Dataset Information

Improving confidence in lipidomic annotations by incorporating empirical ion mobility regression analysis and chemical class prediction.

ABSTRACT:

Motivation

Mass spectrometry-based untargeted lipidomics aims to globally characterize the lipids and lipid-like molecules in biological systems. Ion mobility increases coverage and confidence by offering an additional dimension of separation and a highly reproducible metric for feature annotation, the collision cross-section (CCS).

Results

We present a data processing workflow to increase confidence in molecular class annotations based on CCS values. This approach uses class-specific regression models built from a standardized CCS repository (the Unified CCS Compendium) in a parallel scheme that combines a new annotation filtering approach with a machine learning class prediction strategy. In a proof-of-concept study using murine brain lipid extracts, 883 lipids were assigned higher confidence identifications using the filtering approach, which reduced the tentative candidate lists by over 50% on average. An additional 192 unannotated compounds were assigned a predicted chemical class.

Availability and implementation

All relevant source code is available at https://github.com/McLeanResearchGroup/CCS-filter.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Rose BS

PROVIDER: S-EPMC9306740 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Publications

Improving confidence in lipidomic annotations by incorporating empirical ion mobility regression analysis and chemical class prediction.

Rose Bailey S BS May Jody C JC Picache Jaqueline A JA Codreanu Simona G SG Sherrod Stacy D SD McLean John A JA

Bioinformatics (Oxford, England) 20220501 10

<h4>Motivation</h4>Mass spectrometry-based untargeted lipidomics aims to globally characterize the lipids and lipid-like molecules in biological systems. Ion mobility increases coverage and confidence by offering an additional dimension of separation and a highly reproducible metric for feature annotation, the collision cross-section (CCS).<h4>Results</h4>We present a data processing workflow to increase confidence in molecular class annotations based on CCS values. This approach uses class-spec ...[more]

PMID: 35561172

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Project description:BackgroundThe success of collapsing methods which investigate the combined effect of rare variants on complex traits has so far been limited. The manner in which variants within a gene are selected prior to analysis has a crucial impact on this success, which has resulted in analyses conventionally filtering variants according to their consequence. This study investigates whether an alternative approach to filtering, using annotations from recently developed bioinformatics tools, can aid these types of analyses in comparison to conventional approaches.Methods & resultsWe conducted a candidate gene analysis using the UK10K sequence and lipids data, filtering according to functional annotations using the resource CADD (Combined Annotation-Dependent Depletion) and contrasting results with 'nonsynonymous' and 'loss of function' consequence analyses. Using CADD allowed the inclusion of potentially deleterious intronic variants, which was not possible when filtering by consequence. Overall, different filtering approaches provided similar evidence of association, although filtering according to CADD identified evidence of association between ANGPTL4 and High Density Lipoproteins (P = 0.02, N = 3,210) which was not observed in the other analyses. We also undertook genome-wide analyses to determine how filtering in this manner compared to conventional approaches for gene regions. Results suggested that filtering by annotations according to CADD, as well as other tools known as FATHMM-MKL and DANN, identified association signals not detected when filtering by variant consequence and vice versa.ConclusionIncorporating variant annotations from non-coding bioinformatics tools should prove to be a valuable asset for rare variant analyses in the future. Filtering by variant consequence is only possible in coding regions of the genome, whereas utilising non-coding bioinformatics annotations provides an opportunity to discover unknown causal variants in non-coding regions as well. This should allow studies to uncover a greater number of causal variants for complex traits and help elucidate their functional role in disease.

Dataset Information

Improving confidence in lipidomic annotations by incorporating empirical ion mobility regression analysis and chemical class prediction.

Motivation

Results

Availability and implementation

Supplementary information

Publications

Improving confidence in lipidomic annotations by incorporating empirical ion mobility regression analysis and chemical class prediction.

Similar Datasets

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