ArticleProteomics2014
Proteogenomic strategies for identification of aberrant cancer peptides using large-scale next-generation sequencing data.
Article in Proteomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed.
- Enhanced Genetically Variant Peptide Profiling from Human Hair Using Multiple Enzymes.ACS omega · 2026Article
- Identification of non-canonical peptides with moPepGen.Nature biotechnology · 2026Article
- Review
- PTMs_Closed_Search: Multiple Post-Translational Modification Closed Search Using Reduced Search Space and Transferred FDR.Proteomes · 2026Article
- Opportunities and Challenges of Multiomics for Discovery and Monitoring of Human Pathogens.Environment & health (Washington, D.C.) · 2025Review
- Identification of non-canonical peptides with moPepGen.bioRxiv : the preprint server for biology · 2025Article
- PgxSAVy: A tool for comprehensive evaluation of variant peptide quality in proteogenomics - catching the (un)usual suspects.Computational and structural biotechnology journal · 2024Article
- Chemoproteogenomic stratification of the missense variant cysteinome.Nature communications · 2024Article
- Multi-omic stratification of the missense variant cysteinome.bioRxiv : the preprint server for biology · 2023Article
- [Research progress and application of retention time prediction method based on deep learning].Se pu = Chinese journal of chromatography · 2021Article
- Protein-gene Expression Nexus: Comprehensive characterization of human cancer cell lines with proteogenomic analysis.Computational and structural biotechnology journal · 2021Article
- MutCombinator: identification of mutated peptides allowing combinatorial mutations using nucleotide-based graph search.Bioinformatics (Oxford, England) · 2020Article
- Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis.Nature communications · 2020Article
- FusionPro, a Versatile Proteogenomic Tool for Identification of Novel Fusion Transcripts and Their Potential Translation Products in Cancer Cells.Molecular & cellular proteomics : MCP · 2019Article
- Proteogenomic Annotation of Chinese Hamsters Reveals Extensive Novel Translation Events and Endogenous Retroviral Elements.Journal of proteome research · 2019Article
- ProteinExplorer: A Repository-Scale Resource for Exploration of Protein Detection in Public Mass Spectrometry Data Sets.Journal of proteome research · 2018Article
- High throughput discovery of protein variants using proteomics informed by transcriptomics.Nucleic acids research · 2018Article
- Recognition of the polycistronic nature of human genes is critical to understanding the genotype-phenotype relationship.Genome research · 2018Review
- MHC class I loaded ligands from breast cancer cell lines: A potential HLA-I-typed antigen collection.Journal of proteomics · 2018Article
- Brute-Force Approach for Mass Spectrometry-Based Variant Peptide Identification in Proteogenomics without Personalized Genomic Data.Journal of the American Society for Mass Spectrometry · 2018Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Cancer is driven by the acquisition of somatic DNA lesions. Distinguishing the early driver mutations from subsequent passenger mutations is key to molecular subtyping of cancers, understanding cancer progression, and the discovery of novel biomarkers. The advances of genomics technologies (whole-genome exome, and transcript sequencing, collectively referred to as NGS (next-generation sequencing)) have fueled recent studies on somatic mutation discovery. However, the vision is challenged by the complexity, redundancy, and errors in genomic data, and the difficulty of investigating the proteome translated portion of aberrant genes using only genomic approaches. Combination of proteomic and genomic technologies are increasingly being employed. Various strategies have been employed to allow the usage of large-scale NGS data for conventional MS/MS searches. This paper provides a discussion of applying different strategies relating to large database search, and FDR (false discovery rate) -based error control, and their implication to cancer proteogenomics. Moreover, it extends and develops the idea of a unified genomic variant database that can be searched by any MS sample. A total of 879 BAM files downloaded from TCGA repository were used to create a 4.34 GB unified FASTA database that contained 2787062 novel splice junctions, 38,464 deletions, 1,105 insertions, and 182,302 substitutions. Proteomic data from a single ovarian carcinoma sample (439,858 spectra) was searched against the database. By applying the most conservative FDR measure, we have identified 524 novel peptides and 65,578 known peptides at 1% FDR threshold. The novel peptides include interesting examples of doubly mutated peptides, frame-shifts, and nonsample-recruited mutations, which emphasize the strength of our approach.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.