{"data":{"event":{"id":"6e866ec7-b90e-4323-b1c3-27cc7efb954a","slug":"a-scalable-distributed-memory-mpi-implementation-of-smith-waterman-with--2bf1e81b3a","title":"\n\nA Scalable Distributed-Memory MPI Implementation of Smith-Waterman with Token-Passing Traceback \n\n","short_summary":"As genomic sequencing produces increasingly massive datasets, accurate local sequence alignment via the Smith-Waterman(SW) algorithm remains computationally prohibitive due to its space and quadratic time complexity O(mn). While parallelization addresses a path forward, existing MPI-based solutions present a critical bottleneck during the traceback phase, either omitting it entirely or gathering the entire direction matrix to a single root node, which severely limits scalability. To overcome this, we propose a fully distributed Message Passing Interface (MPI) implementation featuring a token p","full_description":"As genomic sequencing produces increasingly massive datasets, accurate local sequence alignment via the Smith-Waterman(SW) algorithm remains computationally prohibitive due to its space and quadratic time complexity O(mn). While parallelization addresses a path forward, existing MPI-based solutions present a critical bottleneck during the traceback phase, either omitting it entirely or gathering the entire direction matrix to a single root node, which severely limits scalability. To overcome this, we propose a fully distributed Message Passing Interface (MPI) implementation featuring a token passing traceback scheme. Our approach distributes the directional matrix across all participating ranks, reducing per-rank memory footprint from O(mn) to O(mn/p), thereby enabling the alignment of sequences far beyond the capacity of sequential or centralized parallel methods. We validate our method on real human DNA sequences (BRCA1, BRCA2, Titin, chr1, chr2) and synthetic datasets up to 50kx50k. Results demonstrate that this MPI implementation aligns 98kx98k real DNA (chr1xchr2) in 58.9160 seconds across 16 MPI processes, a task where the sequential baseline fails due to out-of-memory errors. We achieve best speedups on synthetic data of 19.30x (40kx40k, 16 MPI processes) and on real DNA data is 13.39x (BRCA1xTitin, 8 MPI processes), while capping per-rank memory for the largest dataset at just 573 MB. By enabling exact, memory-scalable alignment with fully distributed traceback on standard CPU MPI clusters, this work fills a critical gap in high-performance computational genomics.\n\nUpdate (2026-09-17): \n\nModeling Protein Sequence Evolution as an Ornstein-Uhlenbeck Process in a Latent Space \n\n. High-throughput directed evolution produces longitudinal sequence libraries that are ideal for probing local fitness neighborhoods but often underpowered for global inference tasks such as contact prediction. We present an unsupervised inference model that integrates directed-evolution sequencing time series with natural homologs. We project sequences into a low-dimensional latent space learned from the natural multiple sequence alignment and model the experimental process as an Ornstein-Uhlenbeck dynamics in that space. Maximum-likelihood estimation of the latent drift and noise parameters determines a stationary Gaussian distribution, which induces an effective Potts model in sequence space. The inferred couplings improve structural contact prediction by combining global evolutionary constraints from nature with local, experiment-specific signals. Experiments on PSE1 {beta}-lactamase and dihydrofolate reductase demonstrate the ability to identify correct complementary contacts not recovered by methods using either natural or experimental data alone, with gains conce","ledger_type":"benefit","primary_domain_id":"8f1af1b9-7302-4b51-aae9-fdfac04d158a","event_status":"provisional","event_date":"2026-09-14T00:00:00.000Z","discovery_date":"2026-09-14T00:00:00.000Z","first_published_date":"2026-09-14T00:00:00.000Z","last_reviewed_date":"2026-09-17T00:00:00.000Z","geographic_scope":"International","affected_population":null,"base_impact_tier":1,"base_score":"1.00","attribution_multiplier":"0.1000","evidence_multiplier":"0.2000","realization_multiplier":"0.2000","durability_multiplier":"0.5000","current_event_score":"0.002000","confidence_level":"low","score_explanation":"Ingest update: independent source raised evidence; added a follow-up source. 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While parallelization addresses a path forward, existing MPI-based solutions present a critical bottleneck during the traceback phase, either omitting it entirely or gathering the entire direction matrix to a single root node, which severely limits scalability. To overcome thi","content_hash":null,"source_reliability_class":"medium","is_primary_source":true,"is_independent":true,"is_peer_reviewed":false,"archived_url":null,"created_at":"2026-09-15T05:00:51.827Z"},{"id":"6b261f9b-be3d-4db5-8bb0-870bac74ef74","event_id":"6e866ec7-b90e-4323-b1c3-27cc7efb954a","url":"\nhttps://www.biorxiv.org/content/10.64898/2026.09.16.751972v1?rss=1\n","canonical_url":"\nhttps://www.biorxiv.org/content/10.64898/2026.09.16.751972v1?rss=1\n","source_type":"preprint","publisher":"bioRxiv","author":null,"publication_date":"2026-09-17T00:00:00.000Z","retrieved_at":"2026-09-18T02:00:50.844Z","title":"\n\nModeling Protein Sequence Evolution as an Ornstein-Uhlenbeck Process in a Latent Space \n\n","excerpt":"High-throughput directed evolution produces longitudinal sequence libraries that are ideal for probing local fitness neighborhoods but often underpowered for global inference tasks such as contact prediction. We present an unsupervised inference model that integrates directed-evolution sequencing time series with natural homologs. We project sequences into a low-dimensional latent space learned from the natural multiple sequence alignment and model the experimental process as an Ornstein-Uhlenbe","content_hash":null,"source_reliability_class":"medium","is_primary_source":false,"is_independent":false,"is_peer_reviewed":false,"archived_url":null,"created_at":"2026-09-18T02:00:50.844Z"}],"claims":[{"id":"0d60a857-e51a-4684-b2b4-f81ff5e98de5","event_id":"6e866ec7-b90e-4323-b1c3-27cc7efb954a","claim_text":"\n\nA Scalable Distributed-Memory MPI Implementation of Smith-Waterman with Token-Passing Traceback \n\n","claim_type":"outcome","claim_status":"supported","confidence_score":null,"created_at":"2026-09-15T05:00:51.835Z","updated_at":"2026-09-15T05:00:51.835Z"}],"revisions":[{"id":"3e068447-68ff-455f-9546-cab07afc2206","event_id":"6e866ec7-b90e-4323-b1c3-27cc7efb954a","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":"2026-09-15T05:00:51.843Z"},{"id":"7e1f87ca-2823-4ad2-9088-f4666f0d24c4","event_id":"6e866ec7-b90e-4323-b1c3-27cc7efb954a","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":["6b261f9b-be3d-4db5-8bb0-870bac74ef74"],"reviewer_id":null,"review_status":"published","created_at":"2026-09-18T02:00:50.872Z"}],"secondary":[]},"methodology_version":"0.1"}