Peptide mass spectrometry relies crucially on algorithms that match peptides to spectra. We describe a method to evaluate the accuracy of these algorithms based on the masses of parent proteins before trypsin endoprotease digestion. Measurement of conformance to parent proteins provides a score for comparison of the performances of different algorithms as well as alternative parameter settings for a given algorithm. Tracking of conformance scores for spectrum matches to proteins with progressively lower expression levels revealed that conformance scores are not uniform within data sets but are significantly lower for less abundant proteins. Similarly peptides with lower algorithm peptide-spectrum match scores have lower conformance. Although peptide mass spectrometry data is typically filtered through decoy analysis to ensure a low false discovery rate, this analysis confirms that the filtered data should not be considered as having a uniform confidence. The analysis suggests that use of different algorithms and multiple standardized parameter settings of these algorithms can increase significantly the numbers of peptides identified. This data set can be used as a resource for future algorithm assessment.

We identified tryptic peptides in yeast cell lysates that map to translation initiation sites downstream of the annotated start sites using the peptide-spectrum matching algorithms OMSSA and Mascot. To increase the accuracy of peptide-spectrum matching, both algorithms were run using several standardized parameter sets, and Mascot was run utilizing a, b, and y ions from collision-induced dissociation. A large fraction (22%) of the detected N-terminal peptides mapped to translation initiation downstream of the annotated initiation sites. Expression of several truncated proteins from downstream initiation in the same reading frame as the full-length protein (frame 1) was verified by western analysis. To facilitate analysis of the larger proteome of Drosophila, we created a streamlined sequence library from which all duplicated trypsin fragments had been removed. OMSSA assessment using this "stripped" library revealed 171 peptides that map to downstream translation initiation sites, 76% of which are in the same reading frame as the full-length annotated proteins, although some are in different reading frames creating new protein sequences not in the annotated proteome. Sequences surrounding implicated downstream AUG start codons are associated with nucleotide preferences with a pronounced three-base periodicity N1^G2^A3.

Background Mounting evidence suggests several diseases and biological processes target transcription termination to misregulate gene expression. Disruption of transcription termination leads to readthrough transcription past the 3′ end of genes, which can result in novel transcripts, changes in epigenetic states and altered 3D genome structure. Results We developed Automatic Readthrough Transcription Detection (ARTDeco), a tool to detect and analyze multiple features of readthrough transcription from RNA-seq and other next-generation sequencing (NGS) assays that profile transcriptional activity. ARTDeco robustly quantifies the global severity of readthrough phenotypes, and reliably identifies individual genes that fail to terminate (readthrough genes), are aberrantly transcribed due to upstream termination failure (read-in genes), and novel transcripts created as a result of readthrough (downstream of gene or DoG transcripts). We used ARTDeco to characterize readthrough transcription observed during influenza A virus (IAV) infection, validating its specificity and sensitivity by comparing its performance in samples infected with a mutant virus that fails to block transcription termination. We verify ARTDeco’s ability to detect readthrough as well as identify read-in genes from different experimental assays across multiple experimental systems with known defects in transcriptional termination, and show how these results can be leveraged to improve the interpretation of gene expression and downstream analysis. Applying ARTDeco to a gene expression data set from IAV-infected monocytes from different donors, we find strong evidence that read-in gene-associated expression quantitative trait loci (eQTLs) likely regulate genes upstream of read-in genes. This indicates that taking readthrough transcription into account is important for the interpretation of eQTLs in systems where transcription termination is blocked. Conclusions ARTDeco aids researchers investigating readthrough transcription in a variety of systems and contexts.

Chinese hamster ovary (CHO) cells are widely used for producing biopharmaceuticals, and engineering gene expression in CHO is key to improving drug quality and affordability. However, engineering gene expression or activating silent genes requires accurate annotation of the underlying regulatory elements and transcription start sites (TSSs). Unfortunately, most TSSs in the published Chinese hamster genome sequence were computationally predicted and are frequently inaccurate. Here, we use nascent transcription start site sequencing methods to revise TSS annotations for 15 308 Chinese hamster genes and 3034 non-coding RNAs based on experimental data from CHO-K1 cells and 10 hamster tissues. We further capture tens of thousands of putative transcribed enhancer regions with this method. Our revised TSSs improves upon the RefSeq annotation by revealing core sequence features of gene regulation such as the TATA box and the Initiator and, as exemplified by targeting the glycosyltransferase gene Mgat3, facilitate activating silent genes by CRISPRa. Together, we envision our revised annotation and data will provide a rich resource for the CHO community, improve genome engineering efforts and aid comparative and evolutionary studies.

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Recent studies have indicated that transcription and translation are more pervasive on a genome-wide level than previously thought (Xu et al., 2009, Nature 457: 1033; Ingolia et al., 2009 Science 324:218). Specifically, studies using ribosome profiling have shown evidence of translation at previously un-annotated open reading frames (ORFs). These findings challenge current approaches to genome annotation. As a result, new methods are being developed in order to more precisely visualize the proteome. One methodology is peptide mass spectrometry wherein we perform tandem MS/MS on whole cell lysate and use a peptide search algorithm to match theoretical peptides provided by the user to observed mass spectra. To test the accuracy of these algorithms, we developed a gel slice method for parent-protein profiling in order to assess the accuracy of these algorithms (Lin et al., 2014, J. Prot. Res. 13: 1823). In Chapter 1, we used this methodology to assess the performance of the Mascot search algorithm. In Chapter 2, we applied the findings of Chapter 1 in order to assess a previously examined set of novel peptides resulting from the translation of open reading frames (ORFs) downstream of the annotated ORF (dnORFs) (Fournier et al., 2012, J. Prot. Res 11: 5712). In Chapter 3, we applied the methodologies described in Chapters 1 and 2 to detect peptides resulting from the translation of long non-coding RNAs (lncRNAs). In summary, the use of peptide mass spectrometry with peptide search algorithms can provide a high-confidence VI assessment of the proteome. This enabled us to detect previously un-annotated proteins and begin to characterize them.