Macdonald, L. E. | Search bioRxiv

bioRxiv2021

Rigorous estimation of post-translational proteasomal splicing in the immunopeptidome

Recently, de novo peptide sequencing has made it possible to gain new insights into the human immunopeptidome without relying on peptide databases, while identifying peptides of unknown origin. Many recent studies have attributed post-translational proteasomal splicing as the origin of those peptides. Here, we describe a peptide source assignment workflow to rigorously assign the source of de novo sequenced peptides and find that the estimated extent of post-translational splicing in the immunopeptidome is much lower than previously reported. Our approach demonstrates that many peptides that were thought to be post-translationally spliced are likely linear peptides, and many peptides that were thought to be trans-spliced could be cis-spliced. We believe our approach furthers the understanding of post-translationally spliced peptides and thus improves the characterization of immunopeptidome which plays a critical role in the immune response to antigens in cancer, autoimmune disease, and infections.

bioinformatics↗

bioRxiv2020

AdRoit: an accurate and robust method to infer complex transcriptome composition

Bulk RNA sequencing technology provides the opportunity to understand biology at the whole transcriptome level without the prohibitive cost of single cell profiling. Advances in spatial transcriptomics enable to dissect tissue organization and function by genome-wide gene expressions. However, the readout of both technologies is the overall gene expression across potentially many cell types without directly providing the information of cell type constitution. Although several in-silico approaches have been proposed to deconvolute RNA-Seq data composed of multiple cell types, many suffer a deterioration of performance in complex tissues. Here we present AdRoit, an accurate and robust method infer the cell composition from transcriptome data comprised of multiple cell types. AdRoit uses gene expression profile obtained from single cell RNA sequencing as a reference. It employs an adaptive learning approach to correct the sequencing technique difference between the single cell data and the bulk or spatial transcriptome data, enabling cross-platform readout comparability. Our systematic benchmarking and applications, which include deconvoluting complex mixtures that encompass 30 cell types, demonstrate its superior sensitivity and specificity compared to other existing methods as well as its utilities. In addition, AdRoit is computationally efficient and runs orders of magnitude faster than many existing methods.

bioinformatics↗