Genome annotation across species using deep convolutional neural networks

Khodabandelou, Ghazaleh; Routhier, Etienne; Mozziconacci, Julien

doi:10.7717/peerj-cs.278

Cited by 11 publications

(10 citation statements)

References 34 publications

(44 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For non-model organisms, it is more difficult to find accurate training data and very few CNN methods have been designed specifically to predict SS in non-model organisms. Since SS and other regulatory motifs may be conserved across similar species [ 20 ], some work has been done to try to transfer models trained on model organisms to related organisms, for example between different vertebrate genomes [ 66 ]. Others have built cross-species models for specific clades, such as animals or plants using Helixer [ 67 ], but unfortunately the source code for this program is not yet stable (according to the authors).…”

Section: Discussionmentioning

confidence: 99%

Spliceator: multi-species splice site prediction using convolutional neural networks

et al. 2021

View full text Add to dashboard Cite

Background Ab initio prediction of splice sites is an essential step in eukaryotic genome annotation. Recent predictors have exploited Deep Learning algorithms and reliable gene structures from model organisms. However, Deep Learning methods for non-model organisms are lacking. Results We developed Spliceator to predict splice sites in a wide range of species, including model and non-model organisms. Spliceator uses a convolutional neural network and is trained on carefully validated data from over 100 organisms. We show that Spliceator achieves consistently high accuracy (89–92%) compared to existing methods on independent benchmarks from human, fish, fly, worm, plant and protist organisms. Conclusions Spliceator is a new Deep Learning method trained on high-quality data, which can be used to predict splice sites in diverse organisms, ranging from human to protists, with consistently high accuracy.

show abstract

Section: Discussionmentioning

confidence: 99%

Spliceator: multi-species splice site prediction using convolutional neural networks

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Deep convolutional neural networks were used to annotate gene-start sites in different species by training the model using the sites from one species as the positive sample and random sequences from the same species as the negative sample. The model was able to identify gene-start sites in other species [ 97 ].…”

Section: Integrating Artificial Intelligence In Metabolic Engineeringmentioning

confidence: 99%

Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering

Helmy

Smith

Selvarajoo

2020

Metabolic Engineering Communications

View full text Add to dashboard Cite

Metabolic engineering aims to maximize the production of bio-economically important substances (compounds, enzymes, or other proteins) through the optimization of the genetics, cellular processes and growth conditions of microorganisms. This requires detailed understanding of underlying metabolic pathways involved in the production of the targeted substances, and how the cellular processes or growth conditions are regulated by the engineering. To achieve this goal, a large system of experimental techniques, compound libraries, computational methods and data resources, including the multi-omics data, are used. The recent advent of multi-omics systems biology approaches significantly impacted the field by opening new avenues to perform dynamic and large-scale analyses that deepen our knowledge on the manipulations. However, with the enormous transcriptomics, proteomics and metabolomics available, it is a daunting task to integrate the data for a more holistic understanding. Novel data mining and analytics approaches, including Artificial Intelligence (AI), can provide breakthroughs where traditional low-throughput experiment-alone methods cannot easily achieve. Here, we review the latest attempts of combining systems biology and AI in metabolic engineering research, and highlight how this alliance can help overcome the current challenges facing industrial biotechnology, especially for food-related substances and compounds using microorganisms.

show abstract

“…This method aims to discover the biomarkers (OTU features) and assess their functions. A CNN model has also shown the feasibility of DL in functional annotation of genome sequences (Khodabandelou et al, 2020). The authors have recognized the short sequences with certain known functions, such as functions of the promoters in different species.…”

Section: Functional Analysis By Deep Learning Methodsmentioning

confidence: 99%

Application of Deep Learning in Plant–Microbiota Association Analysis

Deng

Zhang

et al. 2021

Front. Genet.

View full text Add to dashboard Cite

Unraveling the association between microbiome and plant phenotype can illustrate the effect of microbiome on host and then guide the agriculture management. Adequate identification of species and appropriate choice of models are two challenges in microbiome data analysis. Computational models of microbiome data could help in association analysis between the microbiome and plant host. The deep learning methods have been widely used to learn the microbiome data due to their powerful strength of handling the complex, sparse, noisy, and high-dimensional data. Here, we review the analytic strategies in the microbiome data analysis and describe the applications of deep learning models for plant–microbiome correlation studies. We also introduce the application cases of different models in plant–microbiome correlation analysis and discuss how to adapt the models on the critical steps in data processing. From the aspect of data processing manner, model structure, and operating principle, most deep learning models are suitable for the plant microbiome data analysis. The ability of feature representation and pattern recognition is the advantage of deep learning methods in modeling and interpretation for association analysis. Based on published computational experiments, the convolutional neural network and graph neural networks could be recommended for plant microbiome analysis.

show abstract

Genome annotation across species using deep convolutional neural networks

Cited by 11 publications

References 34 publications

Spliceator: multi-species splice site prediction using convolutional neural networks

Spliceator: multi-species splice site prediction using convolutional neural networks

Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering

Application of Deep Learning in Plant–Microbiota Association Analysis

Contact Info

Product

Resources

About