Evidence map›Paper›PMID 26560066›Full record

ArticleMolecular & cellular proteomics : MCP2016

Integrated Transcriptomic-Proteomic Analysis Using a Proteogenomic Workflow Refines Rat Genome Annotation.

Dhirendra Kumar, Amit Kumar Yadav, Xinying Jia, Jason Mulvenna, Debasis Dash

Open access · hybridAbstract read
In one paragraph

Article in Molecular & cellular proteomics : MCP, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
2.2field-weighted citation impact, top 13% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 37 citations in OpenAlex.

  1. Article
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  4. Article
  5. Review
  6. Article
  7. Proteotranscriptomics - A facilitator in omics research.Computational and structural biotechnology journal · 2022
    Review
  8. Improving the Genome Annotation ofCurrent genomics · 2021
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Proteogenomic Analysis and Discovery of Immune Antigens inMolecular & cellular proteomics : MCP · 2017
    Article
  16. A Golden Age for Working with Public Proteomics Data.Trends in biochemical sciences · 2017
    Review
  17. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 2 countries.

Dhirendra KumarFrom the ‡G. N. Ramachandran Knowledge Centre for Genome Informatics, Council of Scientific and Industrial Research-Institute of Genomics and Integrative Biology, South Campus, Sukhdev Vihar, New Delhi, India;
Amit Kumar YadavFrom the ‡G. N. Ramachandran Knowledge Centre for Genome Informatics, Council of Scientific and Industrial Research-Institute of Genomics and Integrative Biology, South Campus, Sukhdev Vihar, New Delhi, India;
Xinying Jia¶Infectious Diseases Program, QIMR Berghofer Medical Research Institute, Brisbane, Australia.
Jason Mulvenna¶Infectious Diseases Program, QIMR Berghofer Medical Research Institute, Brisbane, Australia.
Debasis DashFrom the ‡G. N. Ramachandran Knowledge Centre for Genome Informatics, Council of Scientific and Industrial Research-Institute of Genomics and Integrative Biology, South Campus, Sukhdev Vihar, New Delhi, India; ddash@igib.res.in.
Institute of Genomics and Integrative Biology · INQIMR Berghofer Medical Research Institute · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Proteogenomic re-annotation and mRNA splicing information can lead to the discovery of various protein forms for eukaryotic model organisms like rat. However, detection of novel proteoforms using mass spectrometry proteomics data remains a formidable challenge. We developed EuGenoSuite, an open source multiple algorithmic proteomic search tool and utilized it in our in-house integrated transcriptomic-proteomic pipeline to facilitate automated proteogenomic analysis. Using four proteogenomic pipelines (integrated transcriptomic-proteomic, Peppy, Enosi, and ProteoAnnotator) on publicly available RNA-sequence and MS proteomics data, we discovered 363 novel peptides in rat brain microglia representing novel proteoforms for 249 gene loci in the rat genome. These novel peptides aided in the discovery of novel exons, translation of annotated untranslated regions, pseudogenes, and splice variants for various loci; many of which have known disease associations, including neurological disorders like schizophrenia, amyotrophic lateral sclerosis, etc. Novel isoforms were also discovered for genes implicated in cardiovascular diseases and breast cancer for which rats are considered model organisms. Our integrative multi-omics data analysis not only enables the discovery of new proteoforms but also generates an improved reference for human disease studies in the rat model.

Indexed as

Alternative SplicingAnimalsComputational BiologyGene Expression ProfilingGenetic Predisposition to DiseaseGenomeGenomicsHumansInformation Storage and RetrievalMass SpectrometryMiceMicrogliaMolecular Sequence AnnotationPeptidesProteomeProteomicsPeptidesProteome

Identifiers

PMID26560066
PMCPMC4762527
OpenAlexW2174838639

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.