Plant Proteogenomics: Improvements to the Grapevine Genome Annotation is a research paper published in PROTEOMICS (2017). On theSindex it has a DataRank of 1.1. It has been cited 27 times, with 24 citing works in its 1-hop citation network.
Abstract Grapevine is an important perennial fruit to the wine industry, and has implications for the health industry with some causative agents proven to reduce heart disease. Since the sequencing and assembly of grapevine cultivar Pinot Noir, several studies have contributed to its genome annotation. This new study further contributes toward genome annotation efforts by conducting a proteogenomics analysis using the latest genome annotation from CRIBI, legacy proteomics dataset from cultivar Cabernet Sauvignon and a large RNA‐seq dataset. A total of 341 novel annotation events are identified consisting of five frame‐shifts, 37 translated UTRs, 15 exon boundaries, one novel splice, nine novel exons, 159 gene boundaries, 112 reverse strands, and one novel gene event in 213 genes and 323 proteins. From this proteogenomics evidence, the Augustus gene prediction tool predicted 52 novel and revised genes (54 protein isoforms), 11 genes of which are associated with key traits such as stress tolerance and floral and fruity wine characteristics. This study also highlights a likely over‐assembly with the genome, particularly on chromosome 7.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.500
From this paper's citation signal
Citation Network Contribution
0.564
From 21 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 47% comes from its base citations and 53% from the citation network (21 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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