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

Microbiome

Tatiana Dvorkina¹  Ryan Williams²  Keith Turner²  Fan Yang³  Boahemaa Adu-Oppong⁴  Anton Bankevich⁵  Pavel A. Pevzner⁵  Alexei Sorokin⁶ 

[1] Center for Algorithmic Biotechnology, Saint Petersburg State University, Saint Petersburg, Russia;Data Science & Analytics, Bayer U.S. - Crop Science, Chesterfield, MO, USA;Data Science & Analytics, Bayer U.S. - Crop Science, Chesterfield, MO, USA;Ascus Biosciences, San Diego, CA, USA;Data Science & Analytics, Bayer U.S. - Crop Science, Chesterfield, MO, USA;Thermo Fisher Scientific, Carlsbad, CA, USA;Department of Computer Science and Engineering, University of California San Diego, San Diego, CA, USA;Université Paris-Saclay, INRAE, Micalis Institute, AgroParisTech, 78350, Jouy-en-Josas, France;

关键词: Bioinformatics;    Gene finding;    Bacterial genomics;    Metagenomics;    Bioinsecticides;   

DOI  :  10.1186/s40168-021-01092-z

来源: Springer

【 摘 要 】

BackgroundSince 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.MethodsAlthough 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.ResultsWe 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.ConclusionsWe 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.9B8eiFq2LCBSHTPVU5hws-Video abstract

【 授权许可】

CC BY