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Transcription Attenuation in Prokaryotes

Transcription Attenuation in Prokaryotes

Transcriptional attenuation occurs when RNA transcription is prematurely terminated due to the formation of a terminator mRNA hairpin structure. Bacteria use these hairpins to regulate the transcription process and control the synthesis of several amino acids including histidine, lysine, threonine, and phenylalanine. Transcription attenuation takes place in the non-coding regions of mRNA.
There are several different mechanisms used to attenuate transcription. In ribosome mediated...

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BacTermFinder: a comprehensive and general bacterial terminator finder using a CNN ensemble.

Seyed Mohammad Amin Taheri Ghahfarokhi¹, Lourdes Peña-Castillo^1,2

¹Department of Computer Science, Memorial University of Newfoundland, St. John's, Newfoundland A1B 3X5, Canada.

NAR Genomics and Bioinformatics

|March 10, 2025

Summary

BacTermFinder, a new tool using convolutional neural networks (CNNs), accurately predicts bacterial transcription terminators. It identifies both intrinsic and factor-dependent types, outperforming existing methods and generalizing to archaea.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Transcription terminators are crucial DNA regions that signal the end of gene transcription.
Existing computational tools for predicting bacterial terminators often lack specificity, being limited to certain bacterial species or terminator types (intrinsic or factor-dependent).

Purpose of the Study:

To develop a novel, accurate, and broadly applicable computational tool for predicting bacterial transcription terminators.
To address the limitations of existing methods by creating a tool that can identify both intrinsic and factor-dependent terminators across diverse bacterial species and generalize to archaea.

Main Methods:

Development of BacTermFinder, an ensemble of convolutional neural networks (CNNs).
Training the model on approximately 41,000 bacterial terminators (intrinsic and factor-dependent) from 22 species with diverse GC content (28%-71%), sourced from RNA-seq studies.
Inputting four distinct representations of terminator sequences into the CNNs.

Main Results:

BacTermFinder demonstrated superior performance compared to four other bacterial terminator prediction tools, achieving higher average recall without an increase in false positives.
The tool successfully identified both intrinsic and factor-dependent terminator types.
BacTermFinder exhibited generalization capabilities, accurately predicting archaeal terminators.
Saliency maps were utilized to visualize CNN insights into species-specific terminator motifs.

Conclusions:

BacTermFinder represents a significant advancement in computational prediction of bacterial transcription terminators, offering improved accuracy and broader applicability.
The tool's ability to identify diverse terminator types and generalize to archaea enhances its utility in genomic analysis.
The visualization of CNN saliency maps provides valuable insights into the sequence features governing termination across different species.