Characterization of promoters in archaeal genomes based on DNA structural parameters

Martinez, Gustavo Sganzerla; Sarkar, Sharmilee; Kumar, Aditya; Pérez‐Rueda, Ernesto; Silva, Scheila de Ávila e

doi:10.1002/mbo3.1230

Cited by 9 publications

(18 citation statements)

References 44 publications

(88 reference statements)

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“…Next, we have observed conserved binding sites of promoter recruitment transcription factor proteins in archaea with varied GC content ( H. volcanii = 66.13%, T. kodakarensis = 50.67%, and S. solfataricus = 34.48%), More GC would indicate less potential binding sites for such proteins, as reported in [ 29 ]. However, our rationale has been able to find the binding site despite the amount of GC in a particular archaeon, Therefore, the binding site of these three proteins are clear in the plots representing the promoters in all organisms, suggesting that DDS succeeded in well representing promoter elements in archaea.…”

Section: Resultssupporting

confidence: 70%

“…The database contains upstream regions for 211 archaeal organisms. Two archaeal genomes, Aciduliprofundum boonei (741 sequences of 400 nucleotides each) and Thermofilum pendens (1926 sequences of 400 nucleotides each) exhibited a promoter-like profile in a previous study [ 29 ], i.e. the codification into DDS of upstream regions was found to be statistically similar to experimentally validated promoters, indicating that these particular upstream regions might contain promoters.…”

Section: Methodsmentioning

confidence: 89%

“…The verification of inter-organism rationale of classification has evidenced that S. solfataricus and T. kodakarensis share similarities, evidenced by the acceptable classification scores between these two archaea. The high values of recall observed in H. volcanii vs. S. solfataricus and T. kodakarensis suggest that very few False Positives were identified, meaning that it was rare for the model to incorrectly classify H. volcanii promoters, this is due to the divergent amount of GC in this organism, reported in [ 29 ]. The bumpy results of precision in H. volcanii and S. solfataricus (and vice-versa) shows that the S. solfataricus model correctly identified non-promoters of the H. volcanii dataset, meaning the model correctly identifies promoters with conserved binding sites.…”

Section: Resultsmentioning

confidence: 99%

“…Firstly, the lower Precision value in the method suggests the model classified too many False Positives, this means non-promoters were classified as promoters in some instances. A reason for this to happen is the diversification found in archaea [ 2 ] and the dissimilarity in owning conserved binding sites [ 29 ]. Secondly, the most fine counts were achieved in the block form of control, matching the identification of Table 1 , in which the means of the blocks are the furthest from the promoters.…”

Section: Resultsmentioning

confidence: 99%

“…The stress of the statistical model, brought by an inter-archaea classification (Table 3 ), similarities have been found in S. solfataricus and T. kodakarensis , confirming what was proposed by Takemasa et al [ 31 ] in order to turn T. kodakarensis and S. solfataricus as regulatory chassis for hyperthermophilic archaea. These two particular organisms were reported to be similar in terms of their AT% throughout the genome [ 29 ], while H. volcanii has higher GC. We found the nucleotide composition directly affects the classification outcome, since it relies on conserved binding sites of transcription factor proteins.…”

Section: Resultsmentioning

confidence: 99%

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Machine learning and statistics shape a novel path in archaeal promoter annotation

et al. 2022

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Background Archaea are a vast and unexplored domain. Bioinformatic techniques might enlighten the path to a higher quality genome annotation in varied organisms. Promoter sequences of archaea have the action of a plethora of proteins upon it. The conservation found in a structural level of the binding site of proteins such as TBP, TFB, and TFE aids RNAP-DNA stabilization and makes the archaeal promoter prone to be explored by statistical and machine learning techniques. Results and discussions In this study, experimentally verified promoter sequences of the organisms Haloferax volcanii, Sulfolobus solfataricus, and Thermococcus kodakarensis were converted into DNA duplex stability attributes (i.e. numerical variables) and were classified through Artificial Neural Networks and an in-house statistical method of classification, being tested with three forms of controls. The recognition of these promoters enabled its use to validate unannotated promoter sequences in other organisms. As a result, the binding site of basal transcription factors was located through a DNA duplex stability codification. Additionally, the classification presented satisfactory results (above 90%) among varied levels of control. Concluding remarks The classification models were employed to perform genomic annotation into the archaea Aciduliprofundum boonei and Thermofilum pendens, from which potential promoters have been identified and uploaded into public repositories.

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Section: Resultssupporting

confidence: 70%

Section: Methodsmentioning

confidence: 89%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Machine learning and statistics shape a novel path in archaeal promoter annotation

et al. 2022

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TSSUNet-MB – ab initio identification of σ70 promoter transcription start sites in Escherichia coli using deep multitask learning

Doan

Chiu

et al. 2023

Computational Biology and Chemistry

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MLDSPP: Bacterial Promoter Prediction Tool Using DNA Structural Properties with Machine Learning and Explainable AI

Paul,

Olymon,

Martinez

et al. 2024

J. Chem. Inf. Model.

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Bacterial promoters play a crucial role in gene expression by serving as docking sites for the transcription initiation machinery. However, accurately identifying promoter regions in bacterial genomes remains a challenge due to their diverse architecture and variations. In this study, we propose MLDSPP (Machine Learning and Duplex Stability based Promoter prediction in Prokaryotes), a machine learning-based promoter prediction tool, to comprehensively screen bacterial promoter regions in 12 diverse genomes. We leveraged biologically relevant and informative DNA structural properties, such as DNA duplex stability and base stacking, and state-of-the-art machine learning (ML) strategies to gain insights into promoter characteristics. We evaluated several machine learning models, including Support Vector Machines, Random Forests, and XGBoost, and assessed their performance using accuracy, precision, recall, specificity, F1 score, and MCC metrics. Our findings reveal that XGBoost outperformed other models and current state-of-the-art promoter prediction tools, namely Sigma70pred and iPromoter2L, achieving F1-scores >95% in most systems. Significantly, the use of one-hot encoding for representing nucleotide sequences complements these structural features, enhancing our XGBoost model's predictive capabilities. To address the challenge of model interpretability, we incorporated explainable AI techniques using Shapley values. This enhancement allows for a better understanding and interpretation of the predictions of our model. In conclusion, our study presents MLDSPP as a novel, generic tool for predicting promoter regions in bacteria, utilizing original downstream sequences as nonpromoter controls. This tool has the potential to significantly advance the field of bacterial genomics and contribute to our understanding of gene regulation in diverse bacterial systems.

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Characterization of promoters in archaeal genomes based on DNA structural parameters

Cited by 9 publications

References 44 publications

Machine learning and statistics shape a novel path in archaeal promoter annotation

Machine learning and statistics shape a novel path in archaeal promoter annotation

TSSUNet-MB – ab initio identification of σ70 promoter transcription start sites in Escherichia coli using deep multitask learning

MLDSPP: Bacterial Promoter Prediction Tool Using DNA Structural Properties with Machine Learning and Explainable AI

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