ORFograph: search for novel insecticidal protein genes in genomic and metagenomic assembly graphs

Microbiome - 2021
Tatiana Dvorkina1, Anton Bankevich2, Alexeï Sorokin3, Fan Yang4,5, Boahemaa Adu-Oppong6,4, Ryan J. Williams5, Keith H. Turner5, Pavel A. Pevzner2
1Center for Algorithmic Biotechnology [Saint Petersburg] (7-9, Universitetskaya nab., St. Petersburg, 199034 - Russia)
2UC San Diego - University of California [San Diego] (UCSD, 9500 Gilman Dr., La Jolla, CA 92093 USA - United States)
3MICALIS - MICrobiologie de l'ALImentation au Service de la Santé (Domaine de Vilvert 78352 JOUY-EN-JOSAS CEDEX - France)
4Bayer Cropscience (France)
5Ascus Biosciences (San Diego - United States)
6Thermo Fisher Scientific Inc. (168 Third Avenue, Waltham, MA USA 02451 - United States)

Tóm tắt

Abstract Background Since the prolonged use of insecticidal proteins has led to toxin resistance, it is important to search for novel insecticidal protein genes (IPGs) that are effective in controlling resistant insect populations. IPGs are usually encoded in the genomes of entomopathogenic bacteria, especially in large plasmids in strains of the ubiquitous soil bacteria, Bacillus thuringiensis (Bt). Since there are often multiple similar IPGs encoded by such plasmids, their assemblies are typically fragmented and many IPGs are scattered through multiple contigs. As a result, existing gene prediction tools (that analyze individual contigs) typically predict partial rather than complete IPGs, making it difficult to conduct downstream IPG engineering efforts in agricultural genomics. Methods Although it is difficult to assemble IPGs in a single contig, the structure of the genome assembly graph often provides clues on how to combine multiple contigs into segments encoding a single IPG. Results We describe ORFograph, a pipeline for predicting IPGs in assembly graphs, benchmark it on (meta)genomic datasets, and discover nearly a hundred novel IPGs. This work shows that graph-aware gene prediction tools enable the discovery of greater diversity of IPGs from (meta)genomes. Conclusions We demonstrated that analysis of the assembly graphs reveals novel candidate IPGs. ORFograph identified both already known genes “hidden” in assembly graphs and potential novel IPGs that evaded existing tools for IPG identification. As ORFograph is fast, one could imagine a pipeline that processes many (meta)genomic assembly graphs to identify even more novel IPGs for phenotypic testing than would previously be inaccessible by traditional gene-finding methods. While here we demonstrated the results of ORFograph only for IPGs, the proposed approach can be generalized to any class of genes.

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