{"data":{"event":{"id":"76386ef1-bbbc-43fd-ac38-39f360fef41e","slug":"flow-orchestrated-regulatory-genomics-engine-forge-a-configurable-nextfl-3361bcfa88","title":"\n\nFlow Orchestrated Regulatory Genomics Engine (FORGE): A Configurable Nextflow Pipeline for End-to-End snMultiome Analysis \n\n","short_summary":"MOTIVATION Single-nucleus resolution multiome (snMultiome) assays concurrently profile gene expression and chromatin accessibility in the same nucleus. Yet regulatory inference from such analyses are difficult to scale, audit, and reproduce; moreover, as a field, snMultiomics and its toolset remains far from standardized. To address these challenges we developed FORGE, a configureable Nextflow workflow that carries paired data from raw counts and fragments through regulatory network inference with a comprehensive differential testing suite. Execution is containerized, tracks provenance, robust","full_description":"MOTIVATION Single-nucleus resolution multiome (snMultiome) assays concurrently profile gene expression and chromatin accessibility in the same nucleus. Yet regulatory inference from such analyses are difficult to scale, audit, and reproduce; moreover, as a field, snMultiomics and its toolset remains far from standardized. To address these challenges we developed FORGE, a configureable Nextflow workflow that carries paired data from raw counts and fragments through regulatory network inference with a comprehensive differential testing suite. Execution is containerized, tracks provenance, robust to interruption, optimized for cluster-based compute environments, and allows for nuanced customization of specific processes. SUMMARY Single-nucleus multiome assays jointly profile gene expression and chromatin accessibility, yet their analysis typically requires bespoke chaining of modality-specific tools, creating barriers to reproducibility, scalability, and regulatory interpretation. We present FORGE (Flow Orchestrated Regulatory Genomics Engine), a configurable workflow that automates standalone snRNA-seq and snATAC-seq analysis, integrates the pair through complementary linear and nonlinear latent-variable models, and carries them through regulatory-network inference and differential testing. We evaluated FORGE on four human and mouse datasets spanning blood, brain, and kidney and two multiome chemistries, including a twelve-sample CRND8 Alzheimers disease cohort. We report cross-modal agreement alongside missing-modality reconstruction and an accounting of computational cost. In the Alzheimer's cohort, FORGE nominated a glial Mef2c-associated program defensible across expression, co-accessibility, footprinting, and eRegulon evidence. In human PBMC, FORGE layered evidence models also provide nuanced interpretations that largely corroborate previously published regulatory links while also proposing an additional CD83 myeloid module.\n\nUpdate (2026-09-18): \n\nPost-selection inference in testing for phenotypic differences with scRNA-Seq \n\n. For the purpose of differential expression (DE) analysis in single-cell RNA-sequencing (scRNA-Seq), phenotype differences between samples are often tested within specific cell types. Cell types are regularly imputed by clustering the same gene expression data which is later used for phenotype testing. This creates the potential for a \"double-dipping\" or post-selection inference problem resulting in inflated rates of false discoveries. While this selection bias is known to inflate significance in cell-type marker identification, its effect on sample-level phenotype testing, e.g. in patient cohorts, has never been explored despite the growing preponderance of this type of analysis. To address this, we perform an extensive simulation study and demonstrate that naive clustering on uncorrected embeddings can severely inflate the False Discovery Rate (FDR) in the presence of strong phenotypic differences. However, we further show that applying batch-correction methods to remove phenotypic effects prior to clustering resolves the FDR inflation with no obvious loss of power. Finally, \n\nUpdate (2026-09-18): \n\nDeepVir: A reproducible workflow for large-scale viral dark matter discovery \n\n. High-throughput sequencing (HTS) has revolutionized virosphere exploration. However, characterizing highly divergent viral sequences remains a bottleneck known as Viral Dark Matter (VDM). Numerous tools were developed to unravel such diversity, but they usually require complex prior HTS data analysis processes. Consequently, a common bottleneck to VDM exploration is the manual, chained execution of complex command-line applications. To address the need for automated and scalable viral discovery, we developed DeepVir, a reproducible Snakemake pipeline that integrates classical homology-based alignments with profile Hidden Markov Model (HMM) mining of the RNA-dependent RNA polymerase (RdRp). To validate the pipelines efficacy, we analyzed 385.23 GB of publicly available transcriptomic data (203 Sequence Read Archive libraries) from 49 American bat species. DeepVir successfully identified 179 distinct viral groups. This included 903 contigs spanning nine known viral families, enabling the characterization of novel genomes within Orthomyxoviridae (Influenza A H7N9), Picornaviridae,","ledger_type":"benefit","primary_domain_id":"8f1af1b9-7302-4b51-aae9-fdfac04d158a","event_status":"provisional","event_date":"2026-09-16T00:00:00.000Z","discovery_date":"2026-09-16T00:00:00.000Z","first_published_date":"2026-09-16T00:00:00.000Z","last_reviewed_date":"2026-09-18T00:00:00.000Z","geographic_scope":"International","affected_population":null,"base_impact_tier":1,"base_score":"1.00","attribution_multiplier":"0.1000","evidence_multiplier":"0.3000","realization_multiplier":"0.2000","durability_multiplier":"0.5000","current_event_score":"0.003000","confidence_level":"low","score_explanation":"Ingest update: independent source raised evidence; added a follow-up source. Score remains provisional until review. Follow-up ingest: independent source raised evidence; added a follow-up source.","methodology_version_id":"8745541b-0f8e-4d5a-9b47-5291d3813a8d","original_methodology_version_id":"8745541b-0f8e-4d5a-9b47-5291d3813a8d","published_at":"2026-09-16T18:00:53.483Z","created_at":"2026-09-16T18:00:53.483Z","updated_at":"2026-09-19T07:00:56.166Z","flags":[],"domain_name":"Biology","domain_slug":"biology","methodology_version":"0.2"},"contributions":[{"id":"204a61b3-8a8c-4eb9-88cc-25c284716bbe","event_id":"76386ef1-bbbc-43fd-ac38-39f360fef41e","model_id":"535014f7-c941-4d39-aa9b-8f9c1d3d07b7","role_description":"Unspecified system mentioned or implied by a news item. Remap to a named release when identified.","attribution_multiplier":"0.1000","credit_share":"1.0000","contribution_score":"0.003000","attribution_rationale":"News ingest does not infer a named model from the publisher alone. Attribution stays unspecified until a release is identified.","attribution_confidence":"medium","first_used_date":"2026-09-16T00:00:00.000Z","model_version_if_known":null,"review_status":"approved","created_at":"2026-09-16T18:00:53.526Z","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":"34e1048e-9863-49a6-ba98-b7aa7a6e3d88","event_id":"76386ef1-bbbc-43fd-ac38-39f360fef41e","url":"\nhttps://www.biorxiv.org/content/10.64898/2026.09.10.750690v1?rss=1\n","canonical_url":"\nhttps://www.biorxiv.org/content/10.64898/2026.09.10.750690v1?rss=1\n","source_type":"preprint","publisher":"bioRxiv","author":null,"publication_date":"2026-09-16T00:00:00.000Z","retrieved_at":"2026-09-16T18:00:53.590Z","title":"\n\nFlow Orchestrated Regulatory Genomics Engine (FORGE): A Configurable Nextflow Pipeline for End-to-End snMultiome Analysis \n\n","excerpt":"MOTIVATION Single-nucleus resolution multiome (snMultiome) assays concurrently profile gene expression and chromatin accessibility in the same nucleus. Yet regulatory inference from such analyses are difficult to scale, audit, and reproduce; moreover, as a field, snMultiomics and its toolset remains far from standardized. To address these challenges we developed FORGE, a configureable Nextflow workflow that carries paired data from raw counts and fragments through regulatory network inference wi","content_hash":null,"source_reliability_class":"medium","is_primary_source":true,"is_independent":true,"is_peer_reviewed":false,"archived_url":null,"created_at":"2026-09-16T18:00:53.590Z"},{"id":"dcf8e241-48bc-4b1b-b802-922e5f61d131","event_id":"76386ef1-bbbc-43fd-ac38-39f360fef41e","url":"\nhttps://www.biorxiv.org/content/10.64898/2026.09.17.752411v1?rss=1\n","canonical_url":"\nhttps://www.biorxiv.org/content/10.64898/2026.09.17.752411v1?rss=1\n","source_type":"preprint","publisher":"bioRxiv","author":null,"publication_date":"2026-09-18T00:00:00.000Z","retrieved_at":"2026-09-19T03:00:56.537Z","title":"\n\nPost-selection