Analyzing a person’s gene expression requires mapping their RNA landscape to a standard reference to gain insight into the degree to which genes are “turned on” and perform functions in the body. But researchers can run into issues when the reference does not provide enough information to allow for accurate mapping, an issue known as reference bias.

In a new paper published in the journal Nature Methods, researchers at UC Santa Cruz introduce the first-ever method for analyzing RNA sequencing data genome-wide using a “pantranscriptome,” which combines a transcriptome and a pangenome—a reference that contains genetic material from a cohort of diverse individuals, rather than just a single linear strand.

A group of scientists led by UCSC Associate Professor of Biomolecular Engineering Benedict Paten have released a toolkit that allows researchers to map an individual’s RNA data to a much richer reference, addressing reference bias and leading to much more accurate mapping.

Many genetic diseases are caused by diverse mutations spread across an entire gene, and designing genome editing approaches for each patient’s mutation would be impractical and costly.

Investigators at Massachusetts General Hospital (MGH) have recently developed an optimized method that improves the accuracy of inserting large DNA segments into a genome.

This approach could be used to insert a whole normal or “wild-type” replacement gene, which could act as a blanket therapy for a disease irrespective of a patient’s particular mutation.