{"id":"https://openalex.org/W2998137959","doi":"https://doi.org/10.1089/cmb.2019.0314","title":"Fast Approximation of Frequentk-Mers and Applications to Metagenomics","display_name":"Fast Approximation of Frequentk-Mers and Applications to Metagenomics","publication_year":2019,"publication_date":"2019-12-31","ids":{"openalex":"https://openalex.org/W2998137959","doi":"https://doi.org/10.1089/cmb.2019.0314","mag":"2998137959","pmid":"https://pubmed.ncbi.nlm.nih.gov/31891535"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1089/cmb.2019.0314","pdf_url":null,"source":{"id":"https://openalex.org/S78571599","display_name":"Journal of Computational Biology","issn_l":"1066-5277","issn":["1066-5277","1557-8666"],"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":true,"is_core":true,"host_organization":"https://openalex.org/P4310320443","host_organization_name":"Mary Ann Liebert, Inc.","host_organization_lineage":["https://openalex.org/P4310320443"],"host_organization_lineage_names":["Mary Ann Liebert, Inc."],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"journal-article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1902.10168","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021603227","display_name":"Leonardo Pellegrina","orcid":"https://orcid.org/0000-0002-6601-5526"},"institutions":[{"id":"https://openalex.org/I138689650","display_name":"University of Padua","ror":"https://ror.org/00240q980","country_code":"IT","type":"funder","lineage":["https://openalex.org/I138689650"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Leonardo Pellegrina","raw_affiliation_strings":["Department of Information Engineering, University of Padova, Padova, Italy."],"affiliations":[{"raw_affiliation_string":"Department of Information Engineering, University of Padova, Padova, Italy.","institution_ids":["https://openalex.org/I138689650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079827122","display_name":"Cinzia Pizzi","orcid":"https://orcid.org/0000-0002-6616-4003"},"institutions":[{"id":"https://openalex.org/I138689650","display_name":"University of Padua","ror":"https://ror.org/00240q980","country_code":"IT","type":"funder","lineage":["https://openalex.org/I138689650"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Cinzia Pizzi","raw_affiliation_strings":["Department of Information Engineering, University of Padova, Padova, Italy."],"affiliations":[{"raw_affiliation_string":"Department of Information Engineering, University of Padova, Padova, Italy.","institution_ids":["https://openalex.org/I138689650"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060279781","display_name":"Fabio Vandin","orcid":"https://orcid.org/0000-0003-2244-2320"},"institutions":[{"id":"https://openalex.org/I138689650","display_name":"University of Padua","ror":"https://ror.org/00240q980","country_code":"IT","type":"funder","lineage":["https://openalex.org/I138689650"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Fabio Vandin","raw_affiliation_strings":["Department of Information Engineering, University of Padova, Padova, Italy."],"affiliations":[{"raw_affiliation_string":"Department of Information Engineering, University of Padova, Padova, Italy.","institution_ids":["https://openalex.org/I138689650"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5060279781"],"corresponding_institution_ids":["https://openalex.org/I138689650"],"apc_list":null,"apc_paid":null,"fwci":0.563,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.833785,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":82,"max":83},"biblio":{"volume":"27","issue":"4","first_page":"534","last_page":"549"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11269","display_name":"Algorithms and Data Compression","score":0.9996,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11269","display_name":"Algorithms and Data Compression","score":0.9996,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10015","display_name":"Genomics and Phylogenetic Studies","score":0.9979,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12254","display_name":"Machine Learning in Bioinformatics","score":0.9962,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.46715325}],"concepts":[{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6160621},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.61033165},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.522156},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.51074505},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.46715325},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4603389},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.45881113},{"id":"https://openalex.org/C148764684","wikidata":"https://www.wikidata.org/wiki/Q621751","display_name":"Approximation algorithm","level":2,"score":0.44314924},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3969157},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1886001},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D059014","descriptor_name":"High-Throughput Nucleotide Sequencing","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D056186","descriptor_name":"Metagenomics","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D017422","descriptor_name":"Sequence Analysis, DNA","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D012984","descriptor_name":"Software","qualifier_ui":"","qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":"","qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D019295","descriptor_name":"Computational Biology","qualifier_ui":"","qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D059014","descriptor_name":"High-Throughput Nucleotide