Evidence map›Paper›PMID 26893356›Full record

ArticleNucleic acids research2016

Using intron position conservation for homology-based gene prediction.

Jens Keilwagen, Michael Wenk, Jessica L Erickson, Martin H Schattat, Jan Grau, Frank Hartung

Abstract read
In one paragraph

Article in Nucleic acids research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 486 papers.

0numbers the graph read from it
0cells of the map it votes in
486citing 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

486 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Mating-Type System Analysis and Domestication ofJournal of fungi (Basel, Switzerland) · 2026
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

426 more citing papers are in PubMed but not listed here.

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.

Jens KeilwagenInstitute for Biosafety in Plant Biotechnology, Julius Kühn-Institut (JKI) - Federal Research Centre for Cultivated Plants, D-06484 Quedlinburg, Germany jens.keilwagen@jki.bund.de.
Michael WenkInstitute of Computer Science, Martin Luther University Halle-Wittenberg, D-06120 Halle (Saale), Germany.
Jessica L EricksonSchattat Lab, Institute of Biology, Martin Luther University Halle-Wittenberg, D-06120 Halle (Saale), Germany.
Martin H SchattatSchattat Lab, Institute of Biology, Martin Luther University Halle-Wittenberg, D-06120 Halle (Saale), Germany.
Jan GrauInstitute of Computer Science, Martin Luther University Halle-Wittenberg, D-06120 Halle (Saale), Germany.
Frank HartungInstitute for Biosafety in Plant Biotechnology, Julius Kühn-Institut (JKI) - Federal Research Centre for Cultivated Plants, D-06484 Quedlinburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Annotation of protein-coding genes is very important in bioinformatics and biology and has a decisive influence on many downstream analyses. Homology-based gene prediction programs allow for transferring knowledge about protein-coding genes from an annotated organism to an organism of interest.Here, we present a homology-based gene prediction program called GeMoMa. GeMoMa utilizes the conservation of intron positions within genes to predict related genes in other organisms. We assess the performance of GeMoMa and compare it with state-of-the-art competitors on plant and animal genomes using an extended best reciprocal hit approach. We find that GeMoMa often makes more precise predictions than its competitors yielding a substantially increased number of correct transcripts. Subsequently, we exemplarily validate GeMoMa predictions using Sanger sequencing. Finally, we use RNA-seq data to compare the predictions of homology-based gene prediction programs, and find again that GeMoMa performs well.Hence, we conclude that exploiting intron position conservation improves homology-based gene prediction, and we make GeMoMa freely available as command-line tool and Galaxy integration.

Indexed as

Models, GeneticAlgorithmsAnimalsArabidopsisBase SequenceCaricaChickensComputational BiologyHigh-Throughput Nucleotide SequencingHumansIntronsMiceMolecular Sequence AnnotationNicotianaOryzaPolymerase Chain ReactionRNA, Messenger

Identifiers

PMID26893356
PMCPMC4872089

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.