A Spectrum Graph-Based Protein Sequence Filtering Algorithm for Proteoform Identification by Top-Down Mass Spectrometry

The salivary microbiome shows a high prevalence of core bacterial members yet variability across human populations

Human saliva contains diverse bacterial communities, reflecting health status, dietary patterns and contributing to variability in the sensory perception of food. Many descriptions of the diversity of the salivary microbiome have focused on the changes induced by certain diseased states, but the commonalities and differences within healthy saliva have not been fully described. Here, we define and explore the core membership of the human salivary microbial community by collecting and re-analysing raw 16S rRNA amplicon sequencing data from 47 studies with 2206 saliva samples. We found 68 core bacterial taxa that were consistently detected. Differences induced by various host intrinsic and behaviour factors, including gender, age, geographic location, tobacco usage and alcohol consumption were evident. The core of the salivary microbiome was verified by collecting and analysing saliva in an independent study. These results suggest that the methods used can effectively define a core microbial community in human saliva. The core salivary microbiome demonstrated both stability and variability among populations. Geographic location was identified as the host factor that is most associated with the structure of salivary microbiota. The independent analysis confirmed the prevalence of the 68 core OTUs we defined from the global data and provides information about how bacterial taxa in saliva varies across human populations.

Proteoform identification based on top-down tandem mass spectra with peak error corrections

In this paper, we study the problem for finding complex proteoforms from protein databases based on top-down tandem mass spectrum data. The main difficulty to solve the problem is to handle the combinatorial explosion of various alterations on a protein. To overcome the combinatorial explosion of various alterations on a protein, the problem has been formulated as the alignment problem of a proteoform mass graph (PMG) and a spectrum mass graph (SMG). The other important issue is to handle mass errors of peaks in the input spectrum. In previous methods, an error tolerance value is used to handle the mass differences between the matched consecutive nodes/peaks in PMG and SMG. However, such a way to handle mass error can not guarantee that the mass difference between any pairs of nodes in the alignment is approximately the same for both PMG and SMG. It may lead to large error accumulation if positive (or negative) errors occur consecutively for a large number of consecutive matched node pairs. The problem is severe so that some existing software packages include a step to further refine the alignments. In this paper, we propose a new model to handle the mass errors of peaks based on the formulation of the PMG and SMG. Note that the masses of sub-paths on the PMG are theoretical and suppose to be accurate. Our method allows each peak in the input spectrum to have a predefined error range. In the alignment of PMG and SMG, we need to give a correction of the mass for each matched peak within the predefined error range. After the correction, we impose that the mass between any two (not necessarily consecutive) matched nodes in the PMG is identical to that of the corresponding two matched peaks in the SMG. Intuitively, this kind of alignment is more accurate. We design an algorithm to find a maximum number of matched node and peak pairs in the two (PMG and SMG) mass graphs under the new constraint. The obtained alignment can show matched node and peak pairs as well as the corrected positions of peaks. The algorithm works well for moderate size input instances and takes very long time as well as huge size memory for large input size instances. Therefore, we propose an algorithm to do diagonal alignment. The diagonal alignment algorithm can solve large input size instances in reasonable time. Experiments show that our new algorithms can report alignments with much larger number of matched node pairs. The software package and test data sets are available at https://github.com/Zeirdo/TopMGRefine.

A graph-based filtering method for top-down mass spectral identification

BACKGROUND

Database search has been the main approach for proteoform identification by top-down tandem mass spectrometry. However, when the target proteoform that produced the spectrum contains post-translational modifications (PTMs) and/or mutations, it is quite time consuming to align a query spectrum against all protein sequences without any PTMs and mutations in a large database. Consequently, it is essential to develop efficient and sensitive filtering algorithms for speeding up database search.

RESULTS

In this paper, we propose a spectrum graph matching (SGM) based protein sequence filtering method for top-down mass spectral identification. It uses the subspectra of a query spectrum to generate spectrum graphs and searches them against a protein database to report the best candidates. As the sequence tag and gaped tag approaches need the preprocessing step to extract and select tags, the SGM filtering method circumvents this preprocessing step, thus simplifying data processing. We evaluated the filtration efficiency of the SGM filtering method with various parameter settings on an Escherichia coli top-down mass spectrometry data set and compared the performances of the SGM filtering method and two tag-based filtering methods on a data set of MCF-7 cells.

CONCLUSIONS

Experimental results on the data sets show that the SGM filtering method achieves high sensitivity in protein sequence filtration. When coupled with a spectral alignment algorithm, the SGM filtering method significantly increases the number of identified proteoform spectrum-matches compared with the tag-based methods in top-down mass spectrometry data analysis.