Dataset Information

PhenoMeter: A Metabolome Database Search Tool Using Statistical Similarity Matching of Metabolic Phenotypes for High-Confidence Detection of Functional Links.

ABSTRACT: This article describes PhenoMeter (PM), a new type of metabolomics database search that accepts metabolite response patterns as queries and searches the MetaPhen database of reference patterns for responses that are statistically significantly similar or inverse for the purposes of detecting functional links. To identify a similarity measure that would detect functional links as reliably as possible, we compared the performance of four statistics in correctly top-matching metabolic phenotypes of Arabidopsis thaliana metabolism mutants affected in different steps of the photorespiration metabolic pathway to reference phenotypes of mutants affected in the same enzymes by independent mutations. The best performing statistic, the PM score, was a function of both Pearson correlation and Fisher's Exact Test of directional overlap. This statistic outperformed Pearson correlation, biweight midcorrelation and Fisher's Exact Test used alone. To demonstrate general applicability, we show that the PM reliably retrieved the most closely functionally linked response in the database when queried with responses to a wide variety of environmental and genetic perturbations. Attempts to match metabolic phenotypes between independent studies were met with varying success and possible reasons for this are discussed. Overall, our results suggest that integration of pattern-based search tools into metabolomics databases will aid functional annotation of newly recorded metabolic phenotypes analogously to the way sequence similarity search algorithms have aided the functional annotation of genes and proteins. PM is freely available at MetabolomeExpress (https://www.metabolome-express.org/phenometer.php).

SUBMITTER: Carroll AJ

PROVIDER: S-EPMC4518198 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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PhenoMeter: A Metabolome Database Search Tool Using Statistical Similarity Matching of Metabolic Phenotypes for High-Confidence Detection of Functional Links.

Carroll Adam J AJ Zhang Peng P Whitehead Lynne L Kaines Sarah S Tcherkez Guillaume G Badger Murray R MR

Frontiers in bioengineering and biotechnology 20150729

This article describes PhenoMeter (PM), a new type of metabolomics database search that accepts metabolite response patterns as queries and searches the MetaPhen database of reference patterns for responses that are statistically significantly similar or inverse for the purposes of detecting functional links. To identify a similarity measure that would detect functional links as reliably as possible, we compared the performance of four statistics in correctly top-matching metabolic phenotypes of ...[more]

PMID: 26284240

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Project description:Spectral similarity calculation is widely used in protein identification tools and mass spectra clustering algorithms while comparing theoretical or experimental spectra. The performance of the spectral similarity calculation plays an important role in these tools and algorithms especially in the analysis of large-scale datasets. Recently, deep learning methods have been proposed to improve the performance of clustering algorithms and protein identification by training the algorithms with existing data and the use of multiple spectra and identified peptide features. While the efficiency of these algorithms is still under study in comparison with traditional approaches, their application in proteomics data analysis is becoming more common. Here, we propose the use of deep learning to improve spectral similarity comparison. We assessed the performance of deep learning for spectral similarity, with GLEAMS and a newly trained embedder model (DLEAMSE), which uses high-quality spectra from PRIDE Cluster. Also, we developed a new bioinformatics tool (mslookup - https://github.com/bigbio/DLEAMSE/) that allows users to quickly search for spectra in previously identified mass spectra publish in public repositories and spectral libraries. Finally, we released a human database to enable bioinformaticians and biologists to search for identified spectra in their machines. SIGNIFICANCE STATEMENT: Spectral similarity calculation plays an important role in proteomics data analysis. With deep learning's ability to learn the implicit and effective features from large-scale training datasets, deep learning-based MS/MS spectra embedding models has emerged as a solution to improve mass spectral clustering similarity calculation algorithms. We compare multiple similarity scoring and deep learning methods in terms of accuracy (compute the similarity for a pair of the mass spectrum) and computing-time performance. The benchmark results showed no major differences in accuracy between DLEAMSE and normalized dot product for spectrum similarity calculations. The DLEAMSE GPU implementation is faster than NDP in preprocessing on the GPU server and the similarity calculation of DLEAMSE (Euclidean distance on 32-D vectors) takes about 1/3 of dot product calculations. The deep learning model (DLEAMSE) encoding and embedding steps needed to run once for each spectrum and the embedded 32-D points can be persisted in the repository for future comparison, which is faster for future comparisons and large-scale data. Based on these, we proposed a new tool mslookup that enables the researcher to find spectra previously identified in public data. The tool can be also used to generate in-house databases of previously identified spectra to share with other laboratories and consortiums.

Project description:BackgroundThe need to compare protein profiles frequently arises in various protein research areas: comparison of protein families, domain searches, resolution of orthology and paralogy. The existing fast algorithms can only compare a protein sequence with a protein sequence and a profile with a sequence. Algorithms to compare profiles use dynamic programming and complex scoring functions.ResultsWe developed a new algorithm called PHOG-BLAST for fast similarity search of profiles. This algorithm uses profile discretization to convert a profile to a finite alphabet and utilizes hashing for fast search. To determine the optimal alphabet, we analyzed columns in reliable multiple alignments and obtained column clusters in the 20-dimensional profile space by applying a special clustering procedure. We show that the clustering procedure works best if its parameters are chosen so that 20 profile clusters are obtained which can be interpreted as ancestral amino acid residues. With these clusters, only less than 2% of columns in multiple alignments are out of clusters. We tested the performance of PHOG-BLAST vs. PSI-BLAST on three well-known databases of multiple alignments: COG, PFAM and BALIBASE. On the COG database both algorithms showed the same performance, on PFAM and BALIBASE PHOG-BLAST was much superior to PSI-BLAST. PHOG-BLAST required 10-20 times less computer memory and computation time than PSI-BLAST.ConclusionSince PHOG-BLAST can compare multiple alignments of protein families, it can be used in different areas of comparative proteomics and protein evolution. For example, PHOG-BLAST helped to build the PHOG database of phylogenetic orthologous groups. An essential step in building this database was comparing protein complements of different species and orthologous groups of different taxons on a personal computer in reasonable time. When it is applied to detect weak similarity between protein families, PHOG-BLAST is less precise than rigorous profile-profile comparison method, though it runs much faster and can be used as a hit pre-selecting tool.

Dataset Information

PhenoMeter: A Metabolome Database Search Tool Using Statistical Similarity Matching of Metabolic Phenotypes for High-Confidence Detection of Functional Links.

Publications

PhenoMeter: A Metabolome Database Search Tool Using Statistical Similarity Matching of Metabolic Phenotypes for High-Confidence Detection of Functional Links.

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