BioContainers Registry: Searching Bioinformatics and Proteomics Tools, Packages, and Containers

Defining the boundaries: challenges and advances in identifying cells in microscopy images

Segmentation, or the outlining of objects within images, is a critical step in the measurement and analysis of cells within microscopy images. While improvements continue to be made in tools that rely on classical methods for segmentation, deep learning-based tools increasingly dominate advances in the technology. Specialist models such as Cellpose continue to improve in accuracy and user-friendliness, and segmentation challenges such as the Multi-Modality Cell Segmentation Challenge continue to push innovation in accuracy across widely varying test data as well as efficiency and usability. Increased attention on documentation, sharing, and evaluation standards is leading to increased user-friendliness and acceleration toward the goal of a truly universal method.

WOMBAT-P: Benchmarking Label-Free Proteomics Data Analysis Workflows

The inherent diversity of approaches in proteomics research has led to a wide range of software solutions for data analysis. These software solutions encompass multiple tools, each employing different algorithms for various tasks such as peptide-spectrum matching, protein inference, quantification, statistical analysis, and visualization. To enable an unbiased comparison of commonly used bottom-up label-free proteomics workflows, we introduce WOMBAT-P, a versatile platform designed for automated benchmarking and comparison. WOMBAT-P simplifies the processing of public data by utilizing the sample and data relationship format for proteomics (SDRF-Proteomics) as input. This feature streamlines the analysis of annotated local or public ProteomeXchange data sets, promoting efficient comparisons among diverse outputs. Through an evaluation using experimental ground truth data and a realistic biological data set, we uncover significant disparities and a limited overlap in the quantified proteins. WOMBAT-P not only enables rapid execution and seamless comparison of workflows but also provides valuable insights into the capabilities of different software solutions. These benchmarking metrics are a valuable resource for researchers in selecting the most suitable workflow for their specific data sets. The modular architecture of WOMBAT-P promotes extensibility and customization. The software is available at https://github.com/wombat-p/WOMBAT-Pipelines.

Challenges and opportunities in sharing microbiome data and analyses

Microbiome data, metadata and analytical workflows have become 'big' in terms of volume and complexity. Although the infrastructure and technologies to share data have been established, the interdisciplinary and multi-omic nature of the field can make resources difficult to identify and use. Following best practices for data deposition requires substantial effort, with sometimes little obvious reward. Gaps remain where microbiome-specific resources for data sharing or reproducibility do not yet exist. We outline available best practices, challenges to their adoption and opportunities in data sharing in microbiome research. We showcase examples of best practices and advocate for their enforcement and incentivization for data sharing. This includes recognition of data curation and sharing endeavours by individuals, institutions, journals and funders. Opportunities for progress include enabling microbiome-specific databases to incorporate future methods for data analysis, integration and reuse.

Accelerating bioinformatics implementation in public health

We have adopted an open bioinformatics ecosystem to address the challenges of bioinformatics implementation in public health laboratories (PHLs). Bioinformatics implementation for public health requires practitioners to undertake standardized bioinformatic analyses and generate reproducible, validated and auditable results. It is essential that data storage and analysis are scalable, portable and secure, and that implementation of bioinformatics fits within the operational constraints of the laboratory. We address these requirements using Terra, a web-based data analysis platform with a graphical user interface connecting users to bioinformatics analyses without the use of code. We have developed bioinformatics workflows for use with Terra that specifically meet the needs of public health practitioners. These Theiagen workflows perform genome assembly, quality control, and characterization, as well as construction of phylogeny for insights into genomic epidemiology. Additonally, these workflows use open-source containerized software and the WDL workflow language to ensure standardization and interoperability with other bioinformatics solutions, whilst being adaptable by the user. They are all open source and publicly available in Dockstore with the version-controlled code available in public GitHub repositories. They have been written to generate outputs in standardized file formats to allow for further downstream analysis and visualization with separate genomic epidemiology software. Testament to this solution meeting the requirements for bioinformatic implementation in public health, Theiagen workflows have collectively been used for over 5 million sample analyses in the last 2 years by over 90 public health laboratories in at least 40 different countries. Continued adoption of technological innovations and development of further workflows will ensure that this ecosystem continues to benefit PHLs.

