BackgroundOur knowledge of natural genetic variation is increasing at an extremely rapid pace, affording an opportunity to come to a much richer understanding of how effects of specific genes are dependent on the genetic background. To achieve a systematic understanding of such GxG interactions, it is desirable to develop genome editing tools that can be rapidly deployed across many different genetic varieties.ResultsWe present an efficient CRISPR/Cas9 toolbox of super module (SM) vectors. These vectors are based on a previously described fluorescence protein marker expressed in seeds allowing identification of transgene-free mutants. We have used this vector series to delete genomic regions ranging from 1.7 to 13 kb in different natural accessions of the wild plant Arabidopsis thaliana. Based on results from 53 pairs of sgRNAs targeting individual nucleotide binding site leucine-rich repeat (NLR) genes, we provide a comprehensive overview of obtaining heritable deletions.ConclusionsThe SM series of CRISPR/Cas9 vectors enables the rapid generation of transgene-free, genome edited plants for a diversity of functional studies.Electronic supplementary materialThe online version of this article (10.1186/s13007-018-0330-7) contains supplementary material, which is available to authorized users.

No abstract

Plasmids are mobile genetic elements that play a key role in microbial ecology and evolution by mediating horizontal transfer of important genes, such as antimicrobial resistance genes. Many microbial genomes have been sequenced by short read sequencers and have resulted in a mix of contigs that derive from plasmids or chromosomes. New tools that accurately identify plasmids are needed to elucidate new plasmid-borne genes of high biological importance. We have developed Deeplasmid, a deep learning tool for distinguishing plasmids from bacterial chromosomes based on the DNA sequence and its encoded biological data. It requires as input only assembled sequences generated by any sequencing platform and assembly algorithm and its runtime scales linearly with the number of assembled sequences. Deeplasmid achieves an AUC–ROC of over 89%, and it was more accurate than five other plasmid classification methods. Finally, as a proof of concept, we used Deeplasmid to predict new plasmids in the fish pathogen Yersinia ruckeri ATCC 29473 that has no annotated plasmids. Deeplasmid predicted with high reliability that a long assembled contig is part of a plasmid. Using long read sequencing we indeed validated the existence of a 102 kb long plasmid, demonstrating Deeplasmid's ability to detect novel plasmids.

Plasmids are mobile genetic elements that play a key role in microbial ecology and evolution by mediating horizontal transfer of important genes, such as antimicrobial resistance genes. Many microbial genomes have been sequenced by short read sequencers and have resulted in a mix of contigs that derive from plasmids or chromosomes. New tools that accurately identify plasmids are needed to elucidate new plasmid-borne genes of high biological importance. We have developed Deeplasmid, a deep learning tool for distinguishing plasmids from bacterial chromosomes based on the DNA sequence and its encoded biological data. It requires as input only assembled sequences generated by any sequencing platform and assembly algorithm and its runtime scales linearly with the number of assembled sequences. Deeplasmid achieves an AUC-ROC of over 93%, and it was much more precise than the state-of-the-art methods. Finally, as a proof of concept, we used Deeplasmid to predict new plasmids in the fish pathogen Yersinia ruckeri ATCC 29473 that has no annotated plasmids. Deeplasmid predicted with high reliability that a long assembled contig is part of a plasmid. Using long read sequencing we indeed validated the existence of a 102 Kbp long plasmid, demonstrating Deeplasmid's ability to detect novel plasmids.