Evidence map›Paper›PMID 32866085›Full record

ArticlePhysiological genomics2020

An integrative proteogenomics approach reveals peptides encoded by annotated lincRNA in the mouse kidney inner medulla.

Cameron T Flower, Lihe Chen, Hyun Jun Jung, Viswanathan Raghuram, Mark A Knepper, Chin-Rang Yang

Open access · greenAbstract read
In one paragraph

Article in Physiological genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.5field-weighted citation impact, top 43% 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

6 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Peptidomics Methods Applied to the Study of Flower Development.Methods in molecular biology (Clifton, N.J.) · 2023
    Article
  5. Peptidomics Methods Applied to the Study of Flower Development.Methods in molecular biology (Clifton, N.J.) · 2023
    Article
  6. Article
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

6 authors at 1 institution in 1 country.

Cameron T FlowerEpithelial Systems Biology Laboratory, Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland.ORCID 0000-0002-9632-9913
Lihe ChenEpithelial Systems Biology Laboratory, Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland.
Hyun Jun JungEpithelial Systems Biology Laboratory, Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland.
Viswanathan RaghuramEpithelial Systems Biology Laboratory, Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland.
Mark A KnepperEpithelial Systems Biology Laboratory, Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland.
Chin-Rang YangEpithelial Systems Biology Laboratory, Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland.
National Institutes of Health · US

Funding

Solute And Water Transport In Renal EpitheliaZIAHL001285 · NHLBI · NATIONAL HEART, LUNG, AND BLOOD INSTITUTE · PI KNEPPER, MARK · 2009 to 2025
$34.9M
Computational Tools for ProteomicsZIAHL006129 · NHLBI · NATIONAL HEART, LUNG, AND BLOOD INSTITUTE · PI KNEPPER, MARK · 2011 to 2025
$8.1M
Intramural NIH HHS ZIA HL001285Intramural NIH HHS ZIA HL006129NHLBI NIH HHS ZIA-HL001285 MAKNHLBI NIH HHS ZIA-HL006129 MAK
6 · The paper itself

Abstract

Long noncoding RNAs (lncRNAs) are intracellular transcripts longer than 200 nucleotides and lack protein-coding information. A subclass of lncRNA known as long intergenic noncoding RNAs (lincRNAs) are transcribed from genomic regions that share no overlap with annotated protein-coding genes. Increasing evidence has shown that some annotated lincRNA transcripts do in fact contain open reading frames (ORFs) encoding functional short peptides in the cell. Few robust methods for lincRNA-encoded peptide identification have been reported, and the tissue-specific expression of these peptides has been largely unexplored. Here we propose an integrative workflow for lincRNA-encoded peptide discovery and test it on the mouse kidney inner medulla (IM). In brief, low molecular weight protein fractions were enriched from homogenate of IMs and trypsinized into shorter peptides, which were sequenced by high resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS). To curate a hypothetical lincRNA-encoded peptide database for peptide-spectrum matching following LC-MS/MS, we performed RNA-Seq on IMs, computationally removed reads overlapping with annotated protein-coding genes, and remapped the remaining reads to a database of mouse noncoding transcripts to infer lincRNA expression. Expressed lincRNAs were searched for ORFs by an existing rule-based algorithm, and translated ORFs were used for peptide-spectrum matching. Peptides identified by LC-MS/MS were further evaluated by using several quality control criteria and bioinformatics methods. We discovered three novel lincRNA-encoded peptides, which are conserved in mouse, rat, and human. The workflow can be adapted for discovery of small protein-coding genes in any species or tissue where noncoding transcriptome information is available.

Indexed as

AnimalsBase SequenceChromatography, LiquidComputational BiologyKidney MedullaMiceMice, Inbred C57BLOpen Reading FramesPeptidesProteinsProteogenomicsProteomeProteomicsRNA, Long NoncodingRNA-SeqTandem Mass SpectrometryPeptidesProteinsProteomeRNA, Long NoncodinglincRNAnoncoding RNAprotein mass spectrometryproteogenomicsRNA-Seq

Identifiers

PMID32866085
PMCPMC7750510
OpenAlexW3081841310

What OpenQuestion holds

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Read underepoch 390

Registered trials

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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.