{"doi":"10.1093/bioinformatics/btac197","title":"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.","journal":"Bioinformatics","year":2022,"id":261849,"datarank":0.5935254508879121,"base_score":2.70805020110221,"endowment":2.70805020110221,"self_citation_contribution":0.40620753016533157,"citation_network_contribution":0.1873179207225805,"self_endowment_contribution":0.40620753016533157,"citer_contribution":0.1873179207225805,"corpus_percentile":66.77496712307574,"corpus_rank":4296,"citation_count":14,"citer_count":7,"citers_with_citation_signal":5,"citers_with_endowment":5,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.6067,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":442056,"name":"Jody C. May","orcid":"0000-0003-4871-5024","position":1,"is_corresponding":false},{"id":843986,"name":"Jaqueline A. Picache","orcid":"0000-0002-1948-9243","position":2,"is_corresponding":false},{"id":373951,"name":"Simona G. Codreanu","orcid":"0000-0003-4435-812X","position":3,"is_corresponding":false},{"id":232777,"name":"Stacy D. Sherrod","orcid":"0000-0002-2346-230X","position":4,"is_corresponding":false},{"id":232772,"name":"John A. McLean","orcid":"0000-0001-8918-6419","position":5,"is_corresponding":false},{"id":773484,"name":"Bailey S. Rose","orcid":"0000-0002-5900-7288","position":0,"is_corresponding":true}],"reference_count":50,"raw_metadata":null,"created_at":"2026-07-19T00:26:16.663144Z","pmid":"35561172","pmcid":"PMC9306740","fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}