Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics.
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ABSTRACT:
SUBMITTER: Verbruggen S
PROVIDER: S-EPMC8214147 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Proteogenomics approaches often struggle with the distinction between right and false peptide-to-spectrum matches as the database size enlarges. However, features extracted from tandem mass spectrometry intensity predictors can enhance the peptide identification rate and can provide extra confidence for spectral matching in a proteogenomic context. To that end, features from the spectral intensity pattern predictors MS2PIP and Prosit were combined with the canonical scores from MaxQuant in the Percolator post-processing tool for protein databases constructed from RNA-seq and ribosome profiling analyses. The presented results provide evidence that this approach enhances the peptide identification power in a proteogenomic setting and in the meantime they lead to the validation of new proteoforms with elevated stringency. In this online repository, we submitted the conventional proteomic search results with MaxQuant against the custom nanopore RNA-seq-based search space. All other results can be found in the supplemental materials of the manuscript, in SRA (sequencing data) or under ProteomeXChange Project PXD011353 (as this is original data from a previuos paper).
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Project description:MotivationPolyadenylation is the addition of a poly(A) tail to an RNA molecule. Identifying DNA sequence motifs that signal the addition of poly(A) tails is essential to improved genome annotation and better understanding of the regulatory mechanisms and stability of mRNA. Existing poly(A) motif predictors demonstrate that information extracted from the surrounding nucleotide sequences of candidate poly(A) motifs can differentiate true motifs from the false ones to a great extent. A variety of sophisticated features has been explored, including sequential, structural, statistical, thermodynamic and evolutionary properties. However, most of these methods involve extensive manual feature engineering, which can be time-consuming and can require in-depth domain knowledge.ResultsWe propose a novel machine-learning method for poly(A) motif prediction by marrying generative learning (hidden Markov models) and discriminative learning (support vector machines). Generative learning provides a rich palette on which the uncertainty and diversity of sequence information can be handled, while discriminative learning allows the performance of the classification task to be directly optimized. Here, we used hidden Markov models for fitting the DNA sequence dynamics, and developed an efficient spectral algorithm for extracting latent variable information from these models. These spectral latent features were then fed into support vector machines to fine-tune the classification performance. We evaluated our proposed method on a comprehensive human poly(A) dataset that consists of 14 740 samples from 12 of the most abundant variants of human poly(A) motifs. Compared with one of the previous state-of-the-art methods in the literature (the random forest model with expert-crafted features), our method reduces the average error rate, false-negative rate and false-positive rate by 26, 15 and 35%, respectively. Meanwhile, our method makes ~30% fewer error predictions relative to the other string kernels. Furthermore, our method can be used to visualize the importance of oligomers and positions in predicting poly(A) motifs, from which we can observe a number of characteristics in the surrounding regions of true and false motifs that have not been reported before.Availabilityhttp://sfb.kaust.edu.sa/Pages/Software.aspx.Supplementary informationSupplementary data are available at Bioinformatics online.
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