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ESPRESSO: Quantifying transcript isoforms from long-read sequencing

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  • ESPRESSO: Quantifying transcript isoforms from long-read sequencing

    Eukaryotic genes commonly generate multiple transcript isoforms which are important for understanding many biological processes and human diseases. Identification of isoforms has traditionally been completed using short-read technologies that limit read length and hinder isoform discovery.

    The increased utilization of long-read sequencing has allowed researchers to sequence thousands of bases in a single read and has improved the quantification and discovery of spice junctions. However, deficiencies with current computational tools motivated researchers from the Children’s Hospital of Philadelphia (CHOP) to develop their own tool, ESPRESSO (Error Statistics PRomoted Evaluator of Splice Site Options).

    This new tool uses long-read sequencing data and processes the resulting alignments to improve splice junction accuracy and isoform quantification. ESPRESSO was designed to compare the long reads for each gene to its corresponding genomic DNA. Then the error patterns of the reads are analyzed to locate splice junctions and complete isoforms.

    The CHOP researchers tested ESPRESSO using data generated from native DNA and RNA sequenced on Oxford Nanopore Technologies devices, along with simulated sequencing data. Over 1 billion long reads were analyzed, covering 30 human tissue types and three human cell lines.

    Using long reads to cross-reference transcripts to the genomic DNA allowed them to identify undocumented isoforms and splice junctions. In addition, ESPRESSO was able to accurately discover RNA isoforms and quantify them better than several other contemporary tools designed for transcript isoform analysis

    This computational tool has demonstrated that it can be a useful resource for investigating RNA from eukaryotic transcriptomes. Researchers from CHOP believe using ESPRESSO with long-read RNA sequencing will aid in our understanding of RNA variation and its role in genetic diseases.

    More information about ESPRESSO can be found on its corresponding GitHub page or by reading the published manuscript.

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