inference in testing for phenotypic differences with scRNA-Seq \n\n","excerpt":"For the purpose of differential expression (DE) analysis in single-cell RNA-sequencing (scRNA-Seq), phenotype differences between samples are often tested within specific cell types. Cell types are regularly imputed by clustering the same gene expression data which is later used for phenotype testing. This creates the potential for a \"double-dipping\" or post-selection inference problem resulting in inflated rates of false discoveries. While this selection bias is known to inflate significance in","content_hash":null,"source_reliability_class":"medium","is_primary_source":false,"is_independent":false,"is_peer_reviewed":false,"archived_url":null,"created_at":"2026-09-19T03:00:56.537Z"},{"id":"7f89244c-0def-4447-91fa-25508ca296ac","event_id":"76386ef1-bbbc-43fd-ac38-39f360fef41e","url":"\nhttps://www.biorxiv.org/content/10.64898/2026.09.15.751837v1?rss=1\n","canonical_url":"\nhttps://www.biorxiv.org/content/10.64898/2026.09.15.751837v1?rss=1\n","source_type":"preprint","publisher":"bioRxiv","author":null,"publication_date":"2026-09-18T00:00:00.000Z","retrieved_at":"2026-09-19T07:00:56.152Z","title":"\n\nDeepVir: A reproducible workflow for large-scale viral dark matter discovery \n\n","excerpt":"High-throughput sequencing (HTS) has revolutionized virosphere exploration. However, characterizing highly divergent viral sequences remains a bottleneck known as Viral Dark Matter (VDM). Numerous tools were developed to unravel such diversity, but they usually require complex prior HTS data analysis processes. Consequently, a common bottleneck to VDM exploration is the manual, chained execution of complex command-line applications. To address the need for automated and scalable viral discovery,","content_hash":null,"source_reliability_class":"medium","is_primary_source":false,"is_independent":false,"is_peer_reviewed":false,"archived_url":null,"created_at":"2026-09-19T07:00:56.152Z"}],"claims":[{"id":"0b47f24b-c387-4218-90fe-cade8bf636df","event_id":"76386ef1-bbbc-43fd-ac38-39f360fef41e","claim_text":"\n\nFlow Orchestrated Regulatory Genomics Engine (FORGE): A Configurable Nextflow Pipeline for End-to-End snMultiome Analysis \n\n","claim_type":"outcome","claim_status":"supported","confidence_score":null,"created_at":"2026-09-16T18:00:53.603Z","updated_at":"2026-09-16T18:00:53.603Z"}],"revisions":[{"id":"aeea30f3-6364-41e1-9206-a040591939d6","event_id":"76386ef1-bbbc-43fd-ac38-39f360fef41e","model_id":null,"previous_score":"0.000000","new_score":"0.001000","previous_factors":{},"new_factors":{"evidence":0.1,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"change_reason":"Auto-published from news ingest.","trigger_type":"news_ingest","trigger_source_ids":null,"reviewer_id":null,"review_status":"published","created_at":"2026-09-16T18:00:53.618Z"},{"id":"678b14f2-802a-4a26-a717-f13c4eb3af1d","event_id":"76386ef1-bbbc-43fd-ac38-39f360fef41e","model_id":null,"previous_score":"0.001000","new_score":"0.002000","previous_factors":{"tier":1,"evidence":0.1,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"new_factors":{"tier":1,"models":["535014f7-c941-4d39-aa9b-8f9c1d3d07b7"],"evidence":0.2,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"change_reason":"Ingest update: independent source raised evidence; added a follow-up source.","trigger_type":"ingest_update","trigger_source_ids":["dcf8e241-48bc-4b1b-b802-922e5f61d131"],"reviewer_id":null,"review_status":"published","created_at":"2026-09-19T03:00:56.563Z"},{"id":"1ae2a819-fcaf-487c-b007-735e63ea16a7","event_id":"76386ef1-bbbc-43fd-ac38-39f360fef41e","model_id":null,"previous_score":"0.002000","new_score":"0.003000","previous_factors":{"tier":1,"evidence":0.2,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"new_factors":{"tier":1,"models":["535014f7-c941-4d39-aa9b-8f9c1d3d07b7"],"evidence":0.3,"base_score":1,"durability":0.5,"attribution":0.1,"realization":0.2},"change_reason":"Ingest update: independent source raised evidence; added a follow-up source.","trigger_type":"ingest_update","trigger_source_ids":["7f89244c-0def-4447-91fa-25508ca296ac"],"reviewer_id":null,"review_status":"published","created_at":"2026-09-19T07:00:56.174Z"}],"secondary":[]},"methodology_version":"0.1"}