Abstract
PROTEOFORMER is a pipeline that enables the automated processing of data derived from ribosome profiling (RIBO-seq, i.e. the sequencing of ribosome-protected mRNA fragments). As such, genome-wide ribosome occupancies lead to the delineation of data-specific translation product candidates and these can improve the mass spectrometry-based identification. Since its first publication, different upgrades, new features and extensions have been added to the PROTEOFORMER pipeline. Some of the most important upgrades include P-site offset calculation during mapping, comprehensive data pre-exploration, the introduction of two alternative proteoform calling strategies and extended pipeline output features. These novelties are illustrated by analyzing ribosome profiling data of human HCT116 and Jurkat data. The different proteoform calling strategies are used alongside one another and in the end combined together with reference sequences from UniProt. Matching mass spectrometry data are searched against this extended search space with MaxQuant. Overall, besides annotated proteoforms, this pipeline leads to the identification and validation of different categories of new proteoforms, including translation products of up- and downstream open reading frames, 5' and 3' extended and truncated proteoforms, single amino acid variants, splice variants and translation products of so-called noncoding regions. Further, proof-of-concept is reported for the improvement of spectrum matching by including Prosit, a deep neural network strategy that adds extra fragmentation spectrum intensity features to the analysis. In the light of ribosome profiling-driven proteogenomics, it is shown that this allows validating the spectrum matches of newly identified proteoforms with elevated stringency. These updates and novel conclusions provide new insights and lessons for the ribosome profiling-based proteogenomic research field. More practical information on the pipeline, raw code, the user manual (README) and explanations on the different modes of availability can be found at the GitHub repository of PROTEOFORMER: https://github.com/Biobix/proteoformer.
Highlights
PROTEOFORMER is a pipeline that enables the automated processing of data derived from ribosome profiling (RIBO-seq, i.e. the sequencing of ribosome-protected mRNA fragments)
Data Quality Assessment, Read Preprocessing, Alignment and Translated Transcript Calling—The updated PROTEOFORMER pipeline (Fig. 1) was applied on matching RIBO-seq and shotgun proteomics data originating from two cell lines, human HCT116 [11] and Jurkat T cells [31]
Visualizing quality and metagenomic features is in our opinion good practice to have a general understanding of the data content before continuing with the rest of the analysis
Summary
PROTEOFORMER is a pipeline that enables the automated processing of data derived from ribosome profiling (RIBO-seq, i.e. the sequencing of ribosome-protected mRNA fragments). Some of the most important upgrades include P-site offset calculation during mapping, comprehensive data preexploration, the introduction of two alternative proteoform calling strategies and extended pipeline output features. These novelties are illustrated by analyzing ribosome profiling data of human HCT116 and Jurkat data. Proteogenomics describes the field where mass spectrometry (MS) based proteomic research is combined with generation sequencing (NGS) based genomics, transcriptomics and translatomics [1]. After mapping the sequenced fragments to the reference genome, specific offsets allow to pinpoint the alignments onto the P-site (i.e. the exact base position where the ribosome was translating the mRNA into a peptide product). With a correct set of P-site offsets, the subcodon resolution can be correctly disclosed and important features of ribosome profiling, like triplet periodicity and translation patterns, can be unveiled
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