Evidence map›Paper›PMID 16543943›Full record

ArticlePLoS computational biology2006

A third approach to gene prediction suggests thousands of additional human transcribed regions.

Gustavo Glusman, Shizhen Qin, M Raafat El-Gewely, Andrew F Siegel, Jared C Roach, Leroy Hood, Arian F A Smit

Abstract read
In one paragraph

Article in PLoS computational biology, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Review
  7. Article
  8. Oncotarget · 2017
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. 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

7 authors.

Gustavo GlusmanInstitute for Systems Biology, Seattle, Washington, USA. Gustavo@SystemsBiology.org
Shizhen Qin
M Raafat El-Gewely
Andrew F Siegel
Jared C Roach
Leroy Hood
Arian F A Smit

Funding

Maintenance and Development of RepeatMasker and RepeatModelerR01HG002939 · NHGRI · INSTITUTE FOR SYSTEMS BIOLOGY · PI Robert MacDonald Hubley, Arian Fredericus Anthonius Smit · 2003 to 2026
$9.1M
Quantifying Protein Predictors of Macrophage ResponsesK08AI056092 · NIAID · SEATTLE CHILDREN'S HOSPITAL · PI ROACH, JARED C · 2003 to 2007
$613k
NHGRI NIH HHS R01 HG002939NIAID NIH HHS 5K08AI056092NIAID NIH HHS K08 AI056092
6 · The paper itself

Abstract

The identification and characterization of the complete ensemble of genes is a main goal of deciphering the digital information stored in the human genome. Many algorithms for computational gene prediction have been described, ultimately derived from two basic concepts: (1) modeling gene structure and (2) recognizing sequence similarity. Successful hybrid methods combining these two concepts have also been developed. We present a third orthogonal approach to gene prediction, based on detecting the genomic signatures of transcription, accumulated over evolutionary time. We discuss four algorithms based on this third concept: Greens and CHOWDER, which quantify mutational strand biases caused by transcription-coupled DNA repair, and ROAST and PASTA, which are based on strand-specific selection against polyadenylation signals. We combined these algorithms into an integrated method called FEAST, which we used to predict the location and orientation of thousands of putative transcription units not overlapping known genes. Many of the newly predicted transcriptional units do not appear to code for proteins. The new algorithms are particularly apt at detecting genes with long introns and lacking sequence conservation. They therefore complement existing gene prediction methods and will help identify functional transcripts within many apparent "genomic deserts."

Indexed as

AlgorithmsComputational BiologyDNA RepairEvolution, MolecularGene Expression ProfilingHumansModels, StatisticalMutationSequence Analysis, DNASoftwareTime Factors

Identifiers

PMID16543943
PMCPMC1391917

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.