The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world’s largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. 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 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.

/pdf/the-pride-database-and-related-tools-and-resources-in-2019-54q3h1hfre.pdf

The PRIDE database and related tools and resources in 2019: improving support for quantification data.

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.

http://academic.oup.com/nar/advance-article-pdf/doi/10.1093/nar/gkab1038/41031430/gkab1038.pdf

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

CellProfiler has enabled the scientific research community to create flexible, modular image analysis pipelines since its release in 2005. Here, we describe CellProfiler 3.0, a new version of the software supporting both whole-volume and plane-wise analysis of three-dimensional (3D) image stacks, increasingly common in biomedical research. CellProfiler's infrastructure is greatly improved, and we provide a protocol for cloud-based, large-scale image processing. New plugins enable running pretrained deep learning models on images. Designed by and for biologists, CellProfiler equips researchers with powerful computational tools via a well-documented user interface, empowering biologists in all fields to create quantitative, reproducible image analysis workflows.

/pdf/cellprofiler-3-0-next-generation-image-processing-for-13cr4raa4t.pdf

CellProfiler 3.0: Next-generation image processing for biology.

The metabolome, the collection of small-molecule chemical entities involved in metabolism, has traditionally been studied with the aim of identifying biomarkers in the diagnosis and prediction of disease. However, the value of metabolome analysis (metabolomics) has been redefined from a simple biomarker identification tool to a technology for the discovery of active drivers of biological processes. It is now clear that the metabolome affects cellular physiology through modulation of other 'omics' levels, including the genome, epigenome, transcriptome and proteome. In this Review, we focus on recent progress in using metabolomics to understand how the metabolome influences other omics and, by extension, to reveal the active role of metabolites in physiology and disease. This concept of utilizing metabolomics to perform activity screens to identify biologically active metabolites - which we term activity metabolomics - is already having a broad impact on biology.

/pdf/identification-of-bioactive-metabolites-using-activity-xqhgb7kqo1.pdf

Identification of bioactive metabolites using activity metabolomics

Three-Way Multidimensional Scaling

KNIME for reproducible cross-domain analysis of life science data

OpenMS - A platform for reproducible analysis of mass spectrometry data

In this paper we show that diversity-driven widening, the parallel exploration of the model space with focus on developing diverse models, can improve hierarchical agglomerative clustering. Depending on the selected linkage method, the model that is found through the widened search achieves a better silhouette coefficient than its sequentially built counterpart.

/pdf/diversity-driven-widening-of-hierarchical-agglomerative-4gltvnnxok.pdf

Diversity-Driven Widening of Hierarchical Agglomerative Clustering

When using a greedy algorithm for finding a model, as is the case in many data mining algorithms, there is a risk of getting caught in local extrema, i.e., suboptimal solutions. Widening is a technique for enhancing greedy algorithms by using parallel resources to broaden the search in the model space. The most important component of widening is the selector, a function that chooses the next models to refine. This selector ideally enforces diversity within the selected set of models in order to ensure that parallel workers explore sufficiently different parts of the model space and do not end up mimicking a simple beam search. Previous publications have shown that this works well for problems with a suitable distance measure for the models, but if no such measure is available, applying widening is challenging. In addition these approaches require extensive, sequential computations for diverse subset selection, making the entire process much slower than the original greedy algorithm. In this paper we propose the bucket selector, a model-independent randomized selection strategy. We find that (a) the bucket selector is a lot faster and not significantly worse when a diversity measure exists and (b) it performs better than existing selection strategies in cases without a diversity measure.

/pdf/bucket-selection-a-model-independent-diverse-selection-4mjnj8cm8h.pdf

Bucket Selection : A Model-Independent Diverse Selection Strategy for Widening

In this paper we describe how ensembles can be trained, modified and applied in the open source data analysis platform, KNIME. We focus on recent extensions that also allow ensembles, represented in PMML, to be processed. This way ensembles generated in KNIME can be deployed to PMML scoring engines. In addition ensembles created by other tools and represented as PMML can be applied or further processed (modified or filtered) using intuitive KNIME workflows.

Alexander Fillbrunn

Papers

KNIME for reproducible cross-domain analysis of life science data

OpenMS - A platform for reproducible analysis of mass spectrometry data

Diversity-Driven Widening of Hierarchical Agglomerative Clustering

Bucket Selection : A Model-Independent Diverse Selection Strategy for Widening

Ensembles and PMML in KNIME