Biology subjects

Pfeuffer, J.

Publications and source records attributed to Pfeuffer, J..

6 recordsLinked to original sources

bioRxiv2024

Chemical crosslinking extends and complements UV crosslinking in analysis of RNA/DNA nucleic acid-protein interaction sites by mass spectrometry

UV (ultra-violet) crosslinking with mass spectrometry (XL-MS) has been established for identifying RNA- and DNA-binding proteins along with their domains and amino acids involved. Here, we explore chemical XL-MS for RNA-protein, DNA-protein, and nucleotide-protein complexes in vitro and in vivo. We introduce a specialized nucleotide-protein-crosslink search engine, NuXL, for robust and fast identification of such crosslinks at amino acid resolution. Chemical XL-MS complements UV XL-MS by generating different crosslink species, increasing crosslinked protein yields in vivo almost four-fold, and thus it expands the structural information accessible via XL-MS. Our workflow facilitates integrative structural modelling of nucleic acid-protein complexes and adds spatial information to the described RNA-binding properties of enzymes, for which crosslinking sites are often observed close to their cofactor-binding domains. In vivo UV and chemical XL-MS data from E. coli cells analysed by NuXL establish a comprehensive nucleic acid-protein crosslink inventory with crosslink sites at amino acid level for more than 1500 proteins. Our new workflow combined with the dedicated NuXL search engine identified RNA crosslinks that cover most RNA-binding proteins, with DNA and RNA crosslinks detected in transcriptional repressors and activators.

biochemistry↗

bioRxiv2021

Generation of ENSEMBL-based proteogenomics databases boosts the identification of non-canonical peptides.

SummaryWe have implemented the pypgatk package and the pgdb workflow to create proteogenomics databases based on ENSEMBL resources. The tools allow the generation of protein sequences from novel protein-coding transcripts by performing a three-frame translation of pseudogenes, lncRNAs, and other non-canonical transcripts, such as those produced by alternative splicing events. It also includes exonic out-of-frame translation from otherwise canonical protein-coding mRNAs. Moreover, the tool enables the generation of variant protein sequences from multiple sources of genomic variants including COSMIC, cBioportal, gnomAD, and mutations detected from sequencing of patient samples. pypgatk and pgdb provide multiple functionalities for database handling, notably optimized target/decoy generation by the algorithm DecoyPyrat. Finally, we perform a reanalysis of four public datasets in PRIDE by generating cell-type specific databases for 65 cell lines using the pypgatk and pgdb workflow, revealing a wealth of non-canonical or cryptic peptides amounting to more than 10% of the total number of peptides identified (43,501 out of 402,512). AvailabilityThe software is freely available. pypgatk: (https://github.com/bigbio/py-pgatk/), and pgdb: (https://github.com/nf-core/pgdb) ContactYasset Perez-Riverol (yperez@ebi.ac.uk), Rui Branca (rui.mamede-branca@ki.se) Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2021

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

The amount of public proteomics data is increasing at an extraordinary rate. Hundreds of datasets are submitted each month to ProteomeXchange repositories, representing many types of proteomics studies, focusing on different aspects such as quantitative experiments, post-translational modifications, protein-protein interactions, or subcellular localization, among many others. For every proteomics dataset, two levels of data are captured: the dataset description, and the data files (encoded in different file formats). Whereas the dataset description and data file formats are supported by all ProteomeXchange partner repositories, there is no standardized format to properly describe the sample metadata and their relationship with the dataset files in a way that fully allows their understanding or re-analysis. It is left to the users choice whether to provide or not an ad hoc document containing this information. Therefore, in many cases, understanding the study design and data requires going back to the associated publication. This can be tedious and may be restricted in the case of non-open access publications. In many cases, this problem limits the generalization and reuse of public proteomics data. Here we present a standard representation for sample metadata tailored to proteomics datasets produced by the HUPO Proteomics Standards Initiative and supported by ProteomeXchange resources. We repurposed the existing data format MAGE-TAB used routinely in the transcriptomics field to represent and annotate proteomics datasets. MAGETAB-Proteomics defines a set of annotation rules that the datasets submitted to ProteomeXchange should follow, ranging from sample properties to data analysis protocols. We also introduce a crowdsourcing project that enabled the manual curation of over 200 public datasets using MAGE-TAB-Proteomics. In addition, we describe an ecosystem of tools and libraries that were developed to validate and submit sample metadata-related information to ProteomeXchange. We expect that these tools will improve the reproducibility of published results and facilitate the reanalysis and integration of public proteomics datasets.

