{"doi":"10.1101/2020.07.07.192286","title":"Ultra-accurate Microbial Amplicon Sequencing Directly from Complex Samples with Synthetic Long Reads","abstract":"Abstract Out of the many pathogenic bacterial species that are known, only a fraction are readily identifiable directly from a complex microbial community using standard next generation DNA sequencing technology. Long-read sequencing offers the potential to identify a wider range of species and to differentiate between strains within a species, but attaining sufficient accuracy in complex metagenomes remains a challenge. Here, we describe and analytically validate LoopSeq, a commercially-available synthetic long-read (SLR) sequencing technology that generates highly-accurate long reads from standard short reads. LoopSeq reads are sufficiently long and accurate to identify microbial genes and species directly from complex samples. LoopSeq applied to full-length 16S rRNA genes from known strains in a microbial community perfectly recovered the full diversity of full-length exact sequence variants in a known microbial community. Full-length LoopSeq reads had a per-base error rate of 0.005%, which exceeds the accuracy reported for other long-read sequencing technologies. 18S-ITS and genomic sequencing of fungal and bacterial isolates confirmed that LoopSeq sequencing maintains that accuracy for reads up to 6 kilobases in length. Analysis of rinsate from retail meat samples demonstrated that LoopSeq full-length 16S rRNA synthetic long-reads could accurately classify organisms down to the species level, and could differentiate between different strains within species identified by the CDC as potential foodborne pathogens. The order-of-magnitude improvement in both length and accuracy over standard Illumina amplicon sequencing achieved with LoopSeq enables accurate species-level and strain identification from complex and low-biomass microbiome samples. The ability to generate accurate and long microbiome sequencing reads using standard short read sequencers will accelerate the building of quality microbial sequence databases and removes a significant hurdle on the path to precision microbial genomics.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2020,"id":120315,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":11,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.947,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":558222,"name":"Dmitry Grinevich","orcid":null,"position":1,"is_corresponding":false},{"id":504766,"name":"Siddhartha Thakur","orcid":"0000-0003-3787-9939","position":2,"is_corresponding":false},{"id":558223,"name":"Michael A Balamotis","orcid":null,"position":3,"is_corresponding":false},{"id":558224,"name":"Tuval Ben Yehezkel","orcid":null,"position":4,"is_corresponding":false},{"id":52081,"name":"Benjamin J. Callahan","orcid":"0000-0002-8752-117X","position":0,"is_corresponding":true}],"reference_count":38,"raw_metadata":null,"created_at":"2026-07-18T23:14:33.640077Z","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":[]}