Evidence map›Paper›PMID 21367939›Full record

ArticleGenome research2011

Discovery and annotation of small proteins using genomics, proteomics, and computational approaches.

Xiaohan Yang, Timothy J Tschaplinski, Gregory B Hurst, Sara Jawdy, Paul E Abraham, Patricia K Lankford, Rachel M Adams, Manesh B Shah, Robert L Hettich, Erika Lindquist and 4 more

Open access · bronzeAbstract read
In one paragraph

Article in Genome research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed
–field-weighted citation impact
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

50 citing papers in PubMed, 115 citations in OpenAlex.

  1. Article
  2. Review
  3. Peptide hormones in plants.Molecular horticulture · 2025
    Review
  4. Article
  5. LncRNA-encoded peptides in cancer.Journal of hematology & oncology · 2024
    Review
  6. Article
  7. Article
  8. Review
  9. Article
  10. Small secreted peptides encoded on the wheat (Frontiers in plant science · 2022
    Article
  11. Understanding small ORF diversity through a comprehensive transcription feature classification.DNA research : an international journal for rapid publication of reports on genes and genomes · 2021
    Review
  12. Review
  13. Article
  14. Review
  15. Article
  16. Review
  17. Article
  18. Article
  19. Identification ofFrontiers in microbiology · 2019
    Article
  20. 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

14 authors at 2 institutions in 1 country.

Xiaohan YangBiosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA. yangx@ornl.gov
Timothy J Tschaplinski
Gregory B Hurst
Sara Jawdy
Paul E Abraham
Patricia K Lankford
Rachel M Adams
Manesh B Shah
Robert L Hettich
Erika Lindquist
Udaya C Kalluri
Lee E Gunter
Christa Pennacchio
Gerald A Tuskan
Oak Ridge National Laboratory · USJoint Genome Institute · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Small proteins (10-200 amino acids [aa] in length) encoded by short open reading frames (sORF) play important regulatory roles in various biological processes, including tumor progression, stress response, flowering, and hormone signaling. However, ab initio discovery of small proteins has been relatively overlooked. Recent advances in deep transcriptome sequencing make it possible to efficiently identify sORFs at the genome level. In this study, we obtained ~2.6 million expressed sequence tag (EST) reads from Populus deltoides leaf transcriptome and reconstructed full-length transcripts from the EST sequences. We identified an initial set of 12,852 sORFs encoding proteins of 10-200 aa in length. Three computational approaches were then used to enrich for bona fide protein-coding sORFs from the initial sORF set: (1) coding-potential prediction, (2) evolutionary conservation between P. deltoides and other plant species, and (3) gene family clustering within P. deltoides. As a result, a high-confidence sORF candidate set containing 1469 genes was obtained. Analysis of the protein domains, non-protein-coding RNA motifs, sequence length distribution, and protein mass spectrometry data supported this high-confidence sORF set. In the high-confidence sORF candidate set, known protein domains were identified in 1282 genes (higher-confidence sORF candidate set), out of which 611 genes, designated as highest-confidence candidate sORF set, were supported by proteomics data. Of the 611 highest-confidence candidate sORF genes, 56 were new to the current Populus genome annotation. This study not only demonstrates that there are potential sORF candidates to be annotated in sequenced genomes, but also presents an efficient strategy for discovery of sORFs in species with no genome annotation yet available.

Indexed as

Computational BiologyGenomicsProteomicsExpressed Sequence TagsMolecular Sequence AnnotationMolecular Sequence DataOpen Reading FramesPlant LeavesPlant ProteinsPopulusResearch DesignRNA, UntranslatedPlant ProteinsRNA, Untranslated

Identifiers

PMID21367939
PMCPMC3065711
OpenAlexW2148771431

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.