Modern biological research is increasingly data-intensive, leading to a growing demand for effective training in biological data science. In this article, we provide an overview of key resources and best practices available within the Bioconductor project-an open-source software community focused on omics data analysis. This guide serves as a valuable reference for both learners and educators in the field.

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis, and stewardship are still rarely taught in life science educational programs, resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https://training.galaxyproject.org), an open access, community-driven framework for the collection of FAIR (Findable, Accessible, Interoperable, Reusable) training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics, and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments.

MOTIVATION: Seurat is one of the most popular software suites for the analysis of single-cell RNA sequencing data. Considering the popularity of the tidyverse ecosystem, which offers a large set of data display, query, manipulation, integration and visualization utilities, a great opportunity exists to interface the Seurat object with the tidyverse. This interface gives the large data science community of tidyverse users the possibility to operate with familiar grammar. RESULTS: To provide Seurat with a tidyverse-oriented interface without compromising efficiency, we developed tidyseurat, a lightweight adapter to the tidyverse. Tidyseurat displays cell information as a tibble abstraction, allowing intuitively interfacing Seurat with dplyr, tidyr, ggplot2 and plotly packages powering efficient data manipulation, integration and visualization. Iterative analyses on data subsets are enabled by interfacing with the popular nest-map framework. AVAILABILITY AND IMPLEMENTATION: The software is freely available at cran.r-project.org/web/packages/tidyseurat and github.com/stemangiola/tidyseurat. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract Motivation: In light of the increasing adoption of targeted resequencing (TR) as a cost-effective strategy to identify disease-causing variants, a robust method for copy number variation (CNV) analysis is needed to maximize the value of this promising technology. Results: We present a method for CNV detection for TR data, including whole-exome capture data. Our method calls copy number gains and losses for each target region based on normalized depth of coverage. Our key strategies include the use of base-level log-ratios to remove GC-content bias, correction for an imbalanced library size effect on log-ratios, and the estimation of log-ratio variations via binning and interpolation. Our methods are made available via CONTRA (COpy Number Targeted Resequencing Analysis), a software package that takes standard alignment formats (BAM/SAM) and outputs in variant call format (VCF4.0), for easy integration with other next-generation sequencing analysis packages. We assessed our methods using samples from seven different target enrichment assays, and evaluated our results using simulated data and real germline data with known CNV genotypes. Availability and implementation: Source code and sample data are freely available under GNU license (GPLv3) at http://contra-cnv.sourceforge.net/ Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

Background The malarial parasite Plasmodium falciparum remains a leading international cause of mortality, with almost a million deaths each year. Determination of protein subcellular localisation remains a challenge in Plasmodium parasites due to their evolutionary distance from wellstudied model organisms, and limited efficiency of appropriate molecular tools. However, abundant large scale systems biology information exist for several Plasmodium species as well as other apicomplexan parasites, including full genomic DNA sequences, plus data sets relating to the transcriptome, protein expression and interactions, polymorphisms and phyletic profiles. To date, most bioinformatic predictors of sub-cellular localisation use sequence information exclusively without consideration for other data sets.

Background Information about chemical compounds and their activity against whole organisms or specific molecular targets is available from the literature or from specialized databases. However, there are few resources that effectively integrate such large chemical datasets with genome data and provide a mechanism to link active compounds to potential target genes. Here, we showcase the integration of chemoinformatic tools for querying chemical datasets and linking chemicals to genes in TDR Targets database (tdrtargets.org), a web accessible resource that integrates a wide range of functional genomic datasets from tropical disease pathogens and provides a ranking mechanism for identifying and prioritising novel therapeutic targets [1].