Identification of lthB, a Gene Encoding a Putative Glycosyltransferase Family 8 Protein Required for Leptothrix Sheath Formation

The bacterium Leptothrix cholodnii generates cell chains encased in sheaths that are composed of woven nanofibrils. The nanofibrils are mainly composed of glycoconjugate repeats, and several glycosyltransferases (GTs) are required for its biosynthesis. However, only one GT (LthA) has been identified to date. In this study, we screened spontaneous variants of L. cholodnii SP6 to find those that form smooth colonies, which is one of the characteristics of sheathless variants. Genomic DNA sequencing of an isolated variant revealed an insertion in the locus Lcho_0972, which encodes a putative GT family 8 protein. We thus designated this protein LthB and characterized it using deletion mutants and antibodies. LthB localized adjacent to the cell envelope. ΔlthB cell chains were nanofibril free and thus sheathless, indicating that LthB is involved in nanofibril biosynthesis. Unlike the ΔlthA mutant and the wild-type strain, which often generate planktonic cells, most ΔlthB organisms presented as long cell chains under static conditions, resulting in deficient pellicle formation, which requires motile planktonic cells. These results imply that sheaths are not required for elongation of cell chains. Finally, calcium depletion, which induces cell chain breakage due to sheath loss, abrogated the expression of LthA, but not LthB, suggesting that these GTs cooperatively participate in glycoconjugate biosynthesis under different signaling controls. IMPORTANCE In recent years, the regulation of cell chain elongation of filamentous bacteria via extracellular signals has attracted attention as a potential strategy to prevent clogging of water distribution systems and filamentous bulking of activated sludge in industrial settings. However, a fundamental understanding of the ecology of filamentous bacteria remains elusive. Since sheath formation is associated with cell chain elongation in most of these bacteria, the molecular mechanisms underlying nanofibril sheath formation, including the intracellular signaling cascade in response to extracellular stimuli, must be elucidated. Here, we isolated a sheathless variant of L. cholodnii SP6 and thus identified a novel glycosyltransferase, LthB. Although mutants with deletions of lthA, encoding another GT, and lthB were both defective for nanofibril formation, they exhibited different phenotypes of cell chain elongation and pellicle formation. Moreover, LthA expression, but not LthB expression, was influenced by extracellular calcium, which is known to affect nanofibril formation, indicating the functional diversities of LthA and LthB. Such molecular insights are critical for a better understanding of ecology of filamentous bacteria, which, in turn, can be used to improve strategies to control filamentous bacteria in industrial facilities.

SynBioTools: a one-stop facility for searching and selecting synthetic biology tools

BACKGROUND

The rapid development of synthetic biology relies heavily on the use of databases and computational tools, which are also developing rapidly. While many tool registries have been created to facilitate tool retrieval, sharing, and reuse, no relatively comprehensive tool registry or catalog addresses all aspects of synthetic biology.

RESULTS

We constructed SynBioTools, a comprehensive collection of synthetic biology databases, computational tools, and experimental methods, as a one-stop facility for searching and selecting synthetic biology tools. SynBioTools includes databases, computational tools, and methods extracted from reviews via SCIentific Table Extraction, a scientific table-extraction tool that we built. Approximately 57% of the resources that we located and included in SynBioTools are not mentioned in bio.tools, the dominant tool registry. To improve users' understanding of the tools and to enable them to make better choices, the tools are grouped into nine modules (each with subdivisions) based on their potential biosynthetic applications. Detailed comparisons of similar tools in every classification are included. The URLs, descriptions, source references, and the number of citations of the tools are also integrated into the system.

CONCLUSIONS

SynBioTools is freely available at https://synbiotools.lifesynther.com/ . It provides end-users and developers with a useful resource of categorized synthetic biology databases, tools, and methods to facilitate tool retrieval and selection.

Trans-Proteomic Pipeline: Robust Mass Spectrometry-Based Proteomics Data Analysis Suite

The Trans-Proteomic Pipeline (TPP) mass spectrometry data analysis suite has been in continual development and refinement since its first tools, PeptideProphet and ProteinProphet, were published 20 years ago. The current release provides a large complement of tools for spectrum processing, spectrum searching, search validation, abundance computation, protein inference, and more. Many of the tools include machine-learning modeling to extract the most information from data sets and build robust statistical models to compute the probabilities that derived information is correct. Here we present the latest information on the many TPP tools, and how TPP can be deployed on various platforms from personal Windows laptops to Linux clusters and expansive cloud computing environments. We describe tutorials on how to use TPP in a variety of ways and describe synergistic projects that leverage TPP. We conclude with plans for continued development of TPP.

Bioinformatics analysis of the potential regulatory mechanisms of renal fibrosis and the screening and identification of factors related to human renal fibrosis

Background

This paper aimed to identify the key genes and potential mechanisms of renal fibrosis, and provide methods of evaluation and new ideas for the early diagnosis and treatment of renal fibrosis.

