Distributed computing and data storage in proteomics: many hands make light work, and a stronger memory

Critical steps in an environmental metaproteomics workflow

Environmental metaproteomics is a rapidly advancing field that provides insights into the structure, dynamics, and metabolic activity of microbial communities. As the field is still maturing, it lacks consistent workflows, making it challenging for non-expert researchers to navigate. This review aims to introduce the workflow of environmental metaproteomics. It outlines the standard practices for sample collection, processing, and analysis, and offers strategies to overcome the unique challenges presented by common environmental matrices such as soil, freshwater, marine environments, biofilms, sludge, and symbionts. The review also highlights the bottlenecks in data analysis that are specific to metaproteomics samples and provides suggestions for researchers to obtain high-quality datasets. It includes recent benchmarking studies and descriptions of software packages specifically built for metaproteomics analysis. The article is written without assuming the reader's familiarity with single-organism proteomic workflows, making it accessible to those new to proteomics or mass spectrometry in general. This primer for environmental metaproteomics aims to improve accessibility to this exciting technology and empower researchers to tackle challenging and ambitious research questions. While it is primarily a resource for those new to the field, it should also be useful for established researchers looking to streamline or troubleshoot their metaproteomics experiments.

Unfolding of protein using MoS2/SnS2heterostructure for nanopore-based sequencing

Protein sequencing is crucial for understanding the complex mechanisms driving biological functions. However, proteins are usually folded in their native state and the mechanism of fast protein conformation transitions still remains unclear, which make protein sequencing challenging. Molecular dynamics simulations with accurate force field are now able to observe the entire folding/unfolding process, providing valuable insights into protein folding mechanisms. Given that proteins can be unfolded, nanopore technology shows great potential for protein sequencing. In this study, we proposed to use MoS2/SnS2heterostructures to firstly unfold proteins and then detect them by a nanopore in the heterostructural membrane. All-atom molecular dynamics simulations performed in this work provided rich atomic-level information for a comprehensive understanding of protein unfolding process and mechanism on the MoS2/SnS2heterostructure, it was found that the strong binding of protein to SnS2nanostripe and hydrogen bond breaking were the main reasons for unfolding the protein on the heterostructure. After the protein was fully unfolded, it was restrained on the nanostripe because of the affinity of protein to the SnS2nanostripe. Thus by integrating the proposed unfolding technique with nanopore technology, detection of linear unfolded peptide was realized in this work, allowing for the identification of protein components, which is essential for sequencing proteins in the near future.

Simulation Testbed for Evaluating Distributed Querying and Searching of Mass Spectrometry Big Data in a Network-based Infrastructure

Advance access and reuse mechanisms for large-scale Mass Spectrometry (MS) data are essential for democratizing data for the omics research community and making it adhere to FAIR (Findable, Accessible, Interoperable, Reusable) principles. Although a number of centralized data repositories have been established, they have been limited to search mechanisms that depend on the meta-data associated with these MS datasets. Furthermore, they require constant influx of resources for maintenance. In this paper, we proposed an alternative novel distributed infrastructure for direct MS/MS spectral search. We designed and developed a simulation testbed using concepts from computer networks, queuing theory, and stochastic simulation methods. Results show that a distributed MS search based on raw MS/MS spectra can scale gracefully for up-to 2000 participating nodes, while simultaneously processing queries using the proposed networked infrastructure on the order of milliseconds to a few seconds for up-to a total of fifty billion MS/MS spectra.

Anatomy and evolution of database search engines-a central component of mass spectrometry based proteomic workflows

Sequence database search engines are bioinformatics algorithms that identify peptides from tandem mass spectra using a reference protein sequence database. Two decades of development, notably driven by advances in mass spectrometry, have provided scientists with more than 30 published search engines, each with its own properties. In this review, we present the common paradigm behind the different implementations, and its limitations for modern mass spectrometry datasets. We also detail how the search engines attempt to alleviate these limitations, and provide an overview of the different software frameworks available to the researcher. Finally, we highlight alternative approaches for the identification of proteomic mass spectrometry datasets, either as a replacement for, or as a complement to, sequence database search engines.

ThermoRawFileParser: Modular, Scalable, and Cross-Platform RAW File Conversion

The field of computational proteomics is approaching the big data age, driven both by a continuous growth in the number of samples analyzed per experiment as well as by the growing amount of data obtained in each analytical run. In order to process these large amounts of data, it is increasingly necessary to use elastic compute resources such as Linux-based cluster environments and cloud infrastructures. Unfortunately, the vast majority of cross-platform proteomics tools are not able to operate directly on the proprietary formats generated by the diverse mass spectrometers. Here, we present ThermoRawFileParser, an open-source, cross-platform tool that converts Thermo RAW files into open file formats such as MGF and the HUPO-PSI standard file format mzML. To ensure the broadest possible availability and to increase integration capabilities with popular workflow systems such as Galaxy or Nextflow, we have also built Conda package and BioContainers container around ThermoRawFileParser. In addition, we implemented a user-friendly interface (ThermoRawFileParserGUI) for those users not familiar with command-line tools. Finally, we performed a benchmark of ThermoRawFileParser and msconvert to verify that the converted mzML files contain reliable quantitative results.

SearchGUI: A Highly Adaptable Common Interface for Proteomics Search and de Novo Engines

Mass-spectrometry-based proteomics has become the standard approach for identifying and quantifying proteins. A vital step consists of analyzing experimentally generated mass spectra to identify the underlying peptide sequences for later mapping to the originating proteins. We here present the latest developments in SearchGUI, a common open-source interface for the most frequently used freely available proteomics search and de novo engines that has evolved into a central component in numerous bioinformatics workflows.

