EPIFANY: A Method for Efficient High-Confidence Protein Inference

Pfeuffer, Julianus; Sachsenberg, Timo; Dijkstra, Tjeerd M. H.; Serang, Oliver; Reinert, Knut; Kohlbacher, Oliver

doi:10.1021/acs.jproteome.9b00566

Cited by 21 publications

(45 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another improvement to the pipeline will be an evolution of the heuristically driven “Rescue & Resolve” approach. We plan to develop a probabilistic protein inference algorithm in which transcriptional abundance values are incorporated into a rigorous statistical framework for the inference of protein isoforms [ 43 , 60 ]. The applications of our computational pipeline could also include the analysis of novel genes or genetic variation that is detectable in long-read data or separately available from previous genotyping, use of ONT (i.e., nanopore) cDNA or direct RNA sequencing data [ 54 ], the analysis of single-cell RNA-seq, use of targeted long-read datasets [ 61 ], or the use of top-down proteomics data for the analysis of proteoform diversity [ 62 ].…”

Section: Discussionmentioning

confidence: 99%

Enhanced protein isoform characterization through long-read proteogenomics

et al. 2022

View full text Add to dashboard Cite

Background The detection of physiologically relevant protein isoforms encoded by the human genome is critical to biomedicine. Mass spectrometry (MS)-based proteomics is the preeminent method for protein detection, but isoform-resolved proteomic analysis relies on accurate reference databases that match the sample; neither a subset nor a superset database is ideal. Long-read RNA sequencing (e.g., PacBio or Oxford Nanopore) provides full-length transcripts which can be used to predict full-length protein isoforms. Results We describe here a long-read proteogenomics approach for integrating sample-matched long-read RNA-seq and MS-based proteomics data to enhance isoform characterization. We introduce a classification scheme for protein isoforms, discover novel protein isoforms, and present the first protein inference algorithm for the direct incorporation of long-read transcriptome data to enable detection of protein isoforms previously intractable to MS-based detection. We have released an open-source Nextflow pipeline that integrates long-read sequencing in a proteomic workflow for isoform-resolved analysis. Conclusions Our work suggests that the incorporation of long-read sequencing and proteomic data can facilitate improved characterization of human protein isoform diversity. Our first-generation pipeline provides a strong foundation for future development of long-read proteogenomics and its adoption for both basic and translational research.

show abstract

Section: Discussionmentioning

confidence: 99%

Enhanced protein isoform characterization through long-read proteogenomics

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Briefly, raw.d files generated from the timsTOF Pro were converted to mzML with OpenMS (v 2.5.0) and tdf2mzml in-house nextflow script. Converted mzML files were searched against a reviewed UniProt human proteome (downloaded 1/1/2020) in OpenMS with the following protein search engine and inference engine combination: Comet fido, Comet epiphany, X!Tandem epiphany, MSGF+ fido, and MSGF+ epiphany [19][20][21][22]. Search parameters included precursor mass tolerance 20 ppm, MS2 mass tolerance 0.05 Da, carbamidomethylation of cysteine as a fixed modification, oxidation of methionine as a variable modification and false discovery rate (FDR) of peptide and proteins equal to 0.05.…”

Section: Protein Identification Data Analysis and Statistics For Prot...mentioning

confidence: 99%

Proteomic analysis of the umbilical cord in fetal growth restriction and preeclampsia

et al. 2022

View full text Add to dashboard Cite

Fetal growth restriction (FGR) is associated with adverse perinatal outcomes. Pre-eclampsia (PreE) increases the associated perinatal morbidity and mortality. The structure of the umbilical cord in the setting of FGR and PreE is understudied. This study aimed to examine changes in the umbilical cord (UC) composition in pregnancies complicated by FGR and FGR with PreE. UC from gestational age-matched pregnancies with isolated FGR (n = 5), FGR+PreE (n = 5) and controls (n = 5) were collected, and a portion of the UC was processed for histologic and proteomic analysis. Manual segmentation analysis was performed to measure cross-section analysis of umbilical cord regions. Wharton’s Jelly samples were analyzed on a tims-TOF Pro. Spectral count and ion abundance data were analyzed, creating an intersection dataset from multiple mass spectrometry search and inference engines. UCs from FGR and FGR with PreE had lower cross-sectional area and Wharton’s Jelly area compared with control (p = 0.03). When comparing FGR to control, 28 proteins were significantly different in abundance analysis and 34 in spectral count analysis (p < 0.05). Differential expression analysis between PreE with FGR vs controls demonstrated that 48 proteins were significantly different in abundance and 5 in spectral count. The majority of changes occurred in proteins associated with extracellular matrix, cellular process, inflammatory, and angiogenesis pathways. The structure and composition of the UC is altered in pregnancies with FGR and FGR with PreE. Future work in validating these proteomic differences will enable identification of therapeutic targets for FGR and FGR with PreE.

show abstract

“…Peptide-level FDR was estimated with a conventional target-decoy approach; the target-decoy FASTA database supplied to MS-GF+ was constructed from the set of verified UniProt sequences by concatenation of the reversed sequences. Proteins were subsequently assembled directly from the set of peptide identifications, or alternatively inferred using the Percolator 49 , Fido 50 or EPIFANY 51 engines. For EPIFANY inference, peptides were first rescored by Percolator.…”

Section: Raw Proteomic Data Processingmentioning

confidence: 99%

Single-sample proteome enrichment enables missing protein recovery and phenotype association

Wong

Kong

Goh

2021

Preprint

View full text Add to dashboard Cite

Proteomic studies characterize the protein composition of complex biological samples. Despite recent developments in mass spectrometry instrumentation and computational tools, low proteome coverage remains a challenge. To address this, we present Proteome Support Vector Enrichment (PROSE), a fast, scalable, and effective pipeline for scoring protein identifications based on gene co-expression matrices. Using a simple set of observed proteins as input, PROSE gauges the relative importance of proteins in the phenotype. The resultant enrichment scores are interpretable and stable, corresponding well to the source phenotype, thus enabling reproducible recovery of missing proteins. We further demonstrate its utility via reanalysis of the Cancer Cell Line Encyclopedia (CCLE) proteomic data, with prediction of oncogenic dependencies and identification of well-defined regulatory modules. PROSE is available as a user-friendly Python module from https://github.com/bwbio/PROSE.

show abstract

EPIFANY: A Method for Efficient High-Confidence Protein Inference

Cited by 21 publications

References 36 publications

Enhanced protein isoform characterization through long-read proteogenomics

Enhanced protein isoform characterization through long-read proteogenomics

Proteomic analysis of the umbilical cord in fetal growth restriction and preeclampsia

Single-sample proteome enrichment enables missing protein recovery and phenotype association

Contact Info

Product

Resources

About