A new Rutgers School of Public Health study employs a novel statistical method for discovering full-length sequences of messenger RNA molecules, or mRNA transcripts, with accuracy and high confidence.
A fundamental process in molecular biology, alternative splicing, produces multiple mRNA sequences from a single gene. Alternative splicing plays a critical role in many genetic diseases. Hence, accurate identification and quantification of full-length mRNA molecules is crucial for investigating various disease mechanisms. Despite continued efforts to develop effective computational tools for this task, it remains a challenge to accurately identify mRNA transcripts from short sequence reads due to the substantial loss of information in the next-generation of RNA-seq. experiments.
Annotation-assisted isoform discovery (AIDE), is the first bioinformatics tool that uses a statistical testing principle to examine the assembled RNA transcripts and directly control for false discoveries. Those that do not pass its test are discarded, while those that do are prioritized with high confidence.
Both computational and experimental analyses found that AIDE had the highest precision in the discovery of full-length RNA transcripts and lowest error rates in RNA abundance estimation.
In addition to reducing false discoveries, AIDE is shown to identify RNA transcripts with biological relevance in disease conditions, such as melanoma and breast cancer. By improving the accuracy of mRNA identification and quantification in disease conditions, AIDE will shed light on the gene regulatory mechanisms of genetic diseases, assisting biomedical researchers in designing targeted therapies.
The AIDE method is implemented as an R package, which is available at Github.
“We expect that AIDE will help researchers accurately discover novel mRNA transcripts in pathological samples where aberrant alternative splicing commonly occurs,” said Dr. Vivian Li, the study’s lead author and an Assistant Professor in the Department of Biostatistics and Epidemiology. “This is important to lower the experimental validation costs and enable biological discoveries at a higher confidence level.”
Dr. Li’s mentor, Dr. Jingyl Jessica Li, was a co-author of the study, as well as Dr. Hubing Shi, Dr. Shan Li, Dr. Xin Tong, and Dr. Ling Deng.