GNL-Scorer: a generalized model for predicting CRISPR on-target activity by machine learning and featurization.
Ontology highlight
ABSTRACT:
SUBMITTER: Wang J
PROVIDER: S-EPMC7883820 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:It has been possible to create tools to predict single guide RNA (sgRNA) activity in the CRISPR/Cas9 system derived from Streptococcus pyogenes due to the large amount of data that has been generated in sgRNA library screens. However, with the discovery of additional CRISPR systems from different bacteria, which show potent activity in eukaryotic cells, the approach of generating large data sets for each of these systems to predict their activity is not tractable. Here, we present a new guide RNA tool that can predict sgRNA activity across multiple CRISPR systems. In addition to predicting activity for Cas9 from S. pyogenes and Streptococcus thermophilus CRISPR1, we experimentally demonstrate that our algorithm can predict activity for Cas9 from Staphylococcus aureus and S. thermophilus CRISPR3. We also have made available a new version of our software, sgRNA Scorer 2.0, which will allow users to identify sgRNA sites for any PAM sequence of interest.
| S-EPMC5793212 | biostudies-literature
Project description:We conducted a two-vector CRISPR/Cas13d proliferation screening experiment. The screening library contains 10,830 sgRNAs targeting 192 protein-coding genes and 234 lncRNAs, and the screening experiment was performed using a melanoma cell line A375. It provides a unique dataset to model Cas13d sgRNA efficiency and specificity. We designed a deep learning model, named DeepCas13, to predict the sgRNA on-target activity with high accuracy from sgRNA sequences and RNA secondary structures.
2023-01-16 | GSE183256 | GEO
Project description:Genome editing through the development of CRISPR (Clustered Regularly Interspaced Short Palindromic Repeat)-Cas technology has revolutionized many fields in biology. Beyond Cas9 nucleases, Cas12a (formerly Cpf1) has emerged as a promising alternative to Cas9 for editing AT-rich genomes. Despite the promises, guide RNA efficiency prediction through computational tools search still lacks accuracy. Through a computational meta-analysis, here we report that Cas12a target and off-target cleavage behavior are a factor of nucleotide bias combined with nucleotide mismatches relative to the protospacer adjacent motif (PAM) site. These features helped to train a Random Forest machine learning model to improve the accuracy by at least 15% over existing algorithms to predict guide RNA efficiency for the Cas12a enzyme. Despite the progresses, our report underscores the need for more representative datasets and further benchmarking to reliably and accurately predict guide RNA efficiency and off-target effects for Cas12a enzymes.
| S-EPMC10581463 | biostudies-literature
Project description:BackgroundDiscover possible Drug Target Interactions (DTIs) is a decisive step in the detection of the effects of drugs as well as drug repositioning. There is a strong incentive to develop effective computational methods that can effectively predict potential DTIs, as traditional DTI laboratory experiments are expensive, time-consuming, and labor-intensive. Some technologies have been developed for this purpose, however large numbers of interactions have not yet been detected, the accuracy of their prediction still low, and protein sequences and structured data are rarely used together in the prediction process.MethodsThis paper presents DTIs prediction model that takes advantage of the special capacity of the structured form of proteins and drugs. Our model obtains features from protein amino-acid sequences using physical and chemical properties, and from drugs smiles (Simplified Molecular Input Line Entry System) strings using encoding techniques. Comparing the proposed model with different existing methods under K-fold cross validation, empirical results show that our model based on ensemble learning algorithms for DTI prediction provide more accurate results from both structures and features data.ResultsThe proposed model is applied on two datasets:Benchmark (feature only) datasets and DrugBank (Structure data) datasets. Experimental results obtained by Light-Boost and ExtraTree using structures and feature data results in 98 % accuracy and 0.97 f-score comparing to 94 % and 0.92 achieved by the existing methods. Moreover, our model can successfully predict more yet undiscovered interactions, and hence can be used as a practical tool to drug repositioning. A case study of applying our prediction model on the proteins that are known to be affected by Corona viruses in order to predict the possible interactions among these proteins and existing drugs is performed. Also, our model is applied on Covid-19 related drugs announced on DrugBank. The results show that some drugs like DB00691 and DB05203 are predicted with 100 % accuracy to interact with ACE2 protein. This protein is a self-membrane protein that enables Covid-19 infection. Hence, our model can be used as an effective tool in drug reposition to predict possible drug treatments for Covid-19.
