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A Comprehensive Framework for Analyzing Long-Read Transcriptomics Data
Long-read sequencing (LRS) technologies have become robust alternatives to short-read methods for a wide range of application. Our lab has been at the forefront of this progress, developing SQANTI, a widely adopted tool for quality assessment and quantification of long-read transcript models. However, with the evolution of LRS technologies, there is a growing demand for complementary algorithms tailored to transcriptomics. These include accurate identification and quantification of transcripts, correction of technological biases, optimization of experimental designs for large-scale studies, creation of benchmarking resources, data visualization tools, and functional annotation of full-length transcript variants. I will present the expanded suite of SQANTI tools, designed to comprehensively address these challenges. Our research highlights distinct quantification biases in lrRNA-seq compared to short-read RNA-seq, underscoring the need for specialized normalization approaches. I will also explore alternative methods for defining joint transcriptomes in multi-sample experiments and their implications for transcript detection. Finally, I will introduce IsoAnnot, now incorporated into the SQANTI suite, which differentiates productive from unproductive transcripts and provides functional labels to deepen our understanding of the biological roles of alternative splicing.
About the speaker
Ana Conesa is a Research Professor at CSIC and Courtesy Professor at the University of Florida. Member of Spanish Royal Academy of Engineering, her lab focuses on genome-wide functional gene expression and transcriptomics. She pioneers multi-omics integration and long-read transcriptomics, creating tools like Blast2GO, PaintOmics and SQANTI. Her work, cited over 40,000 times, bridges genomics data and knowledge through accessible bioinformatics methods and software.
The Causal Analysis of Biomedical Data Lecture Series is supported by the Luxembourg National Research Fund (FNR) RESCOM Program.