Methods

The GSE102515 dataset was searched from the Gene Expression Omnibus (GEO) database was searched, the differential genes were screened out, and the down-regulated and up-regulated genes were identified. Enrichment analysis of differential genes in the development of renal fibrosis was carried out using the DAVID database, differential genes were analyzed using the STRING database, and Cytoscape software was used for visual processing.

Results

Eighteen up-regulated genes and ten down-regulated genes were screened. Differential genes are mainly involved in the integrin-mediated signaling pathway and mitotic sister chromatid binding, etc. We found that the molecular functions (MFs) of the differential genes are phospholipid binding and regulatory region DNA binding, etc. Moreover, the cellular components (CCs) of the differential genes are mainly related to low-density lipoprotein (LDL) particles and nuclei. Screening revealed that ADM, ARRB1, AVPR2, CCR1, MTNR1A, PTH, and S1PR2 were core genes in the interaction network of renal fibrosis risk-related proteins.

Conclusions

In this study, the differential genes in the occurrence of renal fibrosis were screened out via dataset analysis. It was found that ADM, ARRB1, AVPR2, CCR1, MTNR1A, PTH, and S1PR2 may be important participants in the development of renal fibrosis, which provides analytical support for the identification of valuable markers of renal fibrosis.

Phage Genes Induce Quorum Sensing Signal Release through Membrane Vesicle Formation

Membrane vesicles (MVs) released from the bacterium Paracoccus denitrificans Pd1222 are enriched with the quorum sensing (QS) signaling molecule N-hexadecanoyl-l-homoserine lactone (C16-HSL). However, the biogenesis of MVs in Pd1222 remains unclear. Investigations on MV formation are crucial for obtaining a more detailed understanding of the dynamics of MV-assisted signaling. In the present study, live-cell imaging showed that P. denitrificans Pd1222 produced MVs through cell lysis under DNA-damaging conditions. DNA sequencing of MVs and a transcriptome ana-lysis of cells indicated that the expression of a prophage region was up-regulated at the onset of MV formation under DNA-damaging conditions. A further sequence ana-lysis identified a putative endolysin (Pden_0381) and holin (Pden_0382) in the prophage region. The expression of these genes was regulated by RecA. Using gene knockout mutants, we showed that prophage-encoded endolysin was critical for MV formation by P. denitrificans Pd1222 under DNA-damaging conditions. MV triggering by endolysin was dependent on the putative holin, which presumably transported endolysin to the periplasmic space. C16-HSL quantification revealed that more signals were released into the milieu as a consequence of the effects of endolysin. Using a QS reporter strain, we found that the QS response in P. denitrificans was stimulated by inducing the expression of endolysin. Collectively, these results provide novel insights into the mechanisms by which a bacterial cell-to-cell communication system is manipulated by phage genes.

The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences

The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world's largest data repository of mass spectrometry-based proteomics data. PRIDE is one of the founding members of the global ProteomeXchange (PX) consortium and an ELIXIR core data resource. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2019. The number of submitted datasets to PRIDE Archive (the archival component of PRIDE) has reached on average around 500 datasets per month during 2021. In addition to continuous improvements in PRIDE Archive data pipelines and infrastructure, the PRIDE Spectra Archive has been developed to provide direct access to the submitted mass spectra using Universal Spectrum Identifiers. As a key point, the file format MAGE-TAB for proteomics has been developed to enable the improvement of sample metadata annotation. Additionally, the resource PRIDE Peptidome provides access to aggregated peptide/protein evidences across PRIDE Archive. Furthermore, we will describe how PRIDE has increased its efforts to reuse and disseminate high-quality proteomics data into other added-value resources such as UniProt, Ensembl and Expression Atlas.

FA-nf: A Functional Annotation Pipeline for Proteins from Non-Model Organisms Implemented in Nextflow

Functional annotation allows adding biologically relevant information to predicted features in genomic sequences, and it is, therefore, an important procedure of any de novo genome sequencing project. It is also useful for proofreading and improving gene structural annotation. Here, we introduce FA-nf, a pipeline implemented in Nextflow, a versatile computational workflow management engine. The pipeline integrates different annotation approaches, such as NCBI BLAST+, DIAMOND, InterProScan, and KEGG. It starts from a protein sequence FASTA file and, optionally, a structural annotation file in GFF format, and produces several files, such as GO assignments, output summaries of the abovementioned programs and final annotation reports. The pipeline can be broken easily into smaller processes for the purpose of parallelization and easily deployed in a Linux computational environment, thanks to software containerization, thus helping to ensure full reproducibility.

A proteomics sample metadata representation for multiomics integration and big data analysis

The amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets.

Perspectives on automated composition of workflows in the life sciences

Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the "big picture" of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future.