A single-cell RNA-sequencing training and analysis suite using the Galaxy framework

Comparative Genomics and Epigenomics of Transcriptional Regulation

Transcriptional regulation in response to diverse physiological cues involves complicated biological processes. Recent initiatives that leverage whole genome sequencing and annotation of regulatory elements significantly contribute to our understanding of transcriptional gene regulation. Advances in the data sets available for comparative genomics and epigenomics can identify evolutionarily constrained regulatory variants and shed light on noncoding elements that influence transcription in different tissues and developmental stages across species. Most epigenomic data, however, are generated from healthy subjects at specific developmental stages. To bridge the genotype-phenotype gap, future research should focus on generating multidimensional epigenomic data under diverse physiological conditions. Farm animal species offer advantages in terms of feasibility, cost, and experimental design for such integrative analyses in comparison to humans. Deep learning modeling and cutting-edge technologies in sequencing and functional screening and validation also provide great promise for better understanding transcriptional regulation in this dynamic field.

Building a FAIR data ecosystem for incorporating single-cell transcriptomics data into agricultural genome to phenome research

Introduction

The agriculture genomics community has numerous data submission standards available, but the standards for describing and storing single-cell (SC, e.g., scRNA- seq) data are comparatively underdeveloped.

Methods

To bridge this gap, we leveraged recent advancements in human genomics infrastructure, such as the integration of the Human Cell Atlas Data Portal with Terra, a secure, scalable, open-source platform for biomedical researchers to access data, run analysis tools, and collaborate. In parallel, the Single Cell Expression Atlas at EMBL-EBI offers a comprehensive data ingestion portal for high-throughput sequencing datasets, including plants, protists, and animals (including humans). Developing data tools connecting these resources would offer significant advantages to the agricultural genomics community. The FAANG data portal at EMBL-EBI emphasizes delivering rich metadata and highly accurate and reliable annotation of farmed animals but is not computationally linked to either of these resources.

Results

Herein, we describe a pilot-scale project that determines whether the current FAANG metadata standards for livestock can be used to ingest scRNA-seq datasets into Terra in a manner consistent with HCA Data Portal standards. Importantly, rich scRNA-seq metadata can now be brokered through the FAANG data portal using a semi-automated process, thereby avoiding the need for substantial expert curation. We have further extended the functionality of this tool so that validated and ingested SC files within the HCA Data Portal are transferred to Terra for further analysis. In addition, we verified data ingestion into Terra, hosted on Azure, and demonstrated the use of a workflow to analyze the first ingested porcine scRNA-seq dataset. Additionally, we have also developed prototype tools to visualize the output of scRNA-seq analyses on genome browsers to compare gene expression patterns across tissues and cell populations. This JBrowse tool now features distinct tracks, showcasing PBMC scRNA-seq alongside two bulk RNA-seq experiments.

Discussion

We intend to further build upon these existing tools to construct a scientist-friendly data resource and analytical ecosystem based on Findable, Accessible, Interoperable, and Reusable (FAIR) SC principles to facilitate SC-level genomic analysis through data ingestion, storage, retrieval, re-use, visualization, and comparative annotation across agricultural species.

scDAPP: a comprehensive single-cell transcriptomics analysis pipeline optimized for cross-group comparison

Single-cell transcriptomics profiling has increasingly been used to evaluate cross-group (or condition) differences in cell population and cell-type gene expression. This often leads to large datasets with complex experimental designs that need advanced comparative analysis. Concurrently, bioinformatics software and analytic approaches also become more diverse and constantly undergo improvement. Thus, there is an increased need for automated and standardized data processing and analysis pipelines, which should be efficient and flexible too. To address these, we develop the single-cell Differential Analysis and Processing Pipeline (scDAPP), a R-based workflow for comparative analysis of single cell (or nucleus) transcriptomic data between two or more groups and at the levels of single cells or 'pseudobulking' samples. The pipeline automates many steps of pre-processing using data-learnt parameters, uses previously benchmarked software, and generates comprehensive intermediate data and final results that are valuable for both beginners and experts of scRNA-seq analysis. Moreover, the analytic reports, augmented by extensive data visualization, increase the transparency of computational analysis and parameter choices, while facilitate users to go seamlessly from raw data to biological interpretation. scDAPP is freely available under the MIT license, with source code, documentation and sample data at the GitHub (https://github.com/bioinfoDZ/scDAPP).

A Galaxy of informatics resources for MS-based proteomics

INTRODUCTION

Continuous advances in mass spectrometry (MS) technologies have enabled deeper and more reproducible proteome characterization and a better understanding of biological systems when integrated with other 'omics data. Bioinformatic resources meeting the analysis requirements of increasingly complex MS-based proteomic data and associated multi-omic data are critically needed. These requirements included availability of software that would span diverse types of analyses, scalability for large-scale, compute-intensive applications, and mechanisms to ease adoption of the software.