| S-EPMC8256690 | biostudies-literature
Project description:A major challenge in the application of the CRISPR-Cas13d system is to accurately predict its guide-dependent on-target and off-target effect. Here, we perform CRISPR-Cas13d proliferation screens and design a deep learning model, named DeepCas13, to predict the on-target activity from guide sequences and secondary structures. DeepCas13 outperforms existing methods to predict the efficiency of guides targeting both protein-coding and non-coding RNAs. Guides targeting non-essential genes display off-target viability effects, which are closely related to their on-target efficiencies. Choosing proper negative control guides during normalization mitigates the associated false positives in proliferation screens. We apply DeepCas13 to the guides targeting lncRNAs, and identify lncRNAs that affect cell viability and proliferation in multiple cell lines. The higher prediction accuracy of DeepCas13 over existing methods is extensively confirmed via a secondary CRISPR-Cas13d screen and quantitative RT-PCR experiments. DeepCas13 is freely accessible via http://deepcas13.weililab.org. Application of CRISPR-Cas13d is limited by the inability to predict on- and off-targets. Here the authors perform CRISPR-Cas13d proliferation screens followed by modeling of Cas13d on- and off-targets; they design a deep learning model, DeepCas13, to predict the on-target activity of a gRNA.
| S-EPMC9912244 | biostudies-literature
Project description:Manually identifying and correcting errors in protein models can be a slow process, but improvements in validation tools and automated model-building software can contribute to reducing this burden. This article presents a new correctness score that is produced by combining multiple sources of information using a neural network. The residues in 639 automatically built models were marked as correct or incorrect by comparing them with the coordinates deposited in the PDB. A number of features were also calculated for each residue using Coot, including map-to-model correlation, density values, B factors, clashes, Ramachandran scores, rotamer scores and resolution. Two neural networks were created using these features as inputs: one to predict the correctness of main-chain atoms and the other for side chains. The 639 structures were split into 511 that were used to train the neural networks and 128 that were used to test performance. The predicted correctness scores could correctly categorize 92.3% of the main-chain atoms and 87.6% of the side chains. A Coot ML Correctness script was written to display the scores in a graphical user interface as well as for the automatic pruning of chains, residues and side chains with low scores. The automatic pruning function was added to the CCP4i2 Buccaneer automated model-building pipeline, leading to significant improvements, especially for high-resolution structures.
| S-EPMC7397494 | biostudies-literature
Project description:This retrospective cohort study aimed to develop and evaluate a machine-learning algorithm for predicting oliguria, a sign of acute kidney injury (AKI). To this end, electronic health record data from consecutive patients admitted to the intensive care unit (ICU) between 2010 and 2019 were used and oliguria was defined as a urine output of less than 0.5 mL/kg/h. Furthermore, a light-gradient boosting machine was used for model development. Among the 9,241 patients who participated in the study, the proportions of patients with urine output < 0.5 mL/kg/h for 6 h and with AKI during the ICU stay were 27.4% and 30.2%, respectively. The area under the curve (AUC) values provided by the prediction algorithm for the onset of oliguria at 6 h and 72 h using 28 clinically relevant variables were 0.964 (a 95% confidence interval (CI) of 0.963-0.965) and 0.916 (a 95% CI of 0.914-0.918), respectively. The Shapley additive explanation analysis for predicting oliguria at 6 h identified urine values, severity scores, serum creatinine, oxygen partial pressure, fibrinogen/fibrin degradation products, interleukin-6, and peripheral temperature as important variables. Thus, this study demonstrates that a machine-learning algorithm can accurately predict oliguria onset in ICU patients, suggesting the importance of oliguria in the early diagnosis and optimal management of AKI.