MPA Portable: A Stand-Alone Software Package for Analyzing Metaproteome Samples on the Go

Metaproteomics, the mass spectrometry-based analysis of proteins from multispecies samples faces severe challenges concerning data analysis and results interpretation. To overcome these shortcomings, we here introduce the MetaProteomeAnalyzer (MPA) Portable software. In contrast to the original server-based MPA application, this newly developed tool no longer requires computational expertise for installation and is now independent of any relational database system. In addition, MPA Portable now supports state-of-the-art database search engines and a convenient command line interface for high-performance data processing tasks. While search engine results can easily be combined to increase the protein identification yield, an additional two-step workflow is implemented to provide sufficient analysis resolution for further postprocessing steps, such as protein grouping as well as taxonomic and functional annotation. Our new application has been developed with a focus on intuitive usability, adherence to data standards, and adaptation to Web-based workflow platforms. The open source software package can be found at https://github.com/compomics/meta-proteome-analyzer.

Database Search Engines: Paradigms, Challenges and Solutions

The first step in identifying proteins from mass spectrometry based shotgun proteomics data is to infer peptides from tandem mass spectra, a task generally achieved using database search engines. In this chapter, the basic principles of database search engines are introduced with a focus on open source software, and the use of database search engines is demonstrated using the freely available SearchGUI interface. This chapter also discusses how to tackle general issues related to sequence database searching and shows how to minimize their impact.

Future opportunities and trends for e-infrastructures and life sciences: going beyond the grid to enable life science data analysis

With the increasingly rapid growth of data in life sciences we are witnessing a major transition in the way research is conducted, from hypothesis-driven studies to data-driven simulations of whole systems. Such approaches necessitate the use of large-scale computational resources and e-infrastructures, such as the European Grid Infrastructure (EGI). EGI, one of key the enablers of the digital European Research Area, is a federation of resource providers set up to deliver sustainable, integrated and secure computing services to European researchers and their international partners. Here we aim to provide the state of the art of Grid/Cloud computing in EU research as viewed from within the field of life sciences, focusing on key infrastructures and projects within the life sciences community. Rather than focusing purely on the technical aspects underlying the currently provided solutions, we outline the design aspects and key characteristics that can be identified across major research approaches. Overall, we aim to provide significant insights into the road ahead by establishing ever-strengthening connections between EGI as a whole and the life sciences community.

Distributed and interactive visual analysis of omics data

The amount of publicly shared proteomics data has grown exponentially over the last decade as the solutions for sharing and storing the data have improved. However, the use of the data is often limited by the manner of which it is made available. There are two main approaches: download and inspect the proteomics data locally, or interact with the data via one or more web pages. The first is limited by having to download the data and thus requires local computational skills and resources, while the latter most often is limited in terms of interactivity and the analysis options available. A solution is to develop web-based systems supporting distributed and fully interactive visual analysis of proteomics data. The use of a distributed architecture makes it possible to perform the computational analysis at the server, while the results of the analysis can be displayed via a web browser without the need to download the whole dataset. Here the challenges related to developing such systems for omics data will be discussed. Especially how this allows for multiple connected interactive visual displays of omics dataset in a web-based setting, and the benefits this provide for computational analysis of proteomics data.This article is part of a Special Issue entitled: Computational Proteomics.

Fourteen years of plant proteomics reflected in Proteomics: moving from model species and 2DE-based approaches to orphan species and gel-free platforms

In this article, the topic of plant proteomics is reviewed based on related papers published in the journal Proteomics since publication of the first issue in 2001. In total, around 300 original papers and 41 reviews published in Proteomics between 2000 and 2014 have been surveyed. Our main objective for this review is to help bridge the gap between plant biologists and proteomics technologists, two often very separate groups. Over the past years a number of reviews on plant proteomics have been published . To avoid repetition we have focused on more recent literature published after 2010, and have chosen to rather make continuous reference to older publications. The use of the latest proteomics techniques and their integration with other approaches in the "systems biology" direction are discussed more in detail. Finally we comment on the recent history, state of the art, and future directions of plant proteomics, using publications in Proteomics to illustrate the progress in the field. The review is organized into two major blocks, the first devoted to provide an overview of experimental systems (plants, plant organs, biological processes) and the second one to the methodology.

Viewing the proteome: how to visualize proteomics data?

Proteomics has become one of the main approaches for analyzing and understanding biological systems. Yet similar to other high-throughput analysis methods, the presentation of the large amounts of obtained data in easily interpretable ways remains challenging. In this review, we present an overview of the different ways in which proteomics software supports the visualization and interpretation of proteomics data. The unique challenges and current solutions for visualizing the different aspects of proteomics data, from acquired spectra via protein identification and quantification to pathway analysis, are discussed, and examples of the most useful visualization approaches are highlighted. Finally, we offer our ideas about future directions for proteomics data visualization.

An update on LNCipedia: a database for annotated human lncRNA sequences

The human genome is pervasively transcribed, producing thousands of non-coding RNA transcripts. The majority of these transcripts are long non-coding RNAs (lncRNAs) and novel lncRNA genes are being identified at rapid pace. To streamline these efforts, we created LNCipedia, an online repository of lncRNA transcripts and annotation. Here, we present LNCipedia 3.0 (http://www.lncipedia.org), the latest version of the publicly available human lncRNA database. Compared to the previous version of LNCipedia, the database grew over five times in size, gaining over 90,000 new lncRNA transcripts. Assessment of the protein-coding potential of LNCipedia entries is improved with state-of-the art methods that include large-scale reprocessing of publicly available proteomics data. As a result, a high-confidence set of lncRNA transcripts with low coding potential is defined and made available for download. In addition, a tool to assess lncRNA gene conservation between human, mouse and zebrafish has been implemented.