QPromoters: Sequence based prediction of promoter strength in<i>Saccharomyces cerevisiae</i>

Dh, Liya; Elanchezhian, Mirudula; Pahari, Mukulika; Nm, Anand; Suresh, S.; N, Balaji; Ak, Jainarayanan

doi:10.1101/2021.04.27.441621

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…With higher nucleotide differences of 35%, 200 samples provided reasonable prediction qualities (g). Libraries including multiple sigma factors (e, f) (Gilman et al, 2019) resulted in lower prediction qualities, which parallels studies on heterogenous data in E. coli (Cambray et al, 2018) and yeast (Liya et al, 2021). More libraries are necessary to narrow the required sample size over the whole sequence diversity spectrum.…”

Section: Rfmentioning

confidence: 86%

Insight to Gene Expression From Promoter Libraries With the Machine Learning Workflow Exp2Ipynb

et al. 2021

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Metabolic engineering relies on modifying gene expression to regulate protein concentrations and reaction activities. The gene expression is controlled by the promoter sequence, and sequence libraries are used to scan expression activities and to identify correlations between sequence and activity. We introduce a computational workflow called Exp2Ipynb to analyze promoter libraries maximizing information retrieval and promoter design with desired activity. We applied Exp2Ipynb to seven prokaryotic expression libraries to identify optimal experimental design principles. The workflow is open source, available as Jupyter Notebooks and covers the steps to 1) generate a statistical overview to sequence and activity, 2) train machine-learning algorithms, such as random forest, gradient boosting trees and support vector machines, for prediction and extraction of feature importance, 3) evaluate the performance of the estimator, and 4) to design new sequences with a desired activity using numerical optimization. The workflow can perform regression or classification on multiple promoter libraries, across species or reporter proteins. The most accurate predictions in the sample libraries were achieved when the promoters in the library were recognized by a single sigma factor and a unique reporter system. The prediction confidence mostly depends on sample size and sequence diversity, and we present a relationship to estimate their respective effects. The workflow can be adapted to process sequence libraries from other expression-related problems and increase insight to the growing application of high-throughput experiments, providing support for efficient strain engineering.

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Section: Rfmentioning

confidence: 86%

Insight to Gene Expression From Promoter Libraries With the Machine Learning Workflow Exp2Ipynb

et al. 2021

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iProm-Yeast: Prediction Tool for Yeast Promoters Based on ML Stacking

Shujaat,

Yoo,

Tayara

et al. 2024

CBIO

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Background and Objective:: Gene promoters play a crucial role in regulating gene transcription by serving as DNA regulatory elements near transcription start sites. Despite numerous approaches, including alignment signal and content-based methods for promoter prediction, accurately identifying promoters remains challenging due to the lack of explicit features in their sequences. Consequently, many machine learning and deep learning models for promoter identification have been presented, but the performance of these tools is not precise. Most recent investigations have concentrated on identifying sigma or plant promoters. While the accurate identification of Saccharomyces cerevisiae promoters remains an underexplored area. In this study, we introduced “iPromyeast”, a method for identifying yeast promoters. Using genome sequences from the eukaryotic yeast Saccharomyces cerevisiae, we investigate vector encoding and promoter classification. Additionally, we developed a more difficult negative set by employing promoter sequences rather than nonpromoter regions of the genome. The newly developed negative reconstruction approach improves classification and minimizes the amount of false positive predictions. Methods:: To overcome the problems associated with promoter prediction, we investigate alternate vector encoding and feature extraction methodologies. Following that, these strategies are coupled with several machine learning algorithms and a 1-D convolutional neural network model. Our results show that the pseudo-dinucleotide composition is preferable for feature encoding and that the machine- learning stacking approach is excellent for accurate promoter categorization. Furthermore, we provide a negative reconstruction method that uses promoter sequences rather than non-promoter regions, resulting in higher classification performance and fewer false positive predictions. Results:: Based on the results of 5-fold cross-validation, the proposed predictor, iProm-Yeast, has a good potential for detecting Saccharomyces cerevisiae promoters. The accuracy (Acc) was 86.27%, the sensitivity (Sn) was 82.29%, the specificity (Sp) was 89.47%, the Matthews correlation coefficient (MCC) was 0.72, and the area under the receiver operating characteristic curve (AUROC) was 0.98. We also performed a cross-species analysis to determine the generalizability of iProm-Yeast across other species. Conclusion:: iProm-Yeast is a robust method for accurately identifying Saccharomyces cerevisiae promoters. With advanced vector encoding techniques and a negative reconstruction approach, it achieves improved classification accuracy and reduces false positive predictions. In addition, it offers researchers a reliable and precise webserver to study gene regulation in diverse organisms.

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QPromoters: Sequence based prediction of promoter strength inSaccharomyces cerevisiae

Cited by 2 publications

References 32 publications

Insight to Gene Expression From Promoter Libraries With the Machine Learning Workflow Exp2Ipynb

Insight to Gene Expression From Promoter Libraries With the Machine Learning Workflow Exp2Ipynb

iProm-Yeast: Prediction Tool for Yeast Promoters Based on ML Stacking

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