Easy-Prime: a machine learning-based prime editor design tool.
Ontology highlight
ABSTRACT:
SUBMITTER: Li Y
PROVIDER: S-EPMC8377858 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:We developed ML-based methods to optimize PE design
2021-07-26 | GSE175955 | GEO
Project description:Easy-Prime: a machine learning–based prime editor design tool
| PRJNA734350 | ENA
Project description:Molecular classification of medulloblastoma is critical for the correct treatment of this malignant paediatric brain tumour. The analysis of genome-wide DNA methylation patterns has profoundly improved diagnostic precision and classification of brain tumours. However, the implementation of DNA methylation microarrays in daily clinical practice can be time-consuming, costly and inaccessible for many centres worldwide. We aimed to develop a machine-learning decision support system for rapid and cost-effective prediction of medulloblastoma methylation class directly from quantitative PCR data.
2023-08-23 | GSE210723 | GEO
Project description:BackgroundPersonalized asthma management depends on a clinician's ability to efficiently review patient's data and make timely clinical decisions. Unfortunately, efficient and effective review of these data is impeded by the varied format, location, and workflow of data acquisition, storage, and processing in the electronic health record. While machine learning (ML) and clinical decision support tools are well-positioned as potential solutions, the translation of such frameworks requires that barriers to implementation be addressed in the formative research stages.ObjectiveWe aimed to use a structured user-centered design approach (double-diamond design framework) to (1) qualitatively explore clinicians' experience with the current asthma management system, (2) identify user requirements to improve algorithm explainability and Asthma Guidance and Prediction System prototype, and (3) identify potential barriers to ML-based clinical decision support system use.MethodsAt the "discovery" phase, we first shadowed to understand the practice context. Then, semistructured interviews were conducted digitally with 14 clinicians who encountered pediatric asthma patients at 2 outpatient facilities. Participants were asked about their current difficulties in gathering information for patients with pediatric asthma, their expectations of ideal workflows and tools, and suggestions on user-centered interfaces and features. At the "define" phase, a synthesis analysis was conducted to converge key results from interviewees' insights into themes, eventually forming critical "how might we" research questions to guide model development and implementation.ResultsWe identified user requirements and potential barriers associated with three overarching themes: (1) usability and workflow aspects of the ML system, (2) user expectations and algorithm explainability, and (3) barriers to implementation in context. Even though the responsibilities and workflows vary among different roles, the core asthma-related information and functions they requested were highly cohesive, which allows for a shared information view of the tool. Clinicians hope to perceive the usability of the model with the ability to note patients' high risks and take proactive actions to manage asthma efficiently and effectively. For optimal ML algorithm explainability, requirements included documentation to support the validity of algorithm development and output logic, and a request for increased transparency to build trust and validate how the algorithm arrived at the decision. Acceptability, adoption, and sustainability of the asthma management tool are implementation outcomes that are reliant on the proper design and training as suggested by participants.ConclusionsAs part of our comprehensive informatics-based process centered on clinical usability, we approach the problem using a theoretical framework grounded in user experience research leveraging semistructured interviews. Our focus on meeting the needs of the practice with ML technology is emphasized by a user-centered approach to clinician engagement through upstream technology design.
| S-EPMC10825767 | biostudies-literature
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: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:BACKGROUND:Caenorhabditis elegans nematodes are powerful model organisms, yet quantification of visible phenotypes is still often labor-intensive, biased, and error-prone. We developed WorMachine, a three-step MATLAB-based image analysis software that allows (1) automated identification of C. elegans worms, (2) extraction of morphological features and quantification of fluorescent signals, and (3) machine learning techniques for high-level analysis. RESULTS:We examined the power of WorMachine using five separate representative assays: supervised classification of binary-sex phenotype, scoring continuous-sexual phenotypes, quantifying the effects of two different RNA interference treatments, and measuring intracellular protein aggregation. CONCLUSIONS:WorMachine is suitable for analysis of a variety of biological questions and provides an accurate and reproducible analysis tool for measuring diverse phenotypes. It serves as a "quick and easy," convenient, high-throughput, and automated solution for nematode research.
| S-EPMC5769209 | 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:The three-dimensional (3D) vectorial nature of electromagnetic waves of light has not only played a fundamental role in science but also driven disruptive applications in optical display, microscopy, and manipulation. However, conventional optical holography can address only the amplitude and phase information of an optical beam, leaving the 3D vectorial feature of light completely inaccessible. We demonstrate 3D vectorial holography where an arbitrary 3D vectorial field distribution on a wavefront can be precisely reconstructed using the machine learning inverse design based on multilayer perceptron artificial neural networks. This 3D vectorial holography allows the lensless reconstruction of a 3D vectorial holographic image with an ultrawide viewing angle of 94° and a high diffraction efficiency of 78%, necessary for floating displays. The results provide an artificial intelligence-enabled holographic paradigm for harnessing the vectorial nature of light, enabling new machine learning strategies for holographic 3D vectorial fields multiplexing in display and encryption.
| S-EPMC7164942 | biostudies-literature
Project description:Restoring gene function by the induced skipping of deleterious exons has been shown to be effective for treating genetic disorders. However, many of the clinically successful therapies for exon skipping are transient oligonucleotide-based treatments that require frequent dosing. CRISPR-Cas9 based genome editing that causes exon skipping is a promising therapeutic modality that may offer permanent alleviation of genetic disease. We show that machine learning can select Cas9 guide RNAs that disrupt splice acceptors and cause the skipping of targeted exons. We experimentally measured the exon skipping frequencies of a diverse genome-integrated library of 791 splice sequences targeted by 1,063 guide RNAs in mouse embryonic stem cells. We found that our method, SkipGuide, is able to identify effective guide RNAs with a precision of 0.68 (50% threshold predicted exon skipping frequency) and 0.93 (70% threshold predicted exon skipping frequency). We anticipate that SkipGuide will be useful for selecting guide RNA candidates for evaluation of CRISPR-Cas9-mediated exon skipping therapy.
| S-EPMC7819613 | biostudies-literature