High‐throughput parallel proteogenomics: A bacterial case study is a research paper published in PROTEOMICS (2014). On theSindex it has a DataRank of 1.1. It has been cited 22 times, with 21 citing works in its 1-hop citation network.
In recent years, a new paradigm for genome annotation has emerged, termed "proteogenomics," that leverages peptide MS to annotate a genome. This is achieved by mapping peptides to a six‐frame translation of a genome, including available splice databases, which may suggest refinements to gene models. Using this approach, it is possible to refine gene regions such as exon boundaries, novel genes, gene boundaries, frame shifts, reverse strands, translated UTRs, and novel splice junctions. One of the challenges of proteogenomics is how best to (1) tackle assigning confidence to any resulting annotation and (2) apply these gene model refinements, either through manual annotation or through an automated process via training gene prediction tools. This is not a straightforward process, as many gene prediction tools have their defined suitability for niche genomes (either eukaryotic or prokaryotic) trained on and refined with model organisms such as Arabidopsis thaliana and Escherichia coli , and varying degrees of features that can leverage the use of external evidence. In this study, we outline a suitable approach toward preprocessing mass spectra and optimizing the MS/MS search for a given dataset. We also discuss future challenges, which continue to pose a problem in the field of proteogenomics, and better strategies to successfully tackle them with, using existing tools. We use Bradyrhizobium diazoefficiens (Nitrogen‐fixing bacteria), with a 9.1 Mb genome as a case study, utilizing the latest in second‐generation proteogenomics tools with multiple gene models for cross‐validation of proteogenomics annotations.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.470
From this paper's citation signal
Citation Network Contribution
0.675
From 19 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 41% comes from its base citations and 59% from the citation network (19 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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