AGOUTI: improving genome assembly and annotation using transcriptome data

  1. Simo V Zhang
  2. Luting Zhuo
  3. Matthew W Hahn

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Abstract

Background

Genomes 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.

Results

We 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.

Conclusion

AGOUTI 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.

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  1. GigaScience

    Now published in GigaScience doi: 10.1186/s13742-016-0136-3

    Simo V. Zhang 1School of Informatics and Computing, Indiana University, Bloomington, Indiana 47405Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteFor correspondence: simozhan@indiana.eduLuting Zhuo 1School of Informatics and Computing, Indiana University, Bloomington, Indiana 47405Find this author on Google ScholarFind this author on PubMedSearch for this author on this siteMatthew W. Hahn 1School of Informatics and Computing, Indiana University, Bloomington, Indiana 474052Department of Biology, Indiana University, Bloomington, Indiana 47405Find this author on Google ScholarFind this author on PubMedSearch for this author on this site

    A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1186/s13742-016-0136-3 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.

    These peer reviews were as follows:

    Reviewer 1: http://dx.doi.org/10.5524/REVIEW.100463 Reviewer 2: http://dx.doi.org/10.5524/REVIEW.100462

  2. Version published to 10.1186/s13742-016-0136-3
  3. Version published to 10.1101/033019v1 on bioRxiv