{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/grim-filter-fast-seed-location-filtering-in","title":"GRIM-Filter: Fast Seed Location Filtering in DNA Read Mapping Using Processing-in-Memory Technologies","arxiv_id":"1711.01177","date":"2017-11-02","proceeding":null,"authors":["Jeremie S. Kim","Damla Senol Cali","Hongyi Xin","Donghyuk Lee","Saugata Ghose","Mohammed Alser","Hasan Hassan","Oguz Ergin","Can Alkan","Onur Mutlu"],"abstract":"Motivation: Seed location filtering is critical in DNA read mapping, a\nprocess where billions of DNA fragments (reads) sampled from a donor are mapped\nonto a reference genome to identify genomic variants of the donor.\nState-of-the-art read mappers 1) quickly generate possible mapping locations\nfor seeds (i.e., smaller segments) within each read, 2) extract reference\nsequences at each of the mapping locations, and 3) check similarity between\neach read and its associated reference sequences with a\ncomputationally-expensive algorithm (i.e., sequence alignment) to determine the\norigin of the read. A seed location filter comes into play before alignment,\ndiscarding seed locations that alignment would deem a poor match. The ideal\nseed location filter would discard all poor match locations prior to alignment\nsuch that there is no wasted computation on unnecessary alignments.\n  Results: We propose a novel seed location filtering algorithm, GRIM-Filter,\noptimized to exploit 3D-stacked memory systems that integrate computation\nwithin a logic layer stacked under memory layers, to perform\nprocessing-in-memory (PIM). GRIM-Filter quickly filters seed locations by 1)\nintroducing a new representation of coarse-grained segments of the reference\ngenome, and 2) using massively-parallel in-memory operations to identify read\npresence within each coarse-grained segment. Our evaluations show that for a\nsequence alignment error tolerance of 0.05, GRIM-Filter 1) reduces the false\nnegative rate of filtering by 5.59x--6.41x, and 2) provides an end-to-end read\nmapper speedup of 1.81x--3.65x, compared to a state-of-the-art read mapper\nemploying the best previous seed location filtering algorithm.\n  Availability: The code is available online at:\nhttps://github.com/CMU-SAFARI/GRIM","url_abs":"http://arxiv.org/abs/1711.01177v1","url_pdf":"http://arxiv.org/pdf/1711.01177v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"grim-filter-fast-seed-location-filtering-in","repo_url":"https://github.com/CMU-SAFARI/GRIM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}