Researchers at Baylor College of Medicine’s Medical Genetics Multiomics Laboratory have published findings in the American Journal of Human Genetics highlighting the benefits of ultra-deep RNA sequencing for diagnosing Mendelian disorders. While current clinical protocols typically use moderate sequencing depths—about 50 to 150 million reads—the study investigated the impact of using ultra-high depth RNA sequencing, reaching up to one billion reads.
The team found that this deeper approach significantly increased the detection of genes and transcripts with low expression, which are often missed with traditional methods. “Using the Ultima Genomics ultra-high depth RNA-seq platform, we substantially increased the detection of lowly expressed genes and transcripts,” said Dr. Pengfei Liu, corresponding author, MGML director, and associate professor at Baylor College of Medicine. “With ultra-deep sequencing, we can evaluate many more low-expression genes that would be missed when sequenced at traditional sequencing depths.”
Detecting these variants is particularly important because blood and skin samples—commonly used in clinical settings—may not strongly express genes related to developmental or neurological disorders. “Our study shows that if you can sequence blood samples to extremely high depths, you can capture those genes traditionally thought to be tissue specific,” Liu added.
The research also led to the development of an online resource that estimates how much sequencing depth is needed for a genetic diagnosis and predicts abnormal gene splicing from deeply sequenced healthy data. The next phase involves clinical validation and planning a diagnostic test based on these findings. “In the MGML, we are leaders in translating new genomic technologies into real-world clinical practice,” Liu said. “We continue to evaluate new technology that can help improve diagnostic rates for our patients.”
Contributors to this study include Sen Zhao, Jefferson C. Sinson, Shenglan Li, Jill A. Rosenfeld, Gladys Zapata, Kristina Macakova, Mezthly Pena, Becky Maywald, Kim C. Worley, Lindsay Burrage, Monika Weisz Hubshman, Shamika Ketkar, William Craigen, Lisa Emrick, Tyson Clark, Gila Yanai Lithwick, Zohar Shipony, Christine Eng and Brendan Lee from Baylor College of Medicine as well as collaborators from Ultima Genomics and Baylor Genetics. The project was conducted with support from the Undiagnosed Diseases Network and funded by grants from the National Institute of Neurological Disorders and Stroke (U01HG007709 and U01HG007942) along with the National Human Genome Research Institute (R35HG011311).
