Evidence map›Paper›PMID 16925843›Full record

ArticleGenome biology2006

JIGSAW, GeneZilla, and GlimmerHMM: puzzling out the features of human genes in the ENCODE regions.

Jonathan E Allen, William H Majoros, Mihaela Pertea, Steven L Salzberg

Abstract readEvaluation Study
In one paragraph

Article in Genome biology, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing 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

38 citing papers in PubMed.

  1. Review
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  5. Article
  6. Review
  7. Genomic Features ofPathogens (Basel, Switzerland) · 2020
    Article
  8. Article
  9. Article
  10. PalmXplore: oil palm gene database.Database : the journal of biological databases and curation · 2018
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Improved transcript isoform discovery using ORF graphs.Bioinformatics (Oxford, England) · 2014
    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

4 authors.

Jonathan E AllenCenter for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA.
William H Majoros
Mihaela Pertea
Steven L Salzberg

Funding

COMPUTATIONAL MODELING OF GENES AND GENE STRUCTURER01LM006845 · NLM · UNIV OF MARYLAND, COLLEGE PARK · PI SALZBERG, STEVEN L. · 1999 to 2010
$5.9M
Bioinformatics Software for Analyzing Microbial GenomesR01LM007938 · NLM · UNIV OF MARYLAND, COLLEGE PARK · PI SALZBERG, STEVEN L. · 2003 to 2006
$757k
NLM NIH HHS R01 LM006845NLM NIH HHS R01 LM007938NLM NIH HHS R01-LM007938NLM NIH HHS R01-LM06845
6 · The paper itself

Abstract

backgroundPredicting complete protein-coding genes in human DNA remains a significant challenge. Though a number of promising approaches have been investigated, an ideal suite of tools has yet to emerge that can provide near perfect levels of sensitivity and specificity at the level of whole genes. As an incremental step in this direction, it is hoped that controlled gene finding experiments in the ENCODE regions will provide a more accurate view of the relative benefits of different strategies for modeling and predicting gene structures.

resultsHere we describe our general-purpose eukaryotic gene finding pipeline and its major components, as well as the methodological adaptations that we found necessary in accommodating human DNA in our pipeline, noting that a similar level of effort may be necessary by ourselves and others with similar pipelines whenever a new class of genomes is presented to the community for analysis. We also describe a number of controlled experiments involving the differential inclusion of various types of evidence and feature states into our models and the resulting impact these variations have had on predictive accuracy.

conclusionWhile in the case of the non-comparative gene finders we found that adding model states to represent specific biological features did little to enhance predictive accuracy, for our evidence-based 'combiner' program the incorporation of additional evidence tracks tended to produce significant gains in accuracy for most evidence types, suggesting that improved modeling efforts at the hidden Markov model level are of relatively little value. We relate these findings to our current plans for future research.

Indexed as

GenesSoftwareComputational BiologyExpressed Sequence TagsGenome, HumanGenomicsHumansSequence Analysis, DNA

Identifiers

PMID16925843
PMCPMC1810558

What OpenQuestion holds

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LicenceCC BY
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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.