Similar Datasets
Project description:Prime editing is a revolutionary genome-editing technology that can make a wide range of precise edits in DNA. However, designing highly efficient prime editors (PEs) remains challenging. We develop Easy-Prime, a machine learning-based program trained with multiple published data sources. Easy-Prime captures both known and novel features, such as RNA folding structure, and optimizes feature combinations to improve editing efficiency. We provide optimized PE design for installation of 89.5% of 152,351 GWAS variants. Easy-Prime is available both as a command line tool and an interactive PE design server at: http://easy-prime.cc/ .
| S-EPMC8377858 | biostudies-literature
Project description:Easy-Prime: a machine learning–based prime editor design tool
| PRJNA734350 | ENA
Project description:Prime editor (PE) has been recently developed to induce efficient and precise on-target editing, whereas its guide RNA (gRNA)-independent off-target effects remain unknown. Here, we used whole-genome and whole-transcriptome sequencing to determine gRNA-independent off-target mutations in cells expanded from single colonies, in which PE generated precise editing at on-target sites. We found that PE triggered no observable gRNA-independent off-target mutation genome-wide or transcriptome-wide in transfected human cells, highlighting its high specificity.
2022-02-03 | GSE178112 | GEO
Project description:Prime editor (PE) has been recently developed to induce efficient and precise on-target editing, whereas its guide RNA (gRNA)-independent off-target effects remain unknown. Here, we used whole-genome and whole-transcriptome sequencing to determine gRNA-independent off-target mutations in cells expanded from single colonies, in which PE generated precise editing at on-target sites. We found that PE triggered no observable gRNA-independent off-target mutation genome-wide or transcriptome-wide in transfected human cells, highlighting its high specificity.
2022-02-03 | GSE178111 | GEO
Project description:Recent discoveries of extreme cellular diversity in the brain warrant rapid development of technologies to access specific cell populations, enabling characterization of their roles in behavior and in disease states. Available approaches for engineering targeted technologies for new neuron subtypes are low-yield, involving intensive transgenic strain or virus screening. Here, we introduce SNAIL (Specific Nuclear-Anchored Independent Labeling), a new virus-based strategy for cell labeling and nuclear isolation from heterogeneous tissue. SNAIL works by leveraging machine learning and other computational approaches to identify DNA sequence features that confer cell type-specific gene activation and using them to make a probe that drives an affinity purification-compatible reporter gene. As a proof of concept, we designed and validated two novel SNAIL probes that target parvalbumin-expressing (PV) neurons. Furthermore, we show that nuclear isolation using SNAIL in wild type mice is sufficient to capture characteristic open chromatin features of PV neurons in the cortex, striatum, and external globus pallidus. Expansion of this technology has broad applications in cell type-specific observation, manipulation, and therapeutics across species and disease models.
2021-06-01 | GSE171549 | GEO
Project description:Nucleotide repeat expansion disorders, a group of genetic diseases characterized by the expansion of specific DNA sequences, pose significant challenges to treatment and therapy development. Here, we present a precise and programmable method called prime editor–mediated correction of nucleotide repeat expansion (PE-CORE) for correcting pathogenic nucleotide repeat expansion. PE-CORE leverages a prime editor and paired pegRNAs to achieve targeted correction of repeat sequences. We demonstrate the effectiveness of PE-CORE in HEK293T cells and patient-derived induced pluripotent stem cells (iPSCs). Specifically, we focus on spinal and bulbar muscular atrophy and spinocerebellar ataxia type, two diseases associated with nucleotide repeat expansion. Our results demonstrate the successful correction of pathogenic expansions in iPSCs and subsequent differentiation into motor neurons. Specifically, we detect distinct downshifts in the size of both the mRNA and protein, confirming the functional correction of the iPSC-derived motor neurons. These findings highlight PE-CORE as a precision tool for addressing the intricate challenges of nucleotide repeat expansion disorders, paving the way for targeted therapies and potential clinical applications.