| S-EPMC10784288 | biostudies-literature
Project description:A computational technique for predicting the DTIs has now turned out to be an indispensable job during the process of drug finding. It tapers the exploration room for interactions by propounding possible interaction contenders for authentication through experiments of wet-lab which are known for their expensiveness and time consumption. Chemogenomics, an emerging research area focused on the systematic examination of the biological impact of a broad series of minute molecular-weighting ligands on a broad raiment of macromolecular target spots. Additionally, with the advancement in time, the complexity of the algorithms is increasing which may result in the entry of big data technologies like Spark in this field soon. In the presented work, we intend to offer an inclusive idea and realistic evaluation of the computational Drug Target Interaction projection approaches, to perform as a guide and reference for researchers who are carrying out work in a similar direction. Precisely, we first explain the data utilized in computational Drug Target Interaction prediction attempts like this. We then sort and explain the best and most modern techniques for the prediction of DTIs. Then, a realistic assessment is executed to show the projection performance of several illustrative approaches in various situations. Ultimately, we underline possible opportunities for additional improvement of Drug Target Interaction projection enactment and also linked study objectives.
| S-EPMC7176722 | biostudies-literature
Project description:The COVID-19 pandemic has been spreading worldwide since December 2019, presenting an urgent threat to global health. Due to the limited understanding of disease progression and of the risk factors for the disease, it is a clinical challenge to predict which hospitalized patients will deteriorate. Moreover, several studies suggested that taking early measures for treating patients at risk of deterioration could prevent or lessen condition worsening and the need for mechanical ventilation. We developed a predictive model for early identification of patients at risk for clinical deterioration by retrospective analysis of electronic health records of COVID-19 inpatients at the two largest medical centers in Israel. Our model employs machine learning methods and uses routine clinical features such as vital signs, lab measurements, demographics, and background disease. Deterioration was defined as a high NEWS2 score adjusted to COVID-19. In the prediction of deterioration within the next 7-30 h, the model achieved an area under the ROC curve of 0.84 and an area under the precision-recall curve of 0.74. In external validation on data from a different hospital, it achieved values of 0.76 and 0.7, respectively.
| S-EPMC8850417 | biostudies-literature
Project description:ObjectiveThe safe delivery of electrical current to neural tissue depends on many factors, yet previous methods for predicting tissue damage rely on only a few stimulation parameters. Here, we report the development of a machine learning approach that could lead to a more reliable method for predicting electrical stimulation-induced tissue damage by incorporating additional stimulation parameters.ApproachA literature search was conducted to build an initial database of tissue response information after electrical stimulation, categorized as either damaging or non-damaging. Subsequently, we used ordinal encoding and random forest for feature selection, and investigated four machine learning models for classification: Logistic Regression, K-nearest Neighbor, Random Forest, and Multilayer Perceptron. Finally, we compared the results of these models against the accuracy of the Shannon equation.Main resultsWe compiled a database with 387 unique stimulation parameter combinations collected from 58 independent studies conducted over a period of 47 years, with 195 (51%) categorized as non-damaging and 190 (49%) categorized as damaging. The features selected for building our model with a Random Forest algorithm were: waveform shape, geometric surface area, pulse width, frequency, pulse amplitude, charge per phase, charge density, current density, duty cycle, daily stimulation duration, daily number of pulses delivered, and daily accumulated charge. The Shannon equation yielded an accuracy of 63.9% using a k value of 1.79. In contrast, the Random Forest algorithm was able to robustly predict whether a set of stimulation parameters was classified as damaging or non-damaging with an accuracy of 88.3%.SignificanceThis novel Random Forest model can facilitate more informed decision making in the selection of neuromodulation parameters for both research studies and clinical practice. This study represents the first approach to use machine learning in the prediction of stimulation-induced neural tissue damage, and lays the groundwork for neurostimulation driven by machine learning models.
| S-EPMC10614958 | biostudies-literature