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.

IonFlow: a galaxy tool for the analysis of ionomics data sets

INTRODUCTION

Inductively coupled plasma mass spectrometry (ICP-MS) experiments generate complex multi-dimensional data sets that require specialist data analysis tools.

OBJECTIVE

Here we describe tools to facilitate analysis of the ionome composed of high-throughput elemental profiling data.

METHODS

IonFlow is a Galaxy tool written in R for ionomics data analysis and is freely accessible at https://github.com/wanchanglin/ionflow . It is designed as a pipeline that can process raw data to enable exploration and interpretation using multivariate statistical techniques and network-based algorithms, including principal components analysis, hierarchical clustering, relevance network extraction and analysis, and gene set enrichment analysis.

RESULTS AND CONCLUSION

The pipeline is described and tested on two benchmark data sets of the haploid S. Cerevisiae ionome and of the human HeLa cell ionome.

Galaxy InteractoMIX: An Integrated Computational Platform for the Study of Protein-Protein Interaction Data

Protein interactions play a crucial role among the different functions of a cell and are central to our understanding of cellular processes both in health and disease. Here we present Galaxy InteractoMIX (http://galaxy.interactomix.com), a platform composed of 13 different computational tools each addressing specific aspects of the study of protein-protein interactions, ranging from large-scale cross-species protein-wide interactomes to atomic resolution level of protein complexes. Galaxy InteractoMIX provides an intuitive interface where users can retrieve consolidated interactomics data distributed across several databases or uncover links between diseases and genes by analyzing the interactomes underlying these diseases. The platform makes possible large-scale prediction and curation protein interactions using the conservation of motifs, interology, or presence or absence of key sequence signatures. The range of structure-based tools includes modeling and analysis of protein complexes, delineation of interfaces and the modeling of peptides acting as inhibitors of protein-protein interactions. Galaxy InteractoMIX includes a range of ready-to-use workflows to run complex analyses requiring minimal intervention by users. The potential range of applications of the platform covers different aspects of life science, biomedicine, biotechnology and drug discovery where protein associations are studied.

EpiMOLAS: an intuitive web-based framework for genome-wide DNA methylation analysis

BACKGROUND

DNA methylation is a crucial epigenomic mechanism in various biological processes. Using whole-genome bisulfite sequencing (WGBS) technology, methylated cytosine sites can be revealed at the single nucleotide level. However, the WGBS data analysis process is usually complicated and challenging.

RESULTS

To alleviate the associated difficulties, we integrated the WGBS data processing steps and downstream analysis into a two-phase approach. First, we set up the required tools in Galaxy and developed workflows to calculate the methylation level from raw WGBS data and generate a methylation status summary, the mtable. This computation environment is wrapped into the Docker container image DocMethyl, which allows users to rapidly deploy an executable environment without tedious software installation and library dependency problems. Next, the mtable files were uploaded to the web server EpiMOLAS_web to link with the gene annotation databases that enable rapid data retrieval and analyses.

CONCLUSION

To our knowledge, the EpiMOLAS framework, consisting of DocMethyl and EpiMOLAS_web, is the first approach to include containerization technology and a web-based system for WGBS data analysis from raw data processing to downstream analysis. EpiMOLAS will help users cope with their WGBS data and also conduct reproducible analyses of publicly available data, thereby gaining insights into the mechanisms underlying complex biological phenomenon. The Galaxy Docker image DocMethyl is available at https://hub.docker.com/r/lsbnb/docmethyl/. EpiMOLAS_web is publicly accessible at http://symbiosis.iis.sinica.edu.tw/epimolas/.

