Motif analysis unveils the possible co-regulation of chloroplast genes and nuclear genes encoding chloroplast proteins

Structural and Comparative Analyses of Insects Suggest the Presence of an Ultra-Conserved Regulatory Element of the Genes Encoding Vacuolar-Type ATPase Subunits and Assembly Factors

Gene and genome comparison represent an invaluable tool to identify evolutionarily conserved sequences with possible functional significance. In this work, we have analyzed orthologous genes encoding subunits and assembly factors of the V-ATPase complex, an important enzymatic complex of the vacuolar and lysosomal compartments of the eukaryotic cell with storage and recycling functions, respectively, as well as the main pump in the plasma membrane that energizes the epithelial transport in insects. This study involves 70 insect species belonging to eight insect orders. We highlighted the conservation of a short sequence in the genes encoding subunits of the V-ATPase complex and their assembly factors analyzed with respect to their exon-intron organization of those genes. This study offers the possibility to study ultra-conserved regulatory elements under an evolutionary perspective, with the aim of expanding our knowledge on the regulation of complex gene networks at the basis of organellar biogenesis and cellular organization.

Interpretation of deep learning in genomics and epigenomics

Machine learning methods have been widely applied to big data analysis in genomics and epigenomics research. Although accuracy and efficiency are common goals in many modeling tasks, model interpretability is especially important to these studies towards understanding the underlying molecular and cellular mechanisms. Deep neural networks (DNNs) have recently gained popularity in various types of genomic and epigenomic studies due to their capabilities in utilizing large-scale high-throughput bioinformatics data and achieving high accuracy in predictions and classifications. However, DNNs are often challenged by their potential to explain the predictions due to their black-box nature. In this review, we present current development in the model interpretation of DNNs, focusing on their applications in genomics and epigenomics. We first describe state-of-the-art DNN interpretation methods in representative machine learning fields. We then summarize the DNN interpretation methods in recent studies on genomics and epigenomics, focusing on current data- and computing-intensive topics such as sequence motif identification, genetic variations, gene expression, chromatin interactions and non-coding RNAs. We also present the biological discoveries that resulted from these interpretation methods. We finally discuss the advantages and limitations of current interpretation approaches in the context of genomic and epigenomic studies. Contact:xiaoman@mail.ucf.edu, haihu@cs.ucf.edu.