DeePromoter: Robust Promoter Predictor Using Deep Learning

Oubounyt, Mhaned; Louadi, Zakaria; Tayara, Hilal; Chong, Kil To

doi:10.3389/fgene.2019.00286

Cited by 135 publications

(139 citation statements)

References 45 publications

(51 reference statements)

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“…Promoter prediction in prokaryotes has received a lot of attention over the past two decades, to enhance the understanding and construction of gene regulatory networks [ 14 ]. Several tools implementing diverse algorithms, ranging from simple motif searches to complex machine learning techniques such as neural networks and support vector machines, have been developed and made available to the scientific community [ 7 , 10 , 11 ]. A unique approach was proposed in a paper studying thermodynamic stability of DNA as a feature for promoter prediction, rather than DNA motifs [ 9 ] .…”

Section: Introductionmentioning

confidence: 99%

SAPPHIRE: a neural network based classifier for σ70 promoter prediction in Pseudomonas

Coppens¹,

Lavigne²

2020

BMC Bioinformatics

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Background In silico promoter prediction represents an important challenge in bioinformatics as it provides a first-line approach to identifying regulatory elements to support wet-lab experiments. Historically, available promoter prediction software have focused on sigma factor-associated promoters in the model organism E. coli. As a consequence, traditional promoter predictors yield suboptimal predictions when applied to other prokaryotic genera, such as Pseudomonas, a Gram-negative bacterium of crucial medical and biotechnological importance. Results We developed SAPPHIRE, a promoter predictor for σ70 promoters in Pseudomonas. This promoter prediction relies on an artificial neural network that evaluates sequences on their similarity to the − 35 and − 10 boxes of σ70 promoters found experimentally in P. aeruginosa and P. putida. SAPPHIRE currently outperforms established predictive software when classifying Pseudomonas σ70 promoters and was built to allow further expansion in the future. Conclusions SAPPHIRE is the first predictive tool for bacterial σ70 promoters in Pseudomonas. SAPPHIRE is free, publicly available and can be accessed online at www.biosapphire.com. Alternatively, users can download the tool as a Python 3 script for local application from this site.

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Section: Introductionmentioning

confidence: 99%

SAPPHIRE: a neural network based classifier for σ70 promoter prediction in Pseudomonas

Coppens¹,

Lavigne²

2020

BMC Bioinformatics

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“…It can also identify candidate genomic regions for further experimental search. 9 Toward the goal of accurate genome and TSS annotation, researchers have developed many algorithms to predict TSSs from genomic sequence [10][11][12][13][14] and to identify genes from RNA-seq data. [15][16][17] No existing methods, however, predict TSSs on histone modification and DNase data alone.…”

Section: Transcription Start Site Predictionmentioning

confidence: 99%

Semi-supervised segmentation and genome annotation

et al. 2020

Preprint

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Segmentation and genome annotation methods automatically discover joint signal patterns in whole genome datasets. Previously, researchers trained these algorithms in a fully unsupervised way, with no prior knowledge of the functions of particular regions. Adding information provided by expert-created annotations to supervise training could improve the annotations created by these methods. We implemented semi-supervised learning using virtual evidence in the annotation method Segway. Additionally, we defined a positionally tolerant precision and recall metric for scoring genome annotations based on the proximity of each annotation feature to the truth set. We demonstrate semi-supervised Segway's ability to learn patterns corresponding to provided transcription start sites on a specified supervision label, and subsequently recover other transcription start sites in unseen data on the same supervision label.

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“…A summary of the main applications is provided in Figure 1. LSTM to predict promoter sequences in genes (27). DL algorithms have also helped to identify splice junctions through CNN (28).…”

Section: Deep Learning Applications In Omics Data Analysismentioning

confidence: 99%

Deep Learning in Omics Data Analysis and Precision Medicine

Martorell-Marugán

Tabik

Benhammou

et al. 2019

Computational Biology

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The rise of omics techniques has resulted in an explosion of molecular data in modern biomedical research. Together with information from medical images and clinical data, the field of omics has driven the implementation of personalized medicine. Biomedical and omics datasets are complex and heterogeneous, and extracting meaningful knowledge from this vast amount of information is by far the most important challenge for bioinformatics and machine learning researchers. In this context, there is an increasing interest in the potential of deep learning (DL) methods to create predictive models and to identify complex patterns from these large datasets. This chapter provides an overview of the main applications of DL methods in biomedical research, with focus on omics data analysis and precision medicine applications. DL algorithms and the most popular architectures are introduced first. This is followed by a review of some of the main applications and problems approached by DL in omics data and medical image analysis. Finally, implementations for improving the diagnosis, treatment, and classification of complex diseases are discussed.

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DeePromoter: Robust Promoter Predictor Using Deep Learning

Cited by 135 publications

References 45 publications

SAPPHIRE: a neural network based classifier for σ70 promoter prediction in Pseudomonas

SAPPHIRE: a neural network based classifier for σ70 promoter prediction in Pseudomonas

Semi-supervised segmentation and genome annotation

Deep Learning in Omics Data Analysis and Precision Medicine

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