2024-06-12 | GSE248085 | GEO
Project description:The differentiation of induced pluripotent stem cells (iPSCs) into diverse functional cell types provides a promising solution to support drug discovery, disease modeling, and regenerative medicine. However, functional cell differentiation is currently limited by the substantial line-to-line and batch-to-batch variability, which severely impede the progress of scientific research and the manufacturing of cell products. For instance, iPSC-to-cardiomyocyte (CM) differentiation is vulnerable with great sensitivity to inappropriate doses of CHIR99021 (CHIR) that are applied in the initial stage of mesoderm differentiation. Here, by harnessing live-cell bright-field imaging and machine learning (ML), we realize real-time cell recognition in the entire differentiation process, e.g., CMs, cardiac progenitor cells (CPCs), iPSC clones, and even misdifferentiated cells. This enables non-invasive prediction of differentiation efficiency, purification of ML-recognized CMs and CPCs for reducing cell contamination, early assessment of the CHIR dose for correcting the misdifferentiation trajectory, and evaluation of initial iPSC colonies for controlling the start point of differentiation, all of which provide a more invulnerable differentiation method with resistance to variability. Moreover, with the established ML models as a readout for the chemical screen, we identify a CDK8 inhibitor to further improve the cell resistance to the overdose of CHIR. Together, this study indicates that artificial intelligence is able to guide and iteratively optimize iPSC differentiation to achieve consistently high efficiency across cell lines and batches, providing a better understanding and rational modulation of the differentiation process for functional cell manufacturing in biomedical applications. With the assistance of RNA sequencing, we transcriptomically characterize the image-recognized CPCs (IR-CPCs), the cells treated with different CHIR doses at stage I, iPSC-CM treated with or without BI-1347, and cells treated with or without BI-1347 at stage I.
2023-04-01 | GSE226159 | GEO
Project description:BackgroundMachine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable information from complex datasets. However, developing an effective and robust ML pipeline can present a real challenge, demanding considerable time and effort, thereby impeding research progress. Existing tools in this landscape require a profound understanding of ML principles and programming skills. Furthermore, users are required to engage in the comprehensive configuration of their ML pipeline to obtain optimal performance.ResultsTo address these challenges, we have developed a novel tool called Machine Learning Made Easy (MLme) that streamlines the use of ML in research, specifically focusing on classification problems at present. By integrating four essential functionalities, namely Data Exploration, AutoML, CustomML, and Visualization, MLme fulfills the diverse requirements of researchers while eliminating the need for extensive coding efforts. To demonstrate the applicability of MLme, we conducted rigorous testing on six distinct datasets, each presenting unique characteristics and challenges. Our results consistently showed promising performance across different datasets, reaffirming the versatility and effectiveness of the tool. Additionally, by utilizing MLme's feature selection functionality, we successfully identified significant markers for CD8+ naive (BACH2), CD16+ (CD16), and CD14+ (VCAN) cell populations.ConclusionMLme serves as a valuable resource for leveraging machine learning (ML) to facilitate insightful data analysis and enhance research outcomes, while alleviating concerns related to complex coding scripts. The source code and a detailed tutorial for MLme are available at https://github.com/FunctionalUrology/MLme.
| S-EPMC10349995 | biostudies-literature
Project description:BackgroundThe rapid expansion of the CRISPR toolbox through tagging effector domains to either enzymatically inactive Cas9 (dCas9) or Cas9 nickase (nCas9) has led to several promising new gene editing strategies. Recent additions include CRISPR cytosine or adenine base editors (CBEs and ABEs) and the CRISPR prime editors (PEs), in which a deaminase or reverse transcriptase are fused to nCas9, respectively. These tools hold great promise to model and correct disease-causing mutations in animal and plant models. But so far, no widely-available tools exist to automate the design of both BE and PE reagents.ResultsWe developed PnB Designer, a web-based application for the design of pegRNAs for PEs and guide RNAs for BEs. PnB Designer makes it easy to design targeting guide RNAs for single or multiple targets on a variant or reference genome from organisms spanning multiple kingdoms. With PnB Designer, we designed pegRNAs to model all known disease causing mutations available in ClinVar. Additionally, PnB Designer can be used to design guide RNAs to install or revert a SNV, scanning the genome with one CBE and seven different ABE PAM variants and returning the best BE to use. PnB Designer is publicly accessible at http://fgcz-shiny.uzh.ch/PnBDesigner/ CONCLUSION: With PnB Designer we created a user-friendly design tool for CRISPR PE and BE reagents, which should simplify choosing editing strategy and avoiding design complications.
| S-EPMC7923538 | biostudies-literature
Project description:Interventions: Phenylephrine (PE):When hypotension occurred,Patients received PE infusion at a starting rate of 5 ml/h of 100µg/ml solution (prepared by diluting 10 mg of PE in 100 ml saline);Norepinephrine (NE):When hypotension occurred,Patients received NE infusion at a starting rate of 5 ml/h of 8 ug/ml solution (prepared by diluting 2 mg NE in 250 ml saline)
Primary outcome(s): First flatus time after surgery;All complications, all-cause mortality, and readmission rates within 28 days after surgery
Study Design: Parallel
| 80763 | ecrin-mdr-crc