Biology subjects

Botella, J. R.

Publications and source records attributed to Botella, J. R..

2 recordsLinked to original sources

bioRxiv2025

Evaluation of computational tools for predicting CRISPR gRNA on-target efficiency in plants.

CRISPR technologies has become an integral part of plant biotechnology, synthetic biology and basic plant research, routinely used by researchers for targeted genome modifications. CRISPR guide RNAs (gRNAs) undermines the highly programmable nature of CRISPR, enabling site-specific genome editing. However, different gRNA targets showed highly variable on-target effectiveness and poor gRNA design could amount to wasting valuable scientific resources. There has been broad development of computational and web-based tools for gRNA efficiency predictions but their performances in plant genome editing remains controversial or untested. Hence, in this study, we systematically evaluated over 20 accessible, web-based in silico gRNA on-target efficiency prediction tools using an experimental plant genome editing dataset. Excitingly, we identified multiple tools, mostly developed using machine learning, that were highly predictive of gRNA on-target genome editing efficiency in planta. The prediction scores assigned to gRNAs in the dataset by these tools were significantly correlated with the frequency of CRISPR-mediated InDels in plants. Furthermore, we evaluated efficiency prediction scores available on popular platforms such as CRISPOR and CRISPR-P which contain large numbers of non-model plant genomes. Our analysis showed that some prediction scores on CRISPOR performed quite well which allows efficient integration of on-target and off-target predictions. Overall, we believe that our study provided insights on improving gRNA design during conventional plant genome editing workflows and should also help unfamiliar researchers interested in CRISPR/SpCas9 genome editing.

plant biology↗

bioRxiv2020

Development and on-site evaluation of an easy-to-perform and low-cost food pathogen diagnostic workflow for low-resource communities

Food-borne diseases are a leading cause of illness and death in many developing countries and thus, there is a real need to develop affordable and practical technologies that can help improve food safety in these countries. The ability to efficiently identify food pathogens is essential to allow national regulatory authorities to monitor food quality and implement safety protocols. In this study, we have developed a simple, low-cost ($0.76 (USD)) complete food pathogen diagnostic workflow ideally suited for deployment in low-resource environments that uses a simple four step process (sample enrichment, cell lysis, DNA amplification, and naked-eye readout). The minimal number of steps and equipment involved in our diagnostic workflow, as well as the simplicity of the yes/no flocculation readout, allows non-technical personnel to perform and interpret the assay. To evaluate the systems performance, we tested the entire system on fresh produce samples collected from local farms and markets in Cambodia for the presence of the E. coli O157 O-antigen polymerase, wzy. Although this was a proof-of-concept study, our system successfully revealed a clear correlation between the origin and condition of the produce collected and their likelihood of contamination. In conclusion, we believe that our easy-to-perform diagnostic system can have a significant impact on improving food quality and human health if adopted by regulatory authorities in developing countries due to the assays simplicity, affordability, and adaptability.

molecular biology↗