Evidence map›Paper›PMID 27435057›Full record

ArticleGigaScience2016

AGOUTI: improving genome assembly and annotation using transcriptome data.

Simo V Zhang, Luting Zhuo, Matthew W Hahn

Abstract read
In one paragraph

Article in GigaScience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing 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

36 citing papers in PubMed.

  1. Article
  2. Chromosome-level dataset fromData in brief · 2025
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  3. Life inside a bag: multiomics insights into the bagworm species Eumeta crameri.DNA research : an international journal for rapid publication of reports on genes and genomes · 2025
    Article
  4. Genome assembly and insights into globally invasive Red-vented Bulbul (Pycnonotus cafer).DNA research : an international journal for rapid publication of reports on genes and genomes · 2025
    Article
  5. A high-quality genome assembly of Annona squamosa (custard apple) provides functional insights into an emerging fruit crop.DNA research : an international journal for rapid publication of reports on genes and genomes · 2025
    Article
  6. Article
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  8. Review
  9. Genome sequencing ofFrontiers in plant science · 2023
    Article
  10. Genome ofFrontiers in plant science · 2023
    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

3 authors.

Simo V ZhangSchool of Informatics and Computing, Indiana University, Bloomington, IN, 47405, USA. simozhan@indiana.edu.
Luting ZhuoSchool of Informatics and Computing, Indiana University, Bloomington, IN, 47405, USA.
Matthew W HahnSchool of Informatics and Computing, Indiana University, Bloomington, IN, 47405, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGenomes sequenced using short-read, next-generation sequencing technologies can have many errors and may be fragmented into thousands of small contigs. These incomplete and fragmented assemblies lead to errors in gene identification, such that single genes spread across multiple contigs are annotated as separate gene models. Such biases can confound inferences about the number and identity of genes within species, as well as gene gain and loss between species.

resultsWe present AGOUTI (Annotated Genome Optimization Using Transcriptome Information), a tool that uses RNA sequencing data to simultaneously combine contigs into scaffolds and fragmented gene models into single models. We show that AGOUTI improves both the contiguity of genome assemblies and the accuracy of gene annotation, providing updated versions of each as output. Running AGOUTI on both simulated and real datasets, we show that it is highly accurate and that it achieves greater accuracy and contiguity when compared with other existing methods.

conclusionAGOUTI is a powerful and effective scaffolder and, unlike most scaffolders, is expected to be more effective in larger genomes because of the commensurate increase in intron length. AGOUTI is able to scaffold thousands of contigs while simultaneously reducing the number of gene models by hundreds or thousands. The software is available free of charge under the MIT license.

Indexed as

AlgorithmsComputational BiologyGene Expression ProfilingGenomeHigh-Throughput Nucleotide SequencingMolecular Sequence AnnotationSequence Analysis, RNASoftwareGenome annotationGenome assemblyRNA-seqRNA sequencingScaffolding

Identifiers

PMID27435057
PMCPMC4952227

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Registered trials

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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.