Kiel, N. | Search bioRxiv

bioRxiv2023

Helixer--de novo Prediction of Primary Eukaryotic Gene Models Combining Deep Learning and a Hidden Markov Model.

AO_SCPLOWBSTRACTC_SCPLOWGene structural annotation is a critical step in obtaining biological knowledge from genome sequences yet remains a major challenge in genomics projects. Current de novo Hidden Markov Models are limited in their capacity to model biological complexity; while current pipelines are resource-intensive and their results vary in quality with the available extrinsic data. Here, we build on our previous work in applying Deep Learning to gene calling to make a fully applicable, fast and user friendly tool for predicting primary gene models from DNA sequence alone. The quality is state-of-the-art, with predictions scoring closer by most measures to the references than to predictions from other de novo tools. Helixers predictions can be used as is or could be integrated in pipelines to boost quality further. Moreover, there is substantial potential for further improvements and advancements in gene calling with Deep Learning. Helixer is open source and available at https://github.com/weberlab-hhu/Helixer A web interface is available at https://www.plabipd.de/helixer_main.html

bioinformatics↗

bioRxiv2023

Targeted gene deletion with SpCas9 and multiple guide RNAs in Arabidopsis thaliana: four are better than two

BackgroundIn plant genome editing, RNA-guided nucleases such as Cas9 from Streptococcus pyogenes (SpCas9) predominantly induce small insertions or deletions at target sites. This can be used for inactivation of protein-coding genes by frame shift mutations. However, in some cases, it may be advantageous to delete larger chromosomal segments. This is achieved by simultaneously inducing double strand breaks upstream and downstream of the fragment to be deleted. Experimental approaches for deletion induction have not been systematically evaluated. ResultsWe designed three pairs of guide RNAs for deletion of the Arabidopsis WRKY30 locus (~2.2 kb). We tested how the combination of guide RNA pairs and co-expression of the exonuclease TREX2 affect the frequency of wrky30 deletions in editing experiments. Our data demonstrate that compared to one pair of guide RNAs, two pairs increase the frequency of chromosomal deletions. The exonuclease TREX2 enhanced mutation frequency at individual target sites and shifted the mutation profile towards larger deletions. However, TREX2 did not elevate the frequency of chromosomal deletions. ConclusionsMultiplex editing with at least two pairs of guide RNAs (four guide RNAs in total) elevates the frequency of chromosomal deletions, and thus simplifies the selection of corresponding mutants. Co-expression of the TREX2 exonuclease can be used as a general strategy to increase editing efficiency in Arabidopsis without obvious negative effects.

plant biology↗