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CUHK Develops New Tech to Accurately Identify low-abundance RNA Transcripts


A research team led by Professor Chan Ting-fung, Associate Professor from the School of Life Sciences at The Chinese University of Hong Kong (CUHK), has developed a new computational method called LAFITE (Low-abundance Aware Full-length Isoform clusTEr).

The technology has been successful in identifying thousands of low-abundance full-length RNA transcripts in lung adenocarcinoma cell lines that were previously unknown using existing technologies. This is a breakthrough in the field of lung cancer research and has the potential to lead to new treatments and a better understanding of the disease.

About low-abundance RNA transcripts

In an organism, different tissues have the same genome but their transcriptomes, the composition of expressed RNA transcripts, vary significantly. Previous research found that the majority of expressed transcripts are present at low levels, referred to as “low abundance”, but they play crucial roles in regulating various biological processes such as metabolism and cancer progression.

Restrictions of current RNA sequencing technologies

RNA-sequencing (RNA-seq) is widely used in biology and clinical studies but has limitations in identifying low-expression or full-length transcripts. RNA-seq fragments and amplifies RNA molecules to improve detection of high-copy, short-length transcripts, but low-copy, low-expression full-length transcripts are challenging to identify.

Higher eukaryotes, including humans, can produce multiple transcript isoforms from the same gene through RNA splicing. However, RNA-seq cannot clearly identify individual transcript isoforms. As a result, current transcriptome research mainly focuses on gene levels and not transcript levels, leading to the overlook of many low-expression transcripts.

The Oxford Nanopore Technologies third-generation sequencing technique can capture native RNA transcripts and has improved ability to identify low-abundance, full-length transcript isoforms. However, the Nanopore DRS data used in this approach has significant noise and intrinsic errors, reducing its accuracy.


To address these limitations, Professor Chan Ting-fung and his team developed LAFITE, a new computational method specifically designed to process Nanopore DRS data and identify full-length isoforms. LAFITE surpasses all existing methods with its higher sensitivity in detecting low-abundance RNA transcripts.

Lung adenocarcinoma is the leading cause of cancer death in Hong Kong and has a lower five-year survival rate compared to other major cancers globally, according to the National Cancer Institute. While the causes of lung adenocarcinoma are complex, many studies have shown that changes in transcriptomes play a significant role in its progression.

The research team used LAFITE to analyze Nanopore DRS data from four lung adenocarcinoma cell lines. They successfully identified a new low-abundance RNA transcript isoform from the cancer gene AKT1 and showed its functional role in lung cancer cell lines, which was linked to patient survival and promoted tumour cell migration in lung adenocarcinoma.

Applicability to other cancers

With LAFITE, the research team discovered thousands of previously missed low-abundance transcripts, creating a complete transcriptome of lung adenocarcinoma. This will be a valuable resource for researchers to understand the mechanism behind the formation, migration, and progression of lung cancer. The team believes that LAFITE can be used for other types of cancer, such as colorectal cancer, and in studies on cancer cell drug resistance for drug development.

According to Professor Chan Ting-fung, a complete characterization of transcript isoforms of individual genes may provide new insight into their biological functions. LAFITE allows researchers to reassess gene function by identifying all expressed transcripts in a comprehensive manner, emphasizing the importance of transcript-level analysis in transcriptomic studies.


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