Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction

Meng Zhang, Cangzhi Jia, Fuyi Li, Chen Li, Yan Zhu, Tatsuya Akutsu, Geoff Webb, Quan Zou, Lachlan J.M. Coin, Jiangning Song

Research output: Contribution to journalReview ArticleResearchpeer-review

16 Citations (Scopus)

Abstract

Promoters are crucial regulatory DNA regions for gene transcriptional activation. Rapid advances in next-generation sequencing technologies have accelerated the accumulation of genome sequences, providing increased training data to inform computational approaches for both prokaryotic and eukaryotic promoter prediction. However, it remains a significant challenge to accurately identify species-specific promoter sequences using computational approaches. To advance computational support for promoter prediction, in this study, we curated 58 comprehensive, up-to-date, benchmark datasets for 7 different species (i.e. Escherichia coli, Bacillus subtilis, Homo sapiens, Mus musculus, Arabidopsis thaliana, Zea mays and Drosophila melanogaster) to assist the research community to assess the relative functionality of alternative approaches and support future research on both prokaryotic and eukaryotic promoters. We revisited 106 predictors published since 2000 for promoter identification (40 for prokaryotic promoter, 61 for eukaryotic promoter, and 5 for both). We systematically evaluated their training datasets, computational methodologies, calculated features, performance and software usability. On the basis of these benchmark datasets, we benchmarked 19 predictors with functioning webservers/local tools and assessed their prediction performance. We found that deep learning and traditional machine learning-based approaches generally outperformed scoring function-based approaches. Taken together, the curated benchmark dataset repository and the benchmarking analysis in this study serve to inform the design and implementation of computational approaches for promoter prediction and facilitate more rigorous comparison of new techniques in the future.

Original languageEnglish
Article numberbbab551
Number of pages25
JournalBriefings in Bioinformatics
Volume23
Issue number2
DOIs
Publication statusPublished - Mar 2022

Keywords

  • deep learning
  • Machine learning
  • performance evaluation
  • promoter identification

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