AREAS COVERED

The Galaxy ecosystem meets these requirements by offering a multitude of open-source tools for MS-based proteomics analyses and applications, all in an adaptable, scalable, and accessible computing environment. A thriving global community maintains these software and associated training resources to empower researcher-driven analyses.

EXPERT OPINION

The community-supported Galaxy ecosystem remains a crucial contributor to basic biological and clinical studies using MS-based proteomics. In addition to the current status of Galaxy-based resources, we describe ongoing developments for meeting emerging challenges in MS-based proteomic informatics. We hope this review will catalyze increased use of Galaxy by researchers employing MS-based proteomics and inspire software developers to join the community and implement new tools, workflows, and associated training content that will add further value to this already rich ecosystem.

A compilation of bioinformatic approaches to identify novel downstream targets for the detection and prophylaxis of cancer

The paradigm of cancer genomics has been radically changed by the development in next-generation sequencing (NGS) technologies making it possible to envisage individualized treatment based on tumor and stromal cells genome in a clinical setting within a short timeframe. The abundance of data has led to new avenues for studying coordinated alterations that impair biological processes, which in turn has increased the demand for bioinformatic tools for pathway analysis. While most of this work has been concentrated on optimizing certain algorithms to obtain quicker and more accurate results. Large volumes of these existing algorithm-based data are difficult for the biologists and clinicians to access, download and reanalyze them. In the present study, we have listed the bioinformatics algorithms and user-friendly graphical user interface (GUI) tools that enable code-independent analysis of big data without compromising the quality and time. We have also described the advantages and drawbacks of each of these platforms. Additionally, we emphasize the importance of creating new, more user-friendly solutions to provide better access to open data and talk about relevant problems like data sharing and patient privacy.

Delayed boosting improves human antigen-specific Ig and B cell responses to the RH5.1/AS01B malaria vaccine

Modifications to vaccine delivery that increase serum antibody longevity are of great interest for maximizing efficacy. We have previously shown that a delayed fractional (DFx) dosing schedule (0-1-6 month) - using AS01B-adjuvanted RH5.1 malaria antigen - substantially improves serum IgG durability as compared with monthly dosing (0-1-2 month; NCT02927145). However, the underlying mechanism and whether there are wider immunological changes with DFx dosing were unclear. Here, PfRH5-specific Ig and B cell responses were analyzed in depth through standardized ELISAs, flow cytometry, systems serology, and single-cell RNA-Seq (scRNA-Seq). Data indicate that DFx dosing increases the magnitude and durability of circulating PfRH5-specific B cells and serum IgG1. At the peak antibody magnitude, DFx dosing was distinguished by a systems serology feature set comprising increased FcRn binding, IgG avidity, and proportion of G2B and G2S2F IgG Fc glycans, alongside decreased IgG3, antibody-dependent complement deposition, and proportion of G1S1F IgG Fc glycan. Concomitantly, scRNA-Seq data show a higher CDR3 percentage of mutation from germline and decreased plasma cell gene expression in circulating PfRH5-specific B cells. Our data, therefore, reveal a profound impact of DFx dosing on the humoral response and suggest plausible mechanisms that could enhance antibody longevity, including improved FcRn binding by serum Ig and a potential shift in the underlying cellular response from circulating short-lived plasma cells to nonperipheral long-lived plasma cells.

Toward a data infrastructure for the Plant Cell Atlas

We review how a data infrastructure for the Plant Cell Atlas might be built using existing infrastructure and platforms. The Human Cell Atlas has developed an extensive infrastructure for human and mouse single cell data, while the European Bioinformatics Institute has developed a Single Cell Expression Atlas, that currently houses several plant data sets. We discuss issues related to appropriate ontologies for describing a plant single cell experiment. We imagine how such an infrastructure will enable biologists and data scientists to glean new insights into plant biology in the coming decades, as long as such data are made accessible to the community in an open manner.

Galaxy Training: A powerful framework for teaching!

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.

From bench to bedside: Single-cell analysis for cancer immunotherapy

Single-cell technologies are emerging as powerful tools for cancer research. These technologies characterize the molecular state of each cell within a tumor, enabling new exploration of tumor heterogeneity, microenvironment cell-type composition, and cell state transitions that affect therapeutic response, particularly in the context of immunotherapy. Analyzing clinical samples has great promise for precision medicine but is technically challenging. Successfully identifying predictors of response requires well-coordinated, multi-disciplinary teams to ensure adequate sample processing for high-quality data generation and computational analysis for data interpretation. Here, we review current approaches to sample processing and computational analysis regarding their application to translational cancer immunotherapy research.