ArticleJournal of proteome research2016
Flexible Data Analysis Pipeline for High-Confidence Proteogenomics.
Article in Journal of proteome research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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Who cites it
8 citing papers in PubMed.
- Article
- Developing a bioinformatics pipeline for comparative protein classification analysis.BMC genomic data · 2022Article
- Software Options for the Analysis of MS-Proteomic Data.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Article
- Evidence for a novel overlapping coding sequence in POLG initiated at a CUG start codon.BMC genetics · 2020Article
- Discovery of high-confidence human protein-coding genes and exons by whole-genome PhyloCSF helps elucidate 118 GWAS loci.Genome research · 2019Article
- GENCODE reference annotation for the human and mouse genomes.Nucleic acids research · 2019Article
- A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes.Journal of visualized experiments : JoVE · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Proteogenomics leverages information derived from proteomic data to improve genome annotations. Of particular interest are "novel" peptides that provide direct evidence of protein expression for genomic regions not previously annotated as protein-coding. We present a modular, automated data analysis pipeline aimed at detecting such "novel" peptides in proteomic data sets. This pipeline implements criteria developed by proteomics and genome annotation experts for high-stringency peptide identification and filtering. Our pipeline is based on the OpenMS computational framework; it incorporates multiple database search engines for peptide identification and applies a machine-learning approach (Percolator) to post-process search results. We describe several new and improved software tools that we developed to facilitate proteogenomic analyses that enhance the wealth of tools provided by OpenMS. We demonstrate the application of our pipeline to a human testis tissue data set previously acquired for the Chromosome-Centric Human Proteome Project, which led to the addition of five new gene annotations on the human reference genome.
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Registered trials
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