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Abstract Genome editing allows scientists to specifically change the DNA sequence of an organism. This powerful technology now fuels basic biology discovery and tangible crop improvement efforts. There is a less well understood layer of information encoded in genomes, known collectively as ‘epigenetics’, that impacts gene expression, without changing the DNA sequence. Epigenetic processes allow organisms to rapidly respond to environmental fluctuation. Like genome editing, recent advances have demonstrated that it is possible to edit the epigenome of a plant and cause heritable phenotypic changes. In this review, we aim to specifically consider the unique advantages that targeted epigenome editing might provide over existing biotechnology tools. This review is aimed at a broad audience. We begin with a high-level overview of the tools currently available for crop improvement. Next, we present a more detailed overview of the key discoveries that have been made in recent years, primarily using the model system Arabidopsis, new efforts to extend targeted methylation to crop plants, the current status of the technology, and the challenges that remain to realize the full potential of targeted epigenome editing. We end with a forward-looking commentary on how epi-alleles might interface with breeding programs across a variety of crops. 

Abstract Motivation Detecting cancer gene expression and transcriptome changes with mRNA-sequencing (RNA-Seq) or array-based data are important for understanding the molecular mechanisms underlying carcinogenesis and cellular events during cancer progression. In previous studies, the differentially expressed genes were detected across patients in one cancer type. These studies ignored the role of mRNA expression changes in driving tumorigenic mechanisms that are either universal or specific in different tumor types. To address the problem, we introduce two network-based multi-task learning frameworks, NetML and NetSML, to discover common differentially expressed genes shared across different cancer types as well as differentially expressed genes specific to each cancer type. The proposed frameworks consider the common latent gene co-expression modules and gene-sample biclusters underlying the multiple cancer datasets to learn the knowledge crossing different tumor types. Results Large-scale experiments on simulations and real cancer high-throughput datasets validate that the proposed network-based multi-task learning frameworks perform better sample classification compared with the models without the knowledge sharing across different cancer types. The common and cancer specific molecular signatures detected by multi-task learning frameworks on TCGA ovarian cancer, breast cancer, and prostate cancer datasets are correlated with the known marker genes and enriched in cancer relevant KEGG pathways and Gene Ontology terms. Availability and Implementation Source code is available at: https://github.com/compbiolabucf/NetML Supplementary information Supplementary data are available at Bioinformatics