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: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: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: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