Graphical Workflow System for Modification Calling by Machine Learning of Reverse Transcription Signatures

Modification mapping from cDNA data has become a tremendously important approach in epitranscriptomics. So-called reverse transcription signatures in cDNA contain information on the position and nature of their causative RNA modifications. Data mining of, e.g. Illumina-based high-throughput sequencing data, is therefore fast growing in importance, and the field is still lacking effective tools. Here we present a versatile user-friendly graphical workflow system for modification calling based on machine learning. The workflow commences with a principal module for trimming, mapping, and postprocessing. The latter includes a quantification of mismatch and arrest rates with single-nucleotide resolution across the mapped transcriptome. Further downstream modules include tools for visualization, machine learning, and modification calling. From the machine-learning module, quality assessment parameters are provided to gauge the suitability of the initial dataset for effective machine learning and modification calling. This output is useful to improve the experimental parameters for library preparation and sequencing. In summary, the automation of the bioinformatics workflow allows a faster turnaround of the optimization cycles in modification calling.

A Computational Pipeline for the Extraction of Actionable Biological Information From NGS-Phage Display Experiments

Phage Display is a powerful method for the identification of peptide binding to targets of variable complexities and tissues, from unique molecules to the internal surfaces of vessels of living organisms. Particularly for in vivo screenings, the resulting repertoires can be very complex and difficult to study with traditional approaches. Next Generation Sequencing (NGS) opened the possibility to acquire high resolution overviews of such repertoires and thus facilitates the identification of binders of interest. Additionally, the ever-increasing amount of available genome/proteome information became satisfactory regarding the identification of putative mimicked proteins, due to the large scale on which partial sequence homology is assessed. However, the subsequent production of massive data stresses the need for high-performance computational approaches in order to perform standardized and insightful molecular network analysis. Systems-level analysis is essential for efficient resolution of the underlying molecular complexity and the extraction of actionable interpretation, in terms of systemic biological processes and pathways that are systematically perturbed. In this work we introduce PepSimili, an integrated workflow tool, which performs mapping of massive peptide repertoires on whole proteomes and delivers a streamlined, systems-level biological interpretation. The tool employs modules for modeling and filtering of background noise due to random mappings and amplifies the biologically meaningful signal through coupling with BioInfoMiner, a systems interpretation tool that employs graph-theoretic methods for prioritization of systemic processes and corresponding driver genes. The current implementation exploits the Galaxy environment and is available online. A case study using public data is presented, with and without a control selection.

Disseminating Metaproteomic Informatics Capabilities and Knowledge Using the Galaxy-P Framework

The impact of microbial communities, also known as the microbiome, on human health and the environment is receiving increased attention. Studying translated gene products (proteins) and comparing metaproteomic profiles may elucidate how microbiomes respond to specific environmental stimuli, and interact with host organisms. Characterizing proteins expressed by a complex microbiome and interpreting their functional signature requires sophisticated informatics tools and workflows tailored to metaproteomics. Additionally, there is a need to disseminate these informatics resources to researchers undertaking metaproteomic studies, who could use them to make new and important discoveries in microbiome research. The Galaxy for proteomics platform (Galaxy-P) offers an open source, web-based bioinformatics platform for disseminating metaproteomics software and workflows. Within this platform, we have developed easily-accessible and documented metaproteomic software tools and workflows aimed at training researchers in their operation and disseminating the tools for more widespread use. The modular workflows encompass the core requirements of metaproteomic informatics: (a) database generation; (b) peptide spectral matching; (c) taxonomic analysis and (d) functional analysis. Much of the software available via the Galaxy-P platform was selected, packaged and deployed through an online metaproteomics "Contribution Fest" undertaken by a unique consortium of expert software developers and users from the metaproteomics research community, who have co-authored this manuscript. These resources are documented on GitHub and freely available through the Galaxy Toolshed, as well as a publicly accessible metaproteomics gateway Galaxy instance. These documented workflows are well suited for the training of novice metaproteomics researchers, through online resources such as the Galaxy Training Network, as well as hands-on training workshops. Here, we describe the metaproteomics tools available within these Galaxy-based resources, as well as the process by which they were selected and implemented in our community-based work. We hope this description will increase access to and utilization of metaproteomics tools, as well as offer a framework for continued community-based development and dissemination of cutting edge metaproteomics software.