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Insects are capable of extraordinary feats of long-distance movement that have profound impacts on the function of terrestrial ecosystems. The ability to undertake these movements arose multiple times through the evolution of a suite of traits that make up the migratory syndrome, however the underlying genetic pathways involved remain poorly understood. Migratory hoverflies (Diptera: Syrphidae) are an emerging model group for studies of migration. They undertake seasonal movements in huge numbers across large parts of the globe and are important pollinators, biological control agents and decomposers. Here, we assembled a high-quality draft genome of the marmalade hoverfly (Episyrphus balteatus). We leveraged this genomic resource to undertake a genome-wide transcriptomic comparison of actively migrating Episyrphus, captured from a high mountain pass as they flew south to overwinter, with the transcriptomes of summer forms which were non-migratory. We identified 1543 genes with very strong evidence for differential expression. Interrogation of this gene set reveals a remarkable range of roles in metabolism, muscle structure and function, hormonal regulation, immunity, stress resistance, flight and feeding behaviour, longevity, reproductive diapause and sensory perception. These features of the migrant phenotype have arisen by the integration and modification of pathways such as insulin signalling for diapause and longevity, JAK/SAT for immunity, and those leading to octopamine production and fuelling to boost flight capabilities. Our results provide a powerful genomic resource for future research, and paint a comprehensive picture of global expression changes in an actively migrating insect, identifying key genomic components involved in this important life-history strategy.

Summary 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., Graphical Abstract, Highlights • Genomic formats can be repurposed to handle behavioral data without information loss • Pergola can store, visualize, and analyze behavioral data with genomics tools • Genomic formats and tools ease standardized, interoperable, and reproducible analysis • Three existing sets of analysis on mouse, fly, and worms are reproduced in this study, Biological Sciences; Genetics; Behavioral Neuroscience; Bioinformatics

Functional annotation allows adding biologically relevant information to predicted features in genomic sequences, and it is, therefore, an important procedure of any de novo genome sequencing project. It is also useful for proofreading and improving gene structural annotation. Here, we introduce FA-nf, a pipeline implemented in Nextflow, a versatile computational workflow management engine. The pipeline integrates different annotation approaches, such as NCBI BLAST+, DIAMOND, InterProScan, and KEGG. It starts from a protein sequence FASTA file and, optionally, a structural annotation file in GFF format, and produces several files, such as GO assignments, output summaries of the abovementioned programs and final annotation reports. The pipeline can be broken easily into smaller processes for the purpose of parallelization and easily deployed in a Linux computational environment, thanks to software containerization, thus helping to ensure full reproducibility. The research leading to these results has received funding from the Plataforma de Recursos Biomoleculares y Bioinformáticos PT 13/0001/0021 from ISCIII, a platform co-funded by the European Regional Development Fund (FEDER) and from the Plan Estatal project funded under grant number PGC2018-094017-B-I00 from the Spanish Ministry of Science and Innovation (AEI/FEDER). We acknowledge support of the Spanish Ministry of Science and Innovation to the EMBL partnership, the Centro de Excelencia Severo Ochoa and the CERCA Programme/Generalitat de Catalunya.

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 converts raw current intensities into multiple types of processed data, providing metrics of the quality of the run, quality-filtering, base-calling and mapping. The output of the pipeline can in turn be used to compute per-gene counts, RNA modifications, and prediction of polyA tail length and RNA isoforms. 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 will significantly simplify the analysis of nanopore direct RNA sequencing data by non-bioinformatics experts, thus boosting the understanding of the (epi)transcriptome with single molecule resolution.

We present a new web application to query and visualize time-series behavioral data: the Pergola web-server. This server provides a user-friendly interface for exploring longitudinal behavioral data taking advantage of the Pergola Python library. Using the server, users can process the data applying some basic operations, such as binning or grouping, while formatting the data into existing genomic formats. Thanks to this repurposing of genomics standards, the application automatically renders an interactive data visualization based on sophisticated genome visualization tools. Our tool allows behavioral scientists to share, display and navigate complex behavioral data comprising multiple individuals and multiple data types, in a scalable and flexible manner. A download option allows for further analysis using genomic tools. The server can be a great resource for the field in a time where behavioral science is entering a data-intensive cycle thanks to high-throughput behavioral phenotyping platforms. Pergola is publicly available at http://pergola.crg.eu/. '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) [BFU2014-55062-P, BFU2017-88264-P]; EMBL partnership, FEDER, the ‘Centro de Excelencia Severo Ochoa’ Programme; CERCA Programme/Generalitatde Catalunya; European Union's Horizon 2020 research and innovation program [635290-PanCanRisk and OpenRiskNet_731076]; DIUE de la Generalitat de Catalunya [Grups consolidatsSGR 2017/926]; 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]; Catalan foundation ‘La Marato′de TV3’ [2016/20-30]; CIBER of Rare Diseases is an initiative of the ISCIII. Funding for open access charge: CRG institutional core funding.