquitination complexes, are promising candidates as OTCS disease-causing genes. ; Funding was from Associació Síndrome Opitz C, Terrassa, Spain; Spanish Ministerio de Economía y Competitividad (SAF2014-56562-R; FECYT, crowdfunding PRECIPITA); Catalan Government (2014SGR932) and from CIBERER (U720). We acknowledge support of the Spanish Ministry of Economy and Competitiveness, 'Centro de Excelencia Severo Ochoa 2013-2017'.