SUMMARY: Life over the past four billion years has been shaped by proteins and their capacity to assemble into three-dimensional conformations. Protein sequence alignments have been the enabling technology for exploring the evolution and functional adaptation of proteins across the tree of life. Recent advancements in scaling the prediction of three-dimensional protein structures from primary sequence alone, revealed that different modes of conservation and function operate on the sequence and structure level. This difference in protein conservation patterns and their underlying functional change that could emerge in suboptimal alignment configurations is often ignored in optimal protein alignment approaches. We introduce EMERALD-UI, an open-source interactive web application which is designed to reveal unexplored biology by visualising stable structural conformations or protein regions hidden in the alternative alignment space. AVAILABILITY: EMERALD-UI is available at https://algbio.github.io/emerald-ui/. The source code of the version described in this manuscript is available at https://github.com/algbio/emerald-ui and archived at Software Heritage: swh: 1: dir: 8b5a70160396d5e9a2e6d015c3b6f1426176d9a4.

Motivation: Next Generation Sequencing (NGS) technologies generate a large amount of high quality transcriptome datasets enabling the investigation of molecular processes on a genomic and metagenomic scale. These transcriptomics studies aim to quantify and compare the molecular phenotypes of the biological processes at hand. Despite the vast increase of available transcriptome datasets, little is known about the evolutionary conservation of those characterized transcriptomes. Results: The myTAI package implements exploratory analysis functions to infer transcriptome conservation patterns in any transcriptome dataset. Comprehensive documentation of myTAI functions and tutorial vignettes provide step-by-step instructions on how to use the package in an exploratory and computationally reproducible manner. Availability and implementation: The open source myTAI package is available at https://github.com/HajkD/myTAI and https://cran.r-project.org/web/packages/myTAI/index.html. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: The secretome denotes the collection of secreted proteins exported outside of the cell. The functional roles of secreted proteins include the maintenance and remodelling of the extracellular matrix as well as signalling between host and non-host cells. These features make secretomes rich reservoirs of biomarkers for disease classification and host-pathogen interaction studies. Common biomarkers are extracellular proteins secreted via classical pathways that can be predicted from sequence by annotating the presence or absence of N-terminal signal peptides. Several heterogeneous command line tools and web-interfaces exist to identify individual motifs, signal sequences and domains that are either characteristic or strictly excluded from secreted proteins. However, a single flexible secretome-prediction workflow that combines all analytic steps is still missing. Results: To bridge this gap the SecretSanta package implements wrapper and parser functions around established command line tools for the integrative prediction of extracellular proteins that are secreted via classical pathways. The modularity of SecretSanta enables users to create tailored pipelines and apply them across the whole tree of life to facilitate comparison of secretomes across multiple species or under various conditions. Availability and implementation: SecretSanta is implemented in the R programming language and is released under GPL-3 license. All functions have been optimized and parallelized to allow large-scale processing of sequences. The open-source code, installation instructions and vignette with use case scenarios can be downloaded from https://github.com/gogleva/SecretSanta. Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Retrieval and reproducible functional annotation of genomic data are crucial in biology. However, the current poor usability and transparency of retrieval methods hinders reproducibility. Here we present an open source R package, biomartr , which provides a comprehensive easy-to-use framework for automating data retrieval and functional annotation for meta-genomic approaches. The functions of biomartr achieve a high degree of clarity, transparency and reproducibility of analyses. Results: The biomartr package implements straightforward functions for bulk retrieval of all genomic data or data for selected genomes, proteomes, coding sequences and annotation files present in databases hosted by the National Center for Biotechnology Information (NCBI) and European Bioinformatics Institute (EMBL-EBI). In addition, biomartr communicates with the BioMart database for functional annotation of retrieved sequences. Comprehensive documentation of biomartr functions and five tutorial vignettes provide step-by-step instructions on how to use the package in a reproducible manner. Availability and Implementation: The open source biomartr package is available at https://github.com/HajkD/biomartr and https://cran.r-project.org/web/packages/biomartr/index.html . Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.