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Motivation Proteogenomics has proven its utility by integrating genomics and proteomics. Typical approaches use data from next-generation sequencing to infer proteins expressed. A sample-specific protein sequence database is often adopted to identify novel peptides from matched mass spectrometry-based proteomics; nevertheless, there is no software that can practically identify all possible forms of mutated peptides suggested by various genomic information sources. Results We propose MutCombinator, which enables us to practically identify mutated peptides from tandem mass spectra allowing combinatorial mutations during the database search. It uses an upgraded version of a variant graph, keeping track of frame information. The variant graph is indexed by nine nucleotides for fast access. Using MutCombinator, we could identify more mutated peptides than previous methods, because combinations of point mutations are considered and also because it can be practically applied together with a large mutation database such as COSMIC. Furthermore, MutCombinator supports in-frame search for coding regions and three-frame search for non-coding regions. Availability and implementation https://prix.hanyang.ac.kr/download/mutcombinator.jsp. Supplementary information Supplementary data are available at Bioinformatics online.

Recent sequencing technologies have highlighted translation of untranslated regions (UTRs) in genomes, although it remains unknown whether the translated products persist in a cell. Here, we propose a proteogenomic approach to UTR identification at the proteome level, which has been challenging due to the lack of corresponding sequences required for peptide spectrum matching. We address the challenge with constructing translated UTR (tUTR) database, consisting of all hypothetical sequences that can be translated from UTR by assuming non-AUG initiation at near-cognate start codons and stop codon readthrough. In the analysis of the H1299 cell line mass spectrometry (MS/MS) dataset, the tUTR DB-based proteogenomic approach enabled the detection of 52 5'-UTR and 9 3'-UTR peptides from 45 and 9 genes, respectively. The identified UTR peptides were validated via high spectral similarity with their synthetic peptides. The 5'-UTR peptides pointed out alternative initiation sites with non-AUG start codons, which exactly conformed to Kozak contexts of annotated initiation sites. It is also worth noting that our approach can detect translated amino acid sequences as well as provide evidence for UTR translation, while ribosome profiling provides only the translation evidence. For previously reported stop codon readthrough in MDH1 gene, we could confirm the amino acid inserted during the readthrough. Data are available via ProteomeXchange with identifier PXD016207.

Summary In many proteogenomic applications, mapping peptide sequences onto genome sequences can be very useful, because it allows us to understand origins of the gene products. Existing software tools either take the genomic position of a peptide start site as an input or assume that the peptide sequence exactly matches the coding sequence of a given gene model. In case of novel peptides resulting from genomic variations, especially structural variations such as alternative splicing, these existing tools cannot be directly applied unless users supply information about the variant, either its genomic position or its transcription model. Mapping potentially novel peptides to genome sequences, while allowing certain genomic variations, requires introducing novel gene models when aligning peptide sequences to gene structures. We have developed a new tool called ACTG (Amino aCids To Genome), which maps peptides to genome, assuming all possible single exon skipping, junction variation allowing three edit distances from the original splice sites, exon extension and frame shift. In addition, it can also consider SNVs (single nucleotide variations) during mapping phase if a user provides the VCF (variant call format) file as an input. Availability and Implementation Available at http://prix.hanyang.ac.kr/ACTG/search.jsp. Supplementary information Supplementary data are available at Bioinformatics online.