Evidence map›Paper›PMID 16817966›Full record

ArticleBMC bioinformatics2006

Using ESTs to improve the accuracy of de novo gene prediction.

Chaochun Wei, Michael R Brent

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
2.9field-weighted citation impact, top 9% 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

18 citing papers in PubMed, 56 citations in OpenAlex.

  1. Whole-Genome Alignment and Comparative Annotation.Annual review of animal biosciences · 2019
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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

2 authors at 1 institution in 1 country.

Chaochun WeiLaboratory for Computational Genomics and Department of Computer Science and Engineering, Washington University, One Brookings Drive, St, Louis, MO 63130, USA. wei@cse.wustl.edu
Michael R Brent
Washington University in St. Louis · US

Funding

Predicting Gene Structure: Vertebrate Genome ComparisonR01HG002278 · NHGRI · WASHINGTON UNIVERSITY · PI BRENT, MICHAEL R · 2002 to 2008
$1.9M
NHGRI NIH HHS HG02278NHGRI NIH HHS R01 HG002278
6 · The paper itself

Abstract

backgroundESTs are a tremendous resource for determining the exon-intron structures of genes, but even extensive EST sequencing tends to leave many exons and genes untouched. Gene prediction systems based exclusively on EST alignments miss these exons and genes, leading to poor sensitivity. De novo gene prediction systems, which ignore ESTs in favor of genomic sequence, can predict such "untouched" exons, but they are less accurate when predicting exons to which ESTs align. TWINSCAN is the most accurate de novo gene finder available for nematodes and N-SCAN is the most accurate for mammals, as measured by exact CDS gene prediction and exact exon prediction.

resultsTWINSCAN_EST is a new system that successfully combines EST alignments with TWINSCAN. On the whole C. elegans genome TWINSCAN_EST shows 14% improvement in sensitivity and 13% in specificity in predicting exact gene structures compared to TWINSCAN without EST alignments. Not only are the structures revealed by EST alignments predicted correctly, but these also constrain the predictions without alignments, improving their accuracy. For the human genome, we used the same approach with N-SCAN, creating N-SCAN_EST. On the whole genome, N-SCAN_EST produced a 6% improvement in sensitivity and 1% in specificity of exact gene structure predictions compared to N-SCAN.

conclusionTWINSCAN_EST and N-SCAN_EST are more accurate than TWINSCAN and N-SCAN, while retaining their ability to discover novel genes to which no ESTs align. Thus, we recommend using the EST versions of these programs to annotate any genome for which EST information is available.TWINSCAN_EST and N-SCAN_EST are part of the TWINSCAN open source software package http://genes.cse.wustl.edu/distribution/download_TS.html.

Indexed as

Expressed Sequence TagsAlgorithmsAnimalsCaenorhabditis elegansComputational BiologyDNA, ComplementaryExonsGenome, HumanGenomicsHumansModels, GeneticProgramming LanguagesReproducibility of ResultsSensitivity and SpecificitySoftwareDNA, Complementary

Identifiers

PMID16817966
PMCPMC1534067
OpenAlexW2122992512

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.