MultiAlign: a multiple LC-MS analysis tool for targeted omics analysis

Abstract

Background: MultiAlign is a free software tool that aligns multiple liquid chromatography-mass spectrometry datasets to one another by clustering mass and chromatographic elution features across datasets. Applicable to both label-free proteomics and metabolomics comparative analyses, the software can be operated in several modes. For example, clustered features can be matched to a reference database to identify analytes, used to generate abundance profiles, linked to tandem mass spectra based on parent precursor masses, and culled for targeted liquid chromatography-tandem mass spectrometric analysis. MultiAlign is also capable of tandem mass spectral clustering to describe proteome structure and find similarity in subsequent sample runs.

Results: MultiAlign was applied to two large proteomics datasets obtained from liquid chromatography-mass spectrometry analyses of environmental samples. Peptides in the datasets for a microbial community that had a known metagenome were identified by matching mass and elution time features to those in an established reference peptide database. Results compared favorably with those obtained using existing tools such as VIPER, but with the added benefit of being able to trace clusters of peptides across conditions to existing tandem mass spectra. MultiAlign was further applied to detect clusters across experimental samples derived from a reactor biomass community for which no metagenome was available. Several clusters were culled for further analysis to explore changes in the community structure. Lastly, MultiAlign was applied to liquid chromatography-mass spectrometry-based datasets obtained from a previously published study of wild type and mitochondrial fatty acid oxidation enzyme knockdown mutants of human hepatocarcinoma to demonstrate its utility for analyzing metabolomics datasets.

Conclusion: MultiAlign is an efficient software package for finding similar analytes across multiple liquid chromatography-mass spectrometry feature maps, as demonstrated here for both proteomics and metabolomics experiments. The software is particularly useful for proteomic studies where little or no genomic context is known, such as with environmental proteomics.

Figure 1

Architecture of the MultiAlign software.

Figure 2

The data workflow for MultiAlign is similar to that of its predecessor VIPER with the addition of the clustering step and the ability to perform multiple dataset analysis. DeconTools is shown as the pre-processing step that reduces raw mass spectra into MS features of resolved monoisotopic masses, charge, and elution time.

Figure 3

Standalone parameter file editor application screenshot.

Figure 4

The Statistics Plots page shows thumbnails of the visualization capabilities in the GUI version of MultiAlign. This figure shows all plots generated when an AMT analysis is run. Each plot is interactive on this view.

Figure 5

The Dataset Plots page shows all of the thumbnails generated for each dataset to display the alignment plots and feature scatter plots.

Figure 6

A scatter plot of all the clusters found in the analysis. The bottom portion of the screen shows details for the selected given cluster.

Figure 7

Alignment heat maps for MultiAlign and VIPER of a dataset to an AMT tag database.

Figure 8

Bioreactor sample heat-maps aligned to the baseline sample. Columns represent the day the bioreactor was sampled, rows represent each technical replicate.

Figure 9

The delta scans between MS/MS clusters are shown above. Left, two datasets from day 1 and day 20 from the anaerobic microbial community extracted from cow rumen fluid were clustered using the MS/MS spectral clustering algorithm. Each point represents a spectral cluster. Right, spectral clustering was performed using two datasets from the same sample day. The spectral clustering method shows a significant difference in community structure between sample days one and five (i.e., days 1 and 20).

Figure 10

Alignment heat maps showing similarity between lipidomics datasets from four Huh7 cell experiment groups. From left to right, the first two groups represent strong and weak knockdowns of dodecenoyl coenzyme A delta isomerase expression. The third corresponds to Huh7 wild type cells, and the last is a short hairpin RNA (shRNA) non-targeting control.

Figure 11

The histograms above shows the distribution of cluster scores for ambiguity and tolerance using the metabolomics data. The ambiguity score is the distance between any two clusters. Low scores signify high ambiguity and potential feature overlap between clusters, where high scores would indicate low ambiguity.