New research improves accuracy of molecular quantification in high throughput sequencing

A team from Oxford Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS) have developed a novel approach to significantly improve the accuracy of RNA sequencing. They indicate the main source of error in short and long readings. RNA sequencing. They introduced the concept of ‘majority vote’ error correction, which greatly improved RNA molecular counting.

Figure 1: A schematic showing homotrimer UMI majority vote error correction. We constructed UMIs with homotrimeric nucleotide blocks (containing combinations of AAA, CCC, GGG, TTT). Deletion, insertion or substitution errors are identified and corrected by a ‘majority vote’ system, selecting the most frequent nucleotide, by evaluating trimer nucleotide similarity.

Accurate sequencing of genetic material is critical in modern biology, especially for understanding and treating diseases associated with genetic abnormalities. However, the current approach faces considerable obstacles. In a ___ Historical studyAn international consortium of researchers, led by Adam Krebs, Associate Professor in Computational BiologyAnd Jian Feng Sunin Postdoctoral Research Associate Boettner InstituteOxford University has developed an innovative method to correct errors in PCR amplification – a widely used technique used in high-throughput sequencing. By identifying PCR artifacts as the main source of false quantification, research Nature’s waysaddresses a long-standing challenge in generating accurate absolute counts of RNA molecules, which is important for a variety of applications in genomics research.

The researchers focused on unique molecular identifiers (UMIs), which are random oligonucleotide sequences, to remove biases introduced during PCR amplification. While UMIs have been widely adopted in sequencing methods, studies have shown that PCR errors can undermine the accuracy of molecular quantification, especially across different sequencing platforms.

Jianfeng Explained: ‘PCR amplification, which is essential for most RNA sequencing techniques, can introduce errors, compromising the integrity of the data. We tackled this by synthesizing UMI barcodes using homotrimer nucleotide blocks, increasing error correction and enabling quantification of adjacent RNA molecules, significantly improving the accuracy of molecular counting.’

Homotrimers are nucleotide sequences that contain three identical bases, eg AAA, CCC, GGG. By evaluating homotrimers nucleotide similarity, errors are detected and corrected by a “majority vote” method (Figure 1).

The study demonstrates that homotrimer UMIs significantly outperform conventional monomer UMIs in reducing false-positive fold enrichment by analyzing different genes and transcripts (DEGs and DETs). This enhancement is crucial for accurate identification and quantification of DEG or DET, especially in large-scale sequencing methods. Furthermore, in single-cell sequencing, where extensive PCR amplification is often required, homotrimer UMIs have been shown to be effective in reducing the effects of PCR artifacts, thereby significantly increasing the reliability of sequencing data. There has been improvement.

‘By building UMIs from identical blocks of nucleosides, we aim to improve error correction in both short- and long-read sequences, demonstrating our commitment to expanding sequencing technology applications,’ says Associate Professor Adam Krebssenior author of the paper and group leader in Computational Biology.

This research has profound implications. By correcting PCR errors in UMIs, it greatly increases the accuracy of molecular quantification in various sequencing applications. It is an important tool for researchers in bulk RNA, single-cell RNA, and DNA sequencing, enabling accurate gene expression and molecular profile analyses. Improved UMI error correction reduces the incidence of false positives and offers numerous diagnostic applications, particularly in scenarios requiring longitudinal analysis of samples.

Source: University of Oxford