Biology subjects

Lovino, M.

Publications and source records attributed to Lovino, M..

3 recordsLinked to original sources

bioRxiv2022

DNAPerceiver and ProteinPerceiver: deep learning models for gene and protein expression prediction from DNA and amino-acid sequences

Background and ObjectiveThe functions of an organism and its biological processes result from the expression of genes and proteins. Therefore quantifying and predicting mRNA and protein levels is a crucial aspect of scientific research. Concerning the prediction of mRNA levels, the available approaches use the sequence straddling the Transcription Start Site (TSS) as input to neural networks. The State-of-the-art models (e.g., Xpresso and Basenjii) predict mRNA levels exploiting Convolutional (CNN) or Long Short Term Memory (LSTM) Networks. However, CNN prediction depends on convolutional kernel size, and LSTM suffers from capturing long-range dependencies in the sequence. Concerning the prediction of protein levels, as far as we know, there is no model for predicting protein levels by exploiting the gene or protein sequences. MethodsHere, we exploit a new model type (called Perceiver) for mRNA and protein level prediction, exploiting a Transformer-based architecture with an attention module to attend to long-range interactions in the sequences. In addition, the Perceiver model overcomes the quadratic complexity of the standard Transformer architectures. This works contributions are 1. DNAPerceiver model to predict mRNA levels from the sequence straddling the TSS; 2. ProteinPerceiver model to predict protein levels from the protein sequence; 3. Protein&DNAPerceiver model to predict protein levels from TSS-straddling and protein sequences. ResultsThe models are evaluated on cell lines, mice, glioblastoma, and lung cancer tissues. The results show the effectiveness of the Perceiver-type models in predicting mRNA and protein levels. ConclusionsThis paper presents a Perceiver architecture for mRNA and protein level prediction. In the future, inserting regulatory and epigenetic information into the model could improve mRNA and protein level predictions. The source code is freely available at https://github.com/MatteoStefanini/DNAPerceiver Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/508821v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1920a69org.highwire.dtl.DTLVardef@e97761org.highwire.dtl.DTLVardef@19c83cborg.highwire.dtl.DTLVardef@90c3d3_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIPredicting mRNA and protein levels from DNA and protein sequences is crucial in clinical applications. C_LIO_LIA transformer-based architecture with asymmetric attention (Perceiver) is exploited for mRNA and protein level prediction. C_LIO_LIThe Perceiver architecture attends to longer range interactions compared to Transformer, CNN, and LSTM. C_LIO_LIThe proposed model achieves state-of-the-art performance for mRNA level prediction. C_LIO_LITo the best of our knowledge, the protein level prediction task is addressed. C_LIO_LIThe proposed model is tested on glioblastoma and lung cancer tissues. C_LI

bioinformatics↗

bioRxiv2022

Integrated microRNA and proteome analysis of cancer datasets with MoPC

MicroRNAs (miRNAs) are small molecules that play an essential role in regulating gene expression by post-transcriptional gene silencing. Their study is crucial in revealing the fundamental processes underlying pathologies and, in particular, cancer. To date, most studies on miRNA regulation consider the effect of specific miRNAs on specific target mRNAs, providing wet-lab validation. However, few tools have been developed to explain the miRNA-mediated regulation at the protein level. In this paper, the MoPc computational tool is presented, that relies on the partial correlation between mRNAs and proteins conditioned on the miRNA expression to predict miRNA-target interactions in multi-omic datasets. MoPc returns the list of significant miRNA-target interactions and plot the significant correlations on the heatmap in which the miRNAs and targets are ordered by the chromosomal location. The software was applied on three TCGA/CPTAC datasets (breast, glioblastoma, and lung cancer), returning enriched results in three independent targets databases. Author summaryAccording to the central dogma of molecular biology, DNA is transcribed into RNA and subsequently translated into proteins. However, many molecules affect the amount of protein produced, including microRNAs (miRNAs). They can inhibit the translation or intervene by implementing the decay of target mRNAs. In literature, most works focus on describing the effect of miRNAs on mRNA targets, while only a few tools integrate protein expression profiles. MoPc predicts miRNA-targets interaction by considering the expression of mRNA, proteins, and miRNAs simultaneously. The method is based on the partial correlation measure between mRNAs and proteins conditioned by the expression of the miRNAs. The results on TCGA/CPTAC datasets prove the relevance of the MoPc method both from a computational and a biological point of view.

bioinformatics↗

bioRxiv2021

Identifying the oncogenic potential of gene fusions exploiting miRNAs

BackgroundIt is estimated that oncogenic gene fusions cause about 20% of human cancer morbidity. Identifying potentially oncogenic gene fusions may improve affected patients diagnosis and treatment. Previous approaches to this issue included exploiting specific gene-related information, such as gene function and regulation. Here we propose a model that profits from the previous findings and includes the microRNAs in the oncogenic assessment. We present a classifier called ChimerDriver for the classification of gene fusions as oncogenic or not oncogenic. ChimerDriver is based on a specifically designed neural network and trained on genetic and post-transcriptional information to obtain a reliable classification. Results and discussionThe designed neural network integrates information related to transcription factors, gene ontologies, microRNAs and other detailed information related to the functions of the genes involved in the fusion and the gene fusion structure. As a result, the performances on the test set reached 0.83 f1-score and 96% recall. The comparison with state-of-the-art tools returned comparable or higher results. Moreover, ChimerDriver performed well in a real-world case where 21 out of 24 validated gene fusion samples were detected by the gene fusion detection tool Starfusion. ConclusionsChimerDriver integrated transcriptional and post-transcriptional information in an ad-hoc designed neural network to effectively discriminate oncogenic gene fusions from passenger ones.

bioinformatics↗