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Yield improvements in cell factories can potentially be obtained by fine-tuning the regulatory mechanisms for gene candidates. In pursuit of such candidates, we performed RNA-sequencing of two α-amylase producing Bacillus strains and predict hundreds of putative novel non-coding transcribed regions. Surprisingly, we found among hundreds of non-coding and structured RNA candidates that non-coding genomic regions are proportionally undergoing the highest changes in expression during fermentation. Since these classes of RNA are also understudied, we targeted the corresponding genomic regions with CRIPSRi knockdown to test for any potential impact on the yield. From differentially expression analysis, we selected 53 non-coding candidates. Although CRISPRi knockdowns target both the sense and the antisense strand, the CRISPRi experiment cannot link causes for yield changes to the sense or antisense disruption. Nevertheless, we observed on several instances with strong changes in enzyme yield. The knockdown targeting the genomic region for a putative antisense RNA of the 3′ UTR of the skfA-skfH operon led to a 21% increase in yield. In contrast, the knockdown targeting the genomic regions of putative antisense RNAs of the cytochrome c oxidase subunit 1 (ctaD) , the sigma factor sigH , and the uncharacterized gene yhfT decreased yields by 31 to 43%. [ABSTRACT FROM AUTHOR]

Different TL techniques were meticulously tested using the surrogate-based dataset of Wang et al. as the "source" dataset for pre-training and one of the 10 smaller endogenous or functional knockout datasets as the "target" for refining the TL model. It is also worth noticing that CRISPRon outperforms the DeepCRISTL model trained on the dataset of Chari I et al. i on seven of the test datasets (Fig. The genome engineering revolution of the past decade has been fueled by the development of several computational tools for the design of experiments mediated by the CRISPR/Cas9 endonuclease ([3]). [Extracted from the article]

Summary The effectiveness of CRISPR/Cas9-mediated genome editing experiments largely depends on the guide RNA (gRNA) used by the CRISPR/Cas9 system for target recognition and cleavage activation. Careful design is necessary to select a gRNA with high editing efficiency at the on-target site and with minimum off-target potential. Here, we present our webserver for gRNA design with a user-friendly graphical interface, which provides interoperability between our on- and off-target prediction tools, CRISPRon and CRISPRoff, for a complete and streamlined gRNA selection. Availability and implementation The graphical interface uses the Integrative Genomic Viewer (IGV) JavaScript plugin. The backend tools are implemented in Python and C. The CRISPRon and CRISPRoff webservers and command-line tools are freely available at https://rth.dk/resources/crispr. [ABSTRACT FROM AUTHOR]

Protein association networks can be inferred from a range of resources including experimental data, literature mining and computational predictions. These types of evidence are emerging for non-coding RNAs (ncRNAs) aswell. However, integration of ncRNAs into protein association networks is challenging due to data heterogeneity. Here, we present a database of ncRNA-RNA and ncRNA-protein interactions and its integration with the STRING database of protein-protein interactions. These ncRNA associations cover four organisms and have been established from curated examples, experimental data, interaction predictions and automatic literaturemining. RAIN uses an integrative scoring scheme to assign a confidence score to each interaction. We demonstrate that RAIN outperforms the underlying microRNA-target predictions in inferring ncRNA interactions. RAIN can be operated through an easily accessibleweb interface and all interaction data can be downloaded. [ABSTRACT FROM AUTHOR]