ReviewJournal of proteome research2014
Integrating genomic, transcriptomic, and interactome data to improve Peptide and protein identification in shotgun proteomics.
Review in Journal of proteome research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
14 citing papers in PubMed.
- Potential promising of synthetic lethality in cancer research and treatment.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- Proteomic Analysis of Single Hairs.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Artificial intelligence and machine learning disciplines with the potential to improve the nanotoxicology and nanomedicine fields: a comprehensive review.Archives of toxicology · 2023Review
- Protein Complexes Form a Basis for Complex Hybrid Incompatibility.Frontiers in genetics · 2021Review
- Article
- A cost-sensitive online learning method for peptide identification.BMC genomics · 2020Article
- Esophageal, gastric and colorectal cancers: Looking beyond classical serological biomarkers towards glycoproteomics-assisted precision oncology.Theranostics · 2020Review
- Multi-Omics Strategies Uncover Host-Pathogen Interactions.ACS infectious diseases · 2019Review
- A System-wide Approach to Monitor Responses to Synergistic BRAF and EGFR Inhibition in Colorectal Cancer Cells.Molecular & cellular proteomics : MCP · 2018Article
- Review
- Methods, Tools and Current Perspectives in Proteogenomics.Molecular & cellular proteomics : MCP · 2017Review
- proBAMsuite, a Bioinformatics Framework for Genome-Based Representation and Analysis of Proteomics Data.Molecular & cellular proteomics : MCP · 2016Article
- MSProGene: integrative proteogenomics beyond six-frames and single nucleotide polymorphisms.Bioinformatics (Oxford, England) · 2015Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Mass spectrometry (MS)-based shotgun proteomics is an effective technology for global proteome profiling. The ultimate goal is to assign tandem MS spectra to peptides and subsequently infer proteins and their abundance. In addition to database searching and protein assembly algorithms, computational approaches have been developed to integrate genomic, transcriptomic, and interactome information to improve peptide and protein identification. Earlier efforts focus primarily on making databases more comprehensive using publicly available genomic and transcriptomic data. More recently, with the increasing affordability of the Next Generation Sequencing (NGS) technologies, personalized protein databases derived from sample-specific genomic and transcriptomic data have emerged as an attractive strategy. In addition, incorporating interactome data not only improves protein identification but also puts identified proteins into their functional context and thus facilitates data interpretation. In this paper, we survey the major integrative bioinformatics approaches that have been developed during the past decade and discuss their merits and demerits.
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What OpenQuestion holds
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.