bioinformatics↗

bioRxiv2020

DIAproteomics: A multi-functional data analysis pipeline for data-independent-acquisition proteomics and peptidomics

Data-independent acquisition (DIA) is becoming a leading analysis method in biomedical mass spectrometry. Main advantages include greater reproducibility, sensitivity and dynamic range compared to data-dependent acquisition (DDA). However, data analysis is complex and often requires expert knowledge when dealing with large-scale data sets. Here we present DIAproteomics a multi-functional, automated high-throughput pipeline implemented in Nextflow that allows to easily process proteomics and peptidomics DIA datasets on diverse compute infrastructures. Central components are well-established tools such as the OpenSwathWorkflow for DIA spectral library search and PyProphet for false discovery rate assessment. In addition, it provides options to generate spectral libraries from existing DDA data and carry out retention time and chromatogram alignment. The output includes annotated tables and diagnostic visualizations from statistical post-processing and computation of fold-changes across pairwise conditions, predefined in an experimental design. DIAproteomics is open-source software and available under a permissive license to the scientific community at https://www.openms.de/diaproteomics/.

bioinformatics↗

bioRxiv2020

SPLICE-q: a Python tool for genome-wide quantification of splicing efficiency

BackgroundIntrons are generally removed from primary transcripts to form mature RNA molecules in a post-transcriptional process called splicing. An efficient splicing of primary transcripts is an essential step in gene expression and its misregulation is related to numerous human diseases. Thus, to better understand the dynamics of this process and the perturbations that might be caused by aberrant transcript processing it is important to quantify splicing efficiency. ResultsHere, we introduce SPLICE-q, a fast and user-friendly Python tool for genome-wide SPLICing Efficiency quantification. It supports studies focusing on the implications of splicing efficiency in transcript processing dynamics. SPLICE-q uses aligned reads from strand-specific RNA-seq to quantify splicing efficiency for each intron individually and allows the user to select different levels of restrictiveness concerning the introns overlap with other genomic elements such as exons of other genes. We applied SPLICE-q to globally assess the dynamics of intron excision in yeast and human nascent RNA-seq. We also show its application using total RNA-seq from a patient-matched prostate cancer sample. ConclusionsOur analyses illustrate that SPLICE-q is suitable to detect a progressive increase of splicing efficiency throughout a time course of nascent RNA-seq and it might be useful when it comes to understanding cancer progression beyond mere gene expression levels. SPLICE-q is available at: https://github.com/vrmelo/SPLICE-q

bioinformatics↗

bioRxiv2019

EPIFANY - A method for efficient high-confidence protein inference

Accurate protein inference under the presence of shared peptides is still one of the key problems in bottom-up proteomics. Most protein inference tools employing simple heuristic inference strategies are efficient, but exhibit reduced accuracy. More advanced probabilistic methods often exhibit better inference quality but tend to be too slow for large data sets.\n\nHere we present a novel protein inference method, EPIFANY, combining a loopy belief propagation algorithm with convolution trees for efficient processing of Bayesian networks. We demonstrate that EPIFANY combines the reliable protein inference of Bayesian methods with significantly shorter runtimes. On the 2016 iPRG protein inference benchmark data EPIFANY is the only tested method which finds all true-positive proteins at a 5% protein FDR without strict pre-filtering on PSM level, yielding an increase in identification performance (+10% in the number of true positives and +35% in partial AUC) compared to previous approaches. Even very large data sets with hundreds of thousands of spectra (which are intractable with other Bayesian and some non-Bayesian tools) can be processed with EPIFANY within minutes. The increased inference quality including shared peptides results in better protein inference results and thus increased robustness of the biological hypotheses generated.\n\nEPIFANY is available as open-source software for all major platforms at https://OpenMS.de/epifany.

bioinformatics↗