AGOUTI: improving genome assembly and annotation using transcriptome data

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Abstract

Summary

Current genome assemblies consist of thousands of 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. We present AGOUTI (Annotated Genome Optimization Using Transcriptome Information), a tool that uses RNA-seq 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.

Availability

The software is implemented in python and is available from github.com/svm-zhang/AGOUTI.

Contact

simozhan@indiana.edu

Supplementary information

Supplementary data are available at Bioinformatics online.

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  1. 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