Cindy Liang

The Long-read RNA-Seq Genome Annotation Assessment Project Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. Using different protocols and sequencing platforms, the consortium generated over 427 million long-read sequences from complementary DNA and direct RNA datasets, encompassing human, mouse and manatee species. Developers utilized these data to address challenges in transcript isoform detection, quantification and de novo transcript detection. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. Incorporating additional orthogonal data and replicate samples is advised when aiming to detect rare and novel transcripts or using reference-free approaches. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.

Abstract The Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. The consortium generated over 427 million long-read sequences from cDNA and direct RNA datasets, encompassing human, mouse, and manatee species, using different protocols and sequencing platforms. These data were utilized by developers to address challenges in transcript isoform detection and quantification, as well as de novo transcript isoform identification. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. When aiming to detect rare and novel transcripts or when using reference-free approaches, incorporating additional orthogonal data and replicate samples are advised. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.

Abstract Generative Artificial Intelligence (GenAI) is fundamentally transforming notions of creativity and creative production across disciplines, yet a comprehensive understanding of professional attitudes and integration patterns remains challenging. This scoping review examines how creative professionals perceive and integrate GenAI technologies across four domains: visual art and design, writing and literature, performing arts, and environmental and spatial design. Following PRISMA-ScR guidelines, we analysed 57 papers (2022–2025) from multiple databases, focusing mainly on empirically based studies of professional creative practice. We identify universal trends, including the shift from creation to curation and meta-creation, the emergence of new literacies (prompt engineering, AI evaluation), and the reconfiguration of expertise hierarchies. Career stage emerges as a critical factor: across domains, entry-level professionals demonstrate enthusiasm and view GenAI as a natural extension of digital tools, while senior practitioners express scepticism about expertise devaluation. All fields consistently position GenAI in early-stage conceptualisation rather than final production, developing hybrid methodologies that preserve human judgment in convergent creative phases. However, impacts manifest differently based on each field/ sub-field’s relationship to embodiment and materiality. Integration levels follow an inverse relationship with traditional notions of “pure” creativity—fields prioritising embodied practice and cultural authenticity (fine arts, literary fiction, classical music) show the most significant resistance. At the same time, commercially oriented domains embrace higher adoption for efficiency gains. Visual artists face “erasure by obscurity” from AI output volume; writers negotiate between nonlinear, associative language generation and AI’s tendency toward coherence and cliché; performers uniquely embrace computational unpredictability as a generative force; architects balance efficiency imperatives with concerns about cultural homogenisation effects of Western-centric AI systems. Our conceptual framework positions creative professionals’ attitudes along two dimensions: knowledge codifiability (tacit/embodied to explicit/codifiable) and output materiality (physical/permanent to digital/ephemeral). Key tensions include balancing productivity with meaningful engagement, volume with distinction, precision with serendipity, and individual with collective intelligence. Ultimately, our findings reveal complex patterns of ambivalence shaped by competing value systems. This review contributes a comprehensive mapping of GenAI applications across creative disciplines, potential disconnects between educational emphasis on critique and industry’s pragmatic adoption, and gaps for future research on longitudinal skill impacts, emerging AI-native practices, Global South perspectives, and forms of creative resistance.