Improving accuracy of molecular quantification in high throughput sequencing

 Credit: Nature Methods (2024). DOI: 10.1038/s41592-024-02168-y

A team at NDORMS has developed a new approach to significantly improve the accuracy of RNA sequencing. They have pinpointed the primary source of inaccurate quantification in both short and long-read RNA sequencing, and have introduced the concept of “majority vote” error correction leading to a substantial improvement in RNA molecular counting. Accurate sequencing of genetic material is crucial in modern biology, particularly for comprehending and addressing diseases linked to genetic anomalies. However, current methodologies encounter substantial constraints.

In a landmark study, an international consortium of researchers, led by Adam Cribbs, Associate Prof. in Computational Biology, and Jianfeng Sun, Postdoctoral Research Associate at the Botnar Institute, University of Oxford, have developed an innovative method to correct errors in PCR amplification a widely used technique used in high-throughput sequencing. By pinpointing PCR artifacts as the primary source of inaccurate quantification, the researchers, address a long-standing challenge in generating accurate absolute counts of RNA molecules, which is crucial for various applications in genomics research. The study is published in the journal Nature Methods.

The researchers focused on Unique Molecular Identifiers (UMIs), which are random oligonucleotide sequences used to remove biases introduced during PCR amplification. While UMIs have been widely adopted in sequencing methods, the study reveals that PCR errors can undermine the accuracy of molecular quantification, particularly across different sequencing platforms.

By Adam Cribbs, University of Oxford

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