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Choosing the Best Gene Predictions with GeneValidator

  • Ismail MoghulEmail author
  • Anurag Priyam
  • Yannick WurmEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1962)

Abstract

GeneValidator is a tool for determining whether the characteristics of newly predicted protein-coding genes are consistent with those of similar sequences in public databases. For this, it runs up to seven comparisons per gene. Results are shown in an HTML report containing summary statistics and graphical visualizations that aim to be useful for curators. Results are also presented in CSV and JSON formats for automated follow-up analysis.

Here, we describe common usage scenarios of GeneValidator that use the JSON output results together with standard UNIX tools. We demonstrate how GeneValidator’s textual output can be used to filter and subset large gene sets effectively. First, we explain how low-scoring gene models can be identified and extracted for manual curation—for example, as input for genome browsers or gene annotation tools. Second, we show how GeneValidator’s HTML report can be regenerated from a filtered subset of GeneValidator’s JSON output. Subsequently, we demonstrate how GeneValidator’s GUI can be used to complement manual curation efforts. Additionally, we explain how GeneValidator can be used to merge information from multiple annotations by automatically selecting the higher-scoring gene model at each common gene locus. Finally, we show how GeneValidator analyses can be optimized when using large BLAST databases.

Key words

Genome annotation Gene prediction Gene validation GeneValidator 

Notes

Acknowledgments

This work was supported by the Natural Environment Research Council [grant NE/L00626X/1] and the Biotechnology and Biological Sciences Research Council [grant BB/K004204/1 and BB/M009513/1]. This research used Queen Mary’s Apocrita HPC facility, supported by QMUL Research-IT ( https://doi.org/10.5281/zenodo.438045).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.UCL Cancer InstituteUniversity College LondonLondonUK
  2. 2.School of Biological and Chemical SciencesQueen Mary University of LondonLondonUK

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