Evolution of extreme resistance to ionizing radiation via genetic adaptation of DNA repair
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Engineering topics
Publications and source records attributed to Martin, Jeffrey A..
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Agaves are succulent monocotyledonous plants native to hot and arid environments of North America. Because of their adaptations to their environment, including crassulacean acid metabolism (CAM, a water-efficient form of photosynthesis) and existing technologies for ethanol production, agaves have gained attention both as potential lignocellulosic bioenergy feedstocks and models for exploring plant responses to abiotic stress. However, the lack of comprehensive Agave sequence datasets limits the scope of investigations into the molecular-genetic basis of Agave traits. Here, we present comprehensive, high quality de novo transcriptome assemblies of two Agave species, A. tequilana and A. deserti, from short-read RNA-seq data. Our analyses support completeness and accuracy of the de novo transcriptome assemblies, with each species having approximately 35,000 protein-coding genes. Comparison of agave proteomes to those of additional plant species identifies biological functions of gene families displaying sequence divergence in agave species. Additionally, we use RNA-seq data to gain insights into biological functions along the A. deserti juvenile leaf proximal-distal axis. Our work presents a foundation for further investigation of agave biology and their improvement for bioenergy development.
Transcriptomics studies often rely on partial reference transcriptomes that fail to capture the full catalog of transcripts and their variations. Recent advances in sequencing technologies and assembly algorithms have facilitated the reconstruction of the entire transcriptome by deep RNA sequencing (RNA-seq), even without a reference genome. However, transcriptome assembly from billions of RNA-seq reads, which are often very short, poses a significant informatics challenge. This Review summarizes the recent developments in transcriptome assembly approaches - reference-based, de novo and combined strategies-along with some perspectives on transcriptome assembly in the near future.