The Integration of Data from Different Long-Read Sequencing Platforms Enhances Proteoform Characterization in Arabidopsis

The increasing availability of massive omics data requires improving the quality of reference databases and their annotations. The combination of full-length isoform sequencing (Iso-Seq) with short-read transcriptomics and proteomics has been successfully used for increasing proteoform characterization, which is a main ongoing goal in biology. However, the potential of including Oxford Nanopore Technologies Direct RNA Sequencing (ONT-DRS) data has not been explored. In this paper, we analyzed the impact of combining Iso-Seq- and ONT-DRS-derived data on the identification of proteoforms in Arabidopsis MS proteomics data. To this end, we selected a proteomics dataset corresponding to senescent leaves and we performed protein searches using three different protein databases: AtRTD2 and AtRTD3, built from the homonymous transcriptomes, regarded as the most complete and up-to-date available for the species; and a custom hybrid database combining AtRTD3 with publicly available ONT-DRS transcriptomics data generated from Arabidopsis leaves. Our results show that the inclusion and combination of long-read sequencing data from Iso-Seq and ONT-DRS into a proteogenomic workflow enhances proteoform characterization and discovery in bottom-up proteomics studies. This represents a great opportunity to further investigate biological systems at an unprecedented scale, although it brings challenges to current protein searching algorithms.

Automated summary

To improve the quality of reference databases and their annotations, researchers combined full-length isoform sequencing (Iso-Seq) with short-read transcriptomics and proteomics. By including Oxford Nanopore Technologies Direct RNA Sequencing (ONT-DRS) data, they enhanced the identification and discovery of proteoforms in Arabidopsis MS proteomics data. This approach offers unprecedented opportunities for investigating biological systems, but also presents challenges for existing protein searching algorithms.