{"doi":"10.1128/aem.06516-11","title":"DECIPHER, a Search-Based Approach to Chimera Identification for 16S rRNA Sequences","abstract":"<jats:title>ABSTRACT</jats:title>\n          <jats:p>\n            DECIPHER is a new method for finding 16S rRNA chimeric sequences by the use of a search-based approach. The method is based upon detecting short fragments that are uncommon in the phylogenetic group where a query sequence is classified but frequently found in another phylogenetic group. The algorithm was calibrated for full sequences (fs_DECIPHER) and short sequences (ss_DECIPHER) and benchmarked against WigeoN (Pintail), ChimeraSlayer, and Uchime using artificially generated chimeras. Overall, ss_DECIPHER and Uchime provided the highest chimera detection for sequences 100 to 600 nucleotides long (79% and 81%, respectively), but Uchime's performance deteriorated for longer sequences, while ss_DECIPHER maintained a high detection rate (89%). Both methods had low false-positive rates (1.3% and 1.6%). The more conservative fs_DECIPHER, benchmarked only for sequences longer than 600 nucleotides, had an overall detection rate lower than that of ss_DECIPHER (75%) but higher than those of the other programs. In addition, fs_DECIPHER had the lowest false-positive rate among all the benchmarked programs (&lt;0.20%). DECIPHER was outperformed only by ChimeraSlayer and Uchime when chimeras were formed from closely related parents (less than 10% divergence). Given the differences in the programs, it was possible to detect over 89% of all chimeras with just the combination of ss_DECIPHER and Uchime. Using fs_DECIPHER, we detected between 1% and 2% additional chimeras in the RDP, SILVA, and Greengenes databases from which chimeras had already been removed with Pintail or Bellerophon. DECIPHER was implemented in the R programming language and is directly accessible through a webpage or by downloading the program as an R package (\n            <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"http://DECIPHER.cee.wisc.edu\">http://DECIPHER.cee.wisc.edu</jats:ext-link>\n            ).\n          </jats:p>","journal":"Applied and Environmental Microbiology","year":2012,"id":595095,"datarank":0.9887510598012988,"base_score":6.591673732008658,"endowment":6.591673732008658,"self_citation_contribution":0.9887510598012988,"citation_network_contribution":0.0,"self_endowment_contribution":0.9887510598012988,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":728,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1492123,"name":"L. Safak Yilmaz","orcid":null,"position":1,"is_corresponding":false},{"id":467762,"name":"Daniel R. Noguera","orcid":"0000-0003-0372-3063","position":2,"is_corresponding":false},{"id":312093,"name":"Erik S. Wright","orcid":"0000-0002-1457-4019","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"DECIPHER, a Search-Based Approach to Chimera Identification for 16S rRNA Sequences","abstract":"<jats:title>ABSTRACT</jats:title>\n          <jats:p>\n            DECIPHER is a new method for finding 16S rRNA chimeric sequences by the use of a search-based approach. The method is based upon detecting short fragments that are uncommon in the phylogenetic group where a query sequence is classified but frequently found in another phylogenetic group. The algorithm was calibrated for full sequences (fs_DECIPHER) and short sequences (ss_DECIPHER) and benchmarked against WigeoN (Pintail), ChimeraSlayer, and Uchime using artificially generated chimeras. Overall, ss_DECIPHER and Uchime provided the highest chimera detection for sequences 100 to 600 nucleotides long (79% and 81%, respectively), but Uchime's performance deteriorated for longer sequences, while ss_DECIPHER maintained a high detection rate (89%). Both methods had low false-positive rates (1.3% and 1.6%). The more conservative fs_DECIPHER, benchmarked only for sequences longer than 600 nucleotides, had an overall detection rate lower than that of ss_DECIPHER (75%) but higher than those of the other programs. In addition, fs_DECIPHER had the lowest false-positive rate among all the benchmarked programs (&lt;0.20%). DECIPHER was outperformed only by ChimeraSlayer and Uchime when chimeras were formed from closely related parents (less than 10% divergence). Given the differences in the programs, it was possible to detect over 89% of all chimeras with just the combination of ss_DECIPHER and Uchime. Using fs_DECIPHER, we detected between 1% and 2% additional chimeras in the RDP, SILVA, and Greengenes databases from which chimeras had already been removed with Pintail or Bellerophon. DECIPHER was implemented in the R programming language and is directly accessible through a webpage or by downloading the program as an R package (\n            <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"http://DECIPHER.cee.wisc.edu\">http://DECIPHER.cee.wisc.edu</jats:ext-link>\n            ).\n          </jats:p>","is_dataset_classified":null,"base_score":6.591673732008658,"endowment":6.591673732008658,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"22101057","pmcid":"PMC3264099","openalex_id":"https://openalex.org/W2113606293","authors":[],"funders":[],"total_grants":0,"fwci":16.787,"citation_percentile":0.99559845,"influential_citations":0,"citation_trend":[{"year":2012,"count":9},{"year":2013,"count":41},{"year":2014,"count":78},{"year":2015,"count":105},{"year":2016,"count":70},{"year":2017,"count":57},{"year":2018,"count":84},{"year":2019,"count":56},{"year":2020,"count":57},{"year":2021,"count":35},{"year":2022,"count":38},{"year":2023,"count":38},{"year":2024,"count":25},{"year":2025,"count":28},{"year":2026,"count":7}],"oa_status":"bronze","license":"https://journals.asm.org/non-commercial-tdm-license","oa_locations":[{"url":"https://aem.asm.org/content/aem/78/3/717.full.pdf","host_type":"journal"},{"url":"https://aem.asm.org/content/aem/78/3/717.full.pdf","host_type":"publisher"},{"url":"https://journals.asm.org/doi/pdf/10.1128/AEM.06516-11","host_type":"publisher"},{"url":"https://doi.org/10.1128/aem.06516-11","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/22101057","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/3264099","host_type":"repository"}],"fields_of_study":["Genomics and Phylogenetic Studies","Microbial Community Ecology and Physiology","Environmental DNA in Biodiversity Studies","Chimera","Computational Biology","Diagnostic Errors","Genes, rRNA","RNA, Ribosomal, 16S","Recombination, Genetic","Sensitivity and Specificity"],"mesh_terms":["Chimera","Diagnostic Errors","Recombination, Genetic","RNA, Ribosomal, 16S","Sensitivity and Specificity","Computational Biology","Genes, rRNA"],"keywords":["DECIPHER","Chimera (genetics)","Computational biology","Phylogenetic tree","Biology","Phylogenetics","Genetics","Gene"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-27T16:35:26.832769Z","pmid":null,"pmcid":null,"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":[]}