SUMMARY: Upstream open reading frames (uORFs, often encoding so-called leader peptides) can regulate translation and transcription of downstream main ORFs (mORFs) in prokaryotes and eukaryotes. However, annotation of novel functional uORFs is challenging due to their short size of usually <100 codons. While transcription- and translation-level next-generation sequencing methods can be used for genome-wide functional uORF identification, this data are not available for the vast majority of species with sequenced genomes. At the same time, the exponentially increasing amount of genome assemblies gives us the opportunity to take advantage of evolutionary conservation in our predictions of functional ORFs.Here, we present a tool for conserved uORF annotation in 5' upstream sequences of a user-defined protein of interest or a set of protein homologs. It can also be used to find small conserved ORFs within a set of nucleotide sequences. The output includes publication-quality figures with multiple sequence alignments, sequence logos, and locus annotation of the predicted conserved uORFs in graphical vector format. AVAILABILITY AND IMPLEMENTATION: uORF4u is written in Python3 and runs on Linux and MacOS. The command-line interface covers most practical use cases, while the provided Python API allows usage within a Python program and additional customization. Source code is available from the GitHub page: github.com/GCA-VH-lab/uorf4u. Detailed documentation that includes an example-driven guide available at the software home page: gca-vh-lab.github.io/uorf4u. A web version of uORF4u is available at server.atkinson-lab.com/uorf4u.

BACKGROUND: High-throughput sequencing often provides a foundation for experimental analyses in the life sciences. For many such methods, an intermediate layer of bioinformatics data analysis is the genomic signal track constructed by short read mapping to a particular genome assembly. There are many software tools to visualize genomic tracks in a web browser or with a stand-alone graphical user interface. However, there are only few command-line applications suitable for automated usage or production of publication-ready visualizations. RESULTS: Here we present svist4get, a command-line tool for customizable generation of publication-quality figures based on data from genomic signal tracks. Similarly to generic genome browser software, svist4get visualizes signal tracks at a given genomic location and is able to aggregate data from several tracks on a single plot along with the transcriptome annotation. The resulting plots can be saved as the vector or high-resolution bitmap images. We demonstrate practical use cases of svist4get for Ribo-Seq and RNA-Seq data. CONCLUSIONS: svist4get is implemented in Python 3 and runs on Linux. The command-line interface of svist4get allows for easy integration into bioinformatics pipelines in a console environment. Extra customization is possible through configuration files and Python API. For convenience, svist4get is provided as pypi package. The source code is available at https://bitbucket.org/artegorov/svist4get/.