{"data":{"event":{"id":"76b38c3b-aca0-40d4-9081-93fa811507fc","slug":"celltypeenrich-a-consensus-based-scrna-seq-cluster-annotation-tool-0f81cc6356","title":"\n\ncelltypeEnrich: a consensus-based scRNA-seq cluster annotation tool \n\n","short_summary":"Motivation Single-cell RNA sequencing (scRNA-seq) cluster annotation is a critical step in data analysis. Current methods are time-consuming, difficult to reproduce, or limited in tissue or species coverage. Results We developed celltypeEnrich, a cluster-level annotation tool that uses a hypergeometric test to identify enrichment of cell-type-specific genes from input gene lists. Enrichment results from up to 26 reference datasets are used to determine a consensus annotation. Benchmarking using scRNA-seq datasets from three tissues spanning two species showed 62-72% annotation accuracy for cel","full_description":"Motivation Single-cell RNA sequencing (scRNA-seq) cluster annotation is a critical step in data analysis. Current methods are time-consuming, difficult to reproduce, or limited in tissue or species coverage. Results We developed celltypeEnrich, a cluster-level annotation tool that uses a hypergeometric test to identify enrichment of cell-type-specific genes from input gene lists. Enrichment results from up to 26 reference datasets are used to determine a consensus annotation. Benchmarking using scRNA-seq datasets from three tissues spanning two species showed 62-72% annotation accuracy for celltypeEnrich, generally outperforming other tools, which had either lower accuracy, incomplete tissue coverage, or the need for parameter optimization. The performance of celltypeEnrich remained stable when input gene lists were down-sampled to 25% of their original size. Availability and Implementation celltypeEnrich is freely available at (https://celltypeenrich.gdcb.iastate.edu) as an R Shiny web application under the MIT license for non-profit academic use.\n\nUpdate (2026-09-21): \n\nPOAnoise: A Graph-based Denoising Pipeline for Amplicon Sequencing Data \n\n. High-throughput DNA metabarcoding enables large-scale biodiversity assessment by identifying taxa from environmental samples, but its accuracy critically depends on denoising methods that separate true biological variation from PCR and sequencing errors. A persistent challenge is robust reconstruction of sequence diversity across abundance distributions, where low-abundance variants are particularly difficult to recover. We introduce POAnoise, a graph-based denoising framework that uses Partial Order Alignment (POA) to model relationships among noisy sequencing reads. POAnoise incrementally constructs sequence graphs that represent substitutions and indels, and derives consensus sequences from graph-supported paths using a weighted consensus strategy. By combining graph-based alignment with abundance-aware clustering, the method provides a structured way to reconstruct sequence variants from noisy amplicon data across heterogeneous abundance regimes. We evaluated POAnoise on simulated ITS and 16S datasets and compared its performance with established denoising methods, DADA2 and UNO","ledger_type":"benefit","primary_domain_id":"8f1af1b9-7302-4b51-aae9-fdfac04d158a","event_status":"provisional","event_date":"2026-09-21T00:00:00.000Z","discovery_date":"2026-09-21T00:00:00.000Z","first_published_date":"2026-09-21T00:00:00.000Z","last_reviewed_date":"2026-09-21T00:00:00.000Z","geographic_scope":"International","affected_population":null,"base_impact_tier":1,"base_score":"1.00","attribution_multiplier":"0.5000","evidence_multiplier":"0.4500","realization_multiplier":"0.4000","durability_multiplier":"0.5000","current_event_score":"0.045000","confidence_level":"low","score_explanation":"Ingest update: independent source raised evidence; added a follow-up source. 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Attribution stays unspecified until a release is identified.","attribution_confidence":"medium","first_used_date":"2026-09-21T00:00:00.000Z","model_version_if_known":null,"review_status":"approved","created_at":"2026-09-21T15:01:00.741Z","model_slug":"unspecified-ai-system","model_name":"Unspecified AI system","identity_class":"unknown","is_internal":false,"family_name":"Unspecified","family_slug":"unknown-unspecified","organization_name":"Unknown","organization_slug":"unknown"}],"sources":[{"id":"54bc905d-2626-43d8-ac69-60580cea0eed","event_id":"76b38c3b-aca0-40d4-9081-93fa811507fc","url":"\nhttps://www.biorxiv.org/content/10.64898/2026.09.15.751735v1?rss=1\n","canonical_url":"\nhttps://www.biorxiv.org/content/10.64898/2026.09.15.751735v1?rss=1\n","source_type":"preprint","publisher":"bioRxiv","author":null,"publication_date":"2026-09-21T00:00:00.000Z","retrieved_at":"2026-09-21T15:01:00.753Z","title":"\n\ncelltypeEnrich: a consensus-based scRNA-seq cluster annotation tool \n\n","excerpt":"Motivation Single-cell RNA sequencing (scRNA-seq) cluster annotation is a critical step in data analysis. Current methods are time-consuming, difficult to reproduce, or limited in tissue or species coverage. Results We developed celltypeEnrich, a cluster-level annotation tool that uses a hypergeometric test to identify enrichment of cell-type-specific genes from input gene lists. Enrichment results from up to 26 reference datasets are used to determine a consensus annotation. Benchmarking using ","content_hash":null,"source_reliability_class":"medium","is_primary_source":true,"is_independent":true,"is_peer_reviewed":false,"archived_url":null,"created_at":"2026-09-21T15:01:00.753Z"},{"id":"a25cd09a-010a-44d6-93cb-2e33dbaec30b","event_id":"76b38c3b-aca0-40d4-9081-93fa811507fc","url":"\nhttps://www.biorxiv.org/content/10.64898/2026.09.14.751445v1?rss=1\n","canonical_url":"\nhttps://www.biorxiv.org/content/10.64898/2026.09.14.751445v1?rss=1\n","source_type":"preprint","publisher":"bioRxiv","author":null,"publication_date":"2026-09-21T00:00:00.000Z","retrieved_at":"2026-09-21T18:00:57.561Z","title":"\n\nPOAnoise: A Graph-based Denoising Pipeline for Amplicon Sequencing Data \n\n","excerpt":"High-throughput DNA metabarcoding enables large-scale biodiversity assessment by identifying taxa from environmental samples, but its accuracy critically depends on denoising methods that separate true biological variation from PCR and sequencing errors. A persistent challenge is robust reconstruction of sequence diversity across abundance distributions, where low-abundance variants are particularly difficult to recover. 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