Optimizing 5’UTRs for mRNA-delivered gene editing using deep learning
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ABSTRACT:
PROVIDER: PRJNA974439 | ENA |
REPOSITORIES: ENA
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Project description:mRNA therapeutics are revolutionizing the pharmaceutical industry, but methods to optimize the primary sequence for increased expression are still lacking. Here, we design 5’UTRs for efficient mRNA translation using deep learning. We perform polysome profiling of fully or partially randomized 5'UTR libraries in three cell types and find that UTR performance is highly correlated across cell types. We train models on all our datasets and use them to guide the design of high-performing 5’UTRs using gradient descent and generative neural networks. We experimentally test designed 5’UTRs with mRNA encoding megaTALTM gene editing enzymes for two different gene targets and in two different cell lines. We find that the designed 5’UTRs support strong gene editing activity. Editing efficiency is correlated between cell types and gene targets, although the best performing UTR was specific to one cargo and cell type. Our results highlight the potential of model-based sequence design for mRNA therapeutics.
2023-07-01 | GSE232927 | GEO
Project description:Many previous studies, including the Next Generation Sequencing (NGS)-based ones, have shown the critical roles of RNA editing in biomedicine. Direct RNA sequencing emerges as another powerful technique to advance the understanding of RNA editing by new paradigms, especially in single-molecule and long-range characterization. The urgent gap is the accurate and robust identification of RNA editing at the single-molecule and single-nucleotide resolution from direct RNA sequencing. This is challenging due to the inherent nature of the context-dependence on the raw signals, which requires enormous training data with considerable diversity. Here we propose two coupled measures to address them: 1) an abductive deep learning strategy implemented as the software ReDD fully utilizes the widely accessible NGS-based RNA editing data as indirect labels of direct RNA sequencing to achieve the detection at the single-molecule level; 2) a cloud-based platform Argo-ReDD serves as a central database for assembling large and diverse data from the community to continuously train the abductive deep learning model, which also meets the community demand of a user-friendly way to perform RNA editing analyses, such as co-occurrence analysis, quantitative analysis and gene isoform-resolved analysis, based on the specific information from direct RNA sequencing.
2024-05-08 | GSE267030 | GEO
Project description:Purpose: The goals of this study are to compare the serum extracellular vesicle (EV) delivered miRNA levels of patients with bone-metastatic prostate cancer (PCa), non-bone -metastatic PCa and benign prostatic hyperplasia (BPH), and to identify EV-delivered microRNAs in patient’s serum as indicators for bone-metastatic PCa. Methods:Serum extracellular vesicle delivered miRNA profiles of patients with bone-metastatic PCa or non-bone -metastatic PCa or BPH were generated by deep sequencing, using Illumina HiSeqTM 2500 platform Results: Using an optimized data analysis method, we mapped about 17 million sequence reads per sample. Differential analysis showed the expressions of 35 EV delivered miRNAs were significantly different between serum of patients with PCa and BPH, with a p value <0.05. the expressions of 5 EV delivered miRNAs were confirmed with qRT–PCR. Conclusions: Serum EV-delivered miR-181a-5p is a promising diagnostic biomarker for bone-metastatic PCa.
2020-01-31 | GSE134205 | GEO
Project description:Purpose: The goals of this study are to compare the serum extracellular vesicle (EV) delivered miRNA levels of patients with bone-metastatic prostate cancer (PCa), non-bone -metastatic PCa and benign prostatic hyperplasia (BPH), and to identify EV-delivered microRNAs in patient’s serum as indicators for bone-metastatic PCa. Methods:Serum extracellular vesicle delivered miRNA profiles of patients with bone-metastatic PCa or non-bone -metastatic PCa or BPH were generated by miRNA chip array, using Agilent-070156 Human_miRNA_V21.0_Microarray plateform. Results: Differential analysis showed the expressions of 27 EV delivered miRNAs were significantly different between serum of patients with bone-metastatic PCa and non-bone-metastatic PCa with a p value <0.05. the expressions of 5 EV delivered miRNAs were confirmed with qRT–PCR. Conclusions: Serum EV-delivered miR-181a-5p is a promising diagnostic biomarker for bone-metastatic PCa.
2020-01-31 | GSE134266 | GEO
Project description:Optimizing genome editing strategy by primer-extension-mediated sequencing
| PRJNA477827 | ENA
Project description:Optimizing CRISPR/Cas9 editing of repetitive single nucleotide variants
| PRJEB52736 | ENA
Project description:High specificity of e\ngineered nucleases ensures precise genome editing. Couple methods were developed to identify off-target sites of CRISPR/Cas9, but hardly any high-throughput sequencing method can unequivocally determine their targeting efficiencies. Here we describe a comprehensive method, primer-extension-mediated sequencing (PEM-seq), which could sensitively detect CRISPR/Cas9 off-target sites as well as assess their editing efficiency by quantifying DNA interference events at on-target sites. Demonstrated by PEM-seq, we generated a high-fidelity Cas9 variant FeCas9 that possesses similar targeting ability as the wild-type while with extremely low off-target activities. Moreover, we provided further evidences for the broader range of xCas9 protospacer adjacent sequence. We also found the AcrIIA4 inhibitor could inhibit both on- and off-target activities of SpCas9, but it suppressed SpCas9 cleavage at the off-target loci not so efficiently as at the on-target sites. Finally, we believe PEM-seq is applicable to optimizing genome editing strategy for clinical purpose or creating animal model.
2019-04-03 | GSE116231 | GEO
Project description:ReDD: RNA editing detection by direct RNA sequencing and deep learning
| PRJNA1109379 | ENA
Project description:Optimizing high molecular weight extractions
| PRJNA1114081 | ENA