Machine learning-based prediction of the activity and specificity of Cas9 variants in gene editing
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA968010 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR24458100_1.fastq.gz | Fastqsanger.gz | |||
| SRR24458100_2.fastq.gz | Fastqsanger.gz | |||
| SRR24458101_1.fastq.gz | Fastqsanger.gz | |||
| SRR24458101_2.fastq.gz | Fastqsanger.gz | |||
| SRR24458102_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 128 |
Similar Datasets
Project description:Machine learning-based prediction of the activity and specificity of Cas9 variants in gene editing
2024-08-22 | GSE231840 | GEO
Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status. RNA was isolated from 199 primary breast cancer patients. A machine learning classifier was built to predict ER status using only three gene features.
2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress
Project description:The RNA polymerase II core promoter is the site of convergence of the signals that lead to the initiation of transcription. Here, we perform a comparative analysis of the downstream core promoter region (DPR) in Drosophila and humans by using machine learning. These studies revealed a distinct human-specific version of the DPR and led to the use of the machine learning models for the identification of synthetic extreme DPR motifs with specificity for human transcription factors relative to Drosophila factors, and vice versa. More generally, machine learning models could be analogously used to design synthetic promoter elements with customized functional properties.
2023-04-06 | GSE225570 | GEO
Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status.
2013-01-01 | GSE29210 | GEO
Project description:In gas chromatography-mass spectrometry-based untargeted metabolomics, metabolites are identified by comparing mass spectra and chromatographic retention time with reference databases or standard materials. In that sense, machine learning has been used to predict the retention time of metabolites lacking reference data. However, the retention time prediction of trimethylsilyl derivatives of metabolites, typically analyzed in untargeted metabolomics using gas chromatography, has been poorly explored. Here, we provide a rationalized framework for machine learning-based retention time prediction of trimethylsilyl derivatives of metabolites in gas chromatography. We compared different machine learning paradigms, in addition to exploring the influence of the computational molecular structure representation to train the prediction models: fingerprint class and fingerprint calculation software. Our study challenged predicted retention time when using chemical ionization and electron impact ionization sources in simulated and real cases, demonstrating a good correct identity ranking capability by machine learning, despite observing a limited false identity filtering power in cases where a spectrum or a monoisotopic mass match to multiple candidates. Specifically, machine learning prediction yielded median absolute and relative retention index (relative retention time) errors of 37.1 retention index units and 2%, respectively. In addition, fingerprint class and fingerprint calculation software, as well as the molecular structural similarity between the training and test or real case sets, showed to be critical modulators of the prediction performance. Finally, we leveraged the structural similarity between the training and test or real case set to determine the probability that the prediction error is below a specific threshold. Overall, our study demonstrates that predicted retention time can provide insights into the true structure of unknown metabolites by ranking from the most to the least plausible molecular identity, and sets the guidelines to assess the confidence in metabolite identification using predicted retention time data.
2022-05-26 | MTBLS2841 | MetaboLights
Project description:Machine learning methods, particularly neural networks trained on large datasets, are transforming how scientists approach scientific discovery and experimental design. However, current state-of-the-art neural networks are limited by their uninterpretability: despite providing accurate predictions, they cannot describe how they arrived at their predictions. Here, using an ``interpretable-by-design'' approach, we present a neural network model that provides insights into RNA splicing, a fundamental process in the transfer of genomic information into functional biochemical products. Although we designed our model to emphasize interpretability, its predictive accuracy is on par with state-of-the-art models. To demonstrate the model's interpretability, we introduce a visualization that, for any given exon, allows us to trace and quantify the entire decision process from input sequence to output splicing prediction. Importantly, the model revealed novel components of the splicing logic, which we experimentally validated. This study highlights how interpretable machine learning can advance scientific discovery.
2022-10-01 | GSE200096 | GEO
Project description:To identify genes with cell-lineage-specific expression not accessible by experimental micro-dissection, we developed a genome-scale iterative method, in-silico nano-dissection, which leverages high-throughput functional-genomics data from tissue homogenates using a machine-learning framework. This study applied nano-dissection to chronic kidney disease and identified transcripts specific to podocytes, key cells in the glomerular filter responsible for hereditary proteinuric syndromes and acquired CKD. In-silico prediction accuracy exceeded predictions derived from fluorescence-tagged-murine podocytes, identified genes recently implicated in hereditary glomerular disease and predicted genes significantly correlated with kidney function. The nano-dissection method is broadly applicable to define lineage specificity in many functional and disease contexts. We applied a machine-learning framework on high-throughput gene expression data from human kidney biopsy tissue homogenates and predict novel podocyte-specific genes. The prediction was validated by Human Protein Atlas at protein level. Prediction accuracy was compared with predictions derived from experimental approach using fluorescence-tagged-murine podocytes.
2013-08-06 | E-GEOD-47184 | biostudies-arrayexpress
Project description:To identify genes with cell-lineage-specific expression not accessible by experimental micro-dissection, we developed a genome-scale iterative method, in-silico nano-dissection, which leverages high-throughput functional-genomics data from tissue homogenates using a machine-learning framework. This study applied nano-dissection to chronic kidney disease and identified transcripts specific to podocytes, key cells in the glomerular filter responsible for hereditary proteinuric syndromes and acquired CKD. In-silico prediction accuracy exceeded predictions derived from fluorescence-tagged-murine podocytes, identified genes recently implicated in hereditary glomerular disease and predicted genes significantly correlated with kidney function. The nano-dissection method is broadly applicable to define lineage specificity in many functional and disease contexts. We applied a machine-learning framework on high-throughput gene expression data from human kidney biopsy tissue homogenates and predict novel podocyte-specific genes. The prediction was validated by Human Protein Atlas at protein level. Prediction accuracy was compared with predictions derived from experimental approach using fluorescence-tagged-murine podocytes.
2013-08-06 | E-GEOD-47183 | biostudies-arrayexpress
Project description:This study aims to predict the activity and specificity of CRISPR/Cas9 by deep learning at genome-scale among different cell lines. Here, we have focused on embracing and modifying a system for evaluating SpCas9 activity of on-target and off-target using >1,000,000 guide RNAs (gRNAs) covering ~20,000 protein-coding genes and ~10,000 non-coding genes in synthetic constructs with a high-throughput manner. With the help of deep learning algorithms in the field of artificial intelligence, three prediction models with the best generalization performance now are constructed: Aidit_Cas9-ON, Aidit_Cas9-OFF, and Aidit_Cas9-DSB. Moreover, through systematically investigating the influence of diverse cellular environment on gRNA activity and specificity, we noticed that distinct features are favored from H1 cell line compared with the other 2 cell lines for on-target activity and the overall distribution of repair outcomes is markedly different across 3 cell lines, especially in Jurkat. Finally, we identify a key effect protein DNTT strongly influences editing outcomes induced by CRISPR/Cas9. We confirm that this study will greatly facilitate CRISPR-based genome editing.
2023-05-24 | GSE181774 | GEO
Project description:A machine learning breast cancer prediction model based on a panel from circulating exosomal miRNAs
| PRJNA808405 | ENA