Esmaeilzadeh, M. | Search bioRxiv

bioRxiv2023

Prediction of SARS-CoV-2 spike protein mutations using Sequence-to-Sequence and Transformer models

In the study of viral epidemics, having information about the structural evolution of the virus can be very helpful in controlling the disease and making vaccines. Various deep learning and natural language processing techniques (NLP) can be used to analyze genetic structure of viruses, namely to predict their mutations. In this paper, by using Sequence-to-Sequence (Seq2Seq) model with Long Short-Term Memory (LSTM) cell and Transformer model with the attention mechanism, we investigate the spike protein mutations of SARS-CoV-2 virus. We make time-series datasets of the spike protein sequences of this virus and generate upcoming spike protein sequences. We also determine the mutations of the generated spike protein sequences, by comparing these sequences with the Wuhan spike protein sequence. We train the models to make predictions in December 2021, February 2022, and October 2022. Furthermore, we find that some of our generated spike protein sequences have been reported in December 2021 and February 2022, which belong to Delta and Omicron variants. The results obtained in the present study could be useful for prediction of future mutations of SARS-CoV-2 and other viruses.

bioinformatics↗

bioRxiv2017

Deep Phenotyping: Deep Learning For Temporal Phenotype/Genotype Classification

High resolution and high throughput, genotype to phenotype studies in plants are underway to accelerate breeding of climate ready crops. Complex developmental phenotypes are observed by imaging a variety of accessions in different environment conditions, however extracting the genetically heritable traits is challenging. In the recent years, deep learning techniques and in particular Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Long-Short Term Memories (LSTMs), have shown great success in visual data recognition, classification, and sequence learning tasks. In this paper, we proposed a CNN-LSTM framework for plant classification of various genotypes. Here, we exploit the power of deep CNNs for joint feature and classifier learning, within an automatic phenotyping scheme for genotype classification. Further, plant growth variation over time is also important in phenotyping their dynamic behavior. This was fed into the deep learning framework using LSTMs to model these temporal cues for different plant accessions. We generated a replicated dataset of four accessions of Arabidopsis and carried out automated phenotyping experiments. The results provide evidence of the benefits of our approach over using traditional hand-crafted image analysis features and other genotype classification frameworks. We also demonstrate that temporal information further improves the performance of the phenotype classification system.

plant biology↗