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Improved RNA virus understanding is critical to studying animal and plant health, and environmental processes. However, the continuous and rapid RNA virus evolution makes their identification and characterization challenging. While recent sequence-based advances have led to extensive RNA virus discovery, there is growing variation in how RNA viruses are identified, analyzed, characterized, and reported. To this end, an RdRp Summit was organized and a hybrid meeting took place in Valencia, Spain in May 2023 to convene leading experts with emphasis on early career researchers (ECRs) across diverse scientific communities. Here we synthesize key insights and recommendations and offer these as a first effort to establish a consensus framework for advancing RNA virus discovery. First, we need interoperability through standardized methodologies, data-sharing protocols, metadata provision and interdisciplinary collaborations and offer specific examples as starting points. Second, as an emergent field, we recognize the need to incorporate cutting-edge technologies and knowledge early and often to improve omic-based viral detection and annotation as novel capabilities reveal new biology. Third, we underscore the significance of ECRs in fostering international partnerships to promote inclusivity and equity in virus discovery efforts. The proposed consensus framework serves as a roadmap for the scientific community to collectively contribute to the tremendous challenge of unveiling the RNA virosphere. 

Abstract Short interspersed nuclear elements (SINEs) are a widespread type of small transposable element (TE). With increasing evidence for their impact on gene function and genome evolution in plants, accurate genome-scale SINE annotation becomes a fundamental step for studying the regulatory roles of SINEs and their relationship with other components in the genomes. Despite the overall promising progress made in TE annotation, SINE annotation remains a major challenge. Unlike some other TEs, SINEs are short and heterogeneous, and they usually lack well-conserved sequence or structural features. Thus, current SINE annotation tools have either low sensitivity or high false discovery rates. Given the demand and challenges, we aimed to provide a more accurate and efficient SINE annotation tool for plant genomes. The pipeline starts with maximizing the pool of SINE candidates via profile hidden Markov model-based homology search and de novo SINE search using structural features. Then, it excludes the false positives by integrating all known features of SINEs and the features of other types of TEs that can often be misannotated as SINEs. As a result, the pipeline substantially improves the tradeoff between sensitivity and accuracy, with both values close to or over 90%. We tested our tool in Arabidopsis thaliana and rice (Oryza sativa), and the results show that our tool competes favorably against existing SINE annotation tools. The simplicity and effectiveness of this tool would potentially be useful for generating more accurate SINE annotations for other plant species. The pipeline is freely available at https://github.com/yangli557/AnnoSINE.