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AGOUTI: improving genome assembly and annotation using transcriptome data

Simo V. Zhang, Luting Zhuo, Matthew W. Hahn
doi: https://doi.org/10.1101/033019
Simo V. Zhang
1School of Informatics and Computing, Indiana University, Bloomington, Indiana 47405
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  • For correspondence: simozhan@indiana.edu
Luting Zhuo
1School of Informatics and Computing, Indiana University, Bloomington, Indiana 47405
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Matthew W. Hahn
1School of Informatics and Computing, Indiana University, Bloomington, Indiana 47405
2Department of Biology, Indiana University, Bloomington, Indiana 47405
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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{at}indiana.edu

Supplementary information Supplementary data are available at Bioinformatics online.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted November 26, 2015.
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AGOUTI: improving genome assembly and annotation using transcriptome data
Simo V. Zhang, Luting Zhuo, Matthew W. Hahn
bioRxiv 033019; doi: https://doi.org/10.1101/033019
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AGOUTI: improving genome assembly and annotation using transcriptome data
Simo V. Zhang, Luting Zhuo, Matthew W. Hahn
bioRxiv 033019; doi: https://doi.org/10.1101/033019

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