Sequencing","qualifier_ui":"","qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D054892","descriptor_name":"Metagenome","qualifier_ui":"","qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D054892","descriptor_name":"Metagenome","qualifier_ui":"Q000235","qualifier_name":"genetics","is_major_topic":false},{"descriptor_ui":"D056186","descriptor_name":"Metagenomics","qualifier_ui":"","qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D018401","descriptor_name":"Sample Size","qualifier_ui":"","qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D017422","descriptor_name":"Sequence Analysis, DNA","qualifier_ui":"","qualifier_name":null,"is_major_topic":false}],"locations_count":3,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1089/cmb.2019.0314","pdf_url":null,"source":{"id":"https://openalex.org/S78571599","display_name":"Journal of Computational Biology","issn_l":"1066-5277","issn":["1066-5277","1557-8666"],"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":true,"is_core":true,"host_organization":"https://openalex.org/P4310320443","host_organization_name":"Mary Ann Liebert, Inc.","host_organization_lineage":["https://openalex.org/P4310320443"],"host_organization_lineage_names":["Mary Ann Liebert, Inc."],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1902.10168","pdf_url":"https://arxiv.org/pdf/1902.10168","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/31891535","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":["National Institutes of Health"],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1902.10168","pdf_url":"https://arxiv.org/pdf/1902.10168","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":30,"referenced_works":["https://openalex.org/W1029409534","https://openalex.org/W2037444377","https://openalex.org/W2057253402","https://openalex.org/W2096128575","https://openalex.org/W2097066660","https://openalex.org/W2104677379","https://openalex.org/W2115546424","https://openalex.org/W2116412478","https://openalex.org/W2121873871","https://openalex.org/W2133956160","https://openalex.org/W2136651963","https://openalex.org/W2148603752","https://openalex.org/W2150774511","https://openalex.org/W2159954944","https://openalex.org/W2160969485","https://openalex.org/W2163584430","https://openalex.org/W2167142762","https://openalex.org/W2171003081","https://openalex.org/W2339602899","https://openalex.org/W2340761128","https://openalex.org/W2515836512","https://openalex.org/W2570372911","https://openalex.org/W2583363792","https://openalex.org/W2748261468","https://openalex.org/W2949074212","https://openalex.org/W2950150251","https://openalex.org/W2952932047","https://openalex.org/W4236362309","https://openalex.org/W4238284510","https://openalex.org/W4245619290"],"related_works":["https://openalex.org/W4231775656","https://openalex.org/W330130819","https://openalex.org/W2760721665","https://openalex.org/W2383646825","https://openalex.org/W2288610023","https://openalex.org/W2136583354","https://openalex.org/W2112044895","https://openalex.org/W2111238207","https://openalex.org/W2107954672","https://openalex.org/W2046435967"],"abstract_inverted_index":{"Estimating":[0],"the":[1,31,44,96,101,137,154,157,169,172,194,225,258],"abundances":[2,253],"of":[3,9,36,103,110,156,171,182,187,219,234,260],"all":[4,40,67],"k-mers":[5,68,139,213],"in":[6,21,59,72,90,100,114,143,257],"a":[7,13,91,125,144,175,183,199,217,220],"set":[8,148,186,222],"biological":[10,22],"sequences":[11],"is":[12,63,95,244],"fundamental":[14],"and":[15,69,123,140,165,223,247],"challenging":[16],"problem":[17],"with":[18,86],"many":[19],"applications":[20,61],"analysis.":[23],"Although":[24,58],"several":[25],"methods":[26],"have":[27],"been":[28],"designed":[29],"for":[30,53,98,131,198],"exact":[32],"or":[33],"approximate":[34,136,211],"solution":[35],"this":[37,118],"problem,":[38],"they":[39],"require":[41],"to":[42,65,79,135,190,209,231,250],"process":[43],"entire":[45],"data":[46,56,92,108,147,221,240,263],"set,":[47],"which":[48,84],"can":[49],"be":[50,77],"extremely":[51],"expensive":[52],"high-throughput":[54,145,238],"sequencing":[55,146,239,262],"sets.":[57,241,264],"some":[60],"it":[62,75],"crucial":[64],"estimate":[66,251],"their":[70,141],"abundances,":[71],"other":[73],"situations":[74],"may":[76],"sufficient":[78],"report":[80],"only":[81,216],"frequent":[82,138,212],"k-mers,":[83],"appear":[85],"relatively":[87],"high":[88],"frequency":[89],"set.":[93],"This":[94],"case,":[97],"example,":[99],"computation":[102],"k-mers'":[104,252],"abundance-based":[105],"distances":[106,236],"among":[107],"sets":[109],"reads,":[111],"commonly":[112],"used":[113],"metagenomic":[115],"analyses.":[116],"In":[117],"study,":[119],"we":[120,166],"develop,":[121],"analyze,":[122],"test":[124],"sampling-based":[126],"approach,":[127],"called":[128],"Sampling":[129],"Algorithm":[130],"K-mErs":[132],"approxIMAtion":[133],"(SAKEIMA),":[134],"frequencies":[142,226],"while":[149],"providing":[150,254],"rigorous":[151,200,248],"guarantees":[152],"on":[153,193],"quality":[155],"approximation.":[158,201],"SAKEIMA":[159,207,229,243],"employs":[160],"an":[161,245],"advanced":[162],"sampling":[163],"scheme":[164],"show":[167],"how":[168],"characterization":[170],"Vapnik\u2013Chervonenkis":[173],"dimension,":[174],"core":[176],"concept":[177],"from":[178],"statistical":[179],"learning":[180],"theory,":[181],"properly":[184],"defined":[185],"functions":[188],"leads":[189],"practical":[191],"bounds":[192],"sample":[195],"size":[196],"required":[197],"Our":[202],"experimental":[203],"evaluation":[204],"shows":[205],"that":[206,224],"allows":[208],"rigorously":[210],"by":[214,228],"processing":[215],"fraction":[218],"estimated":[227],"lead":[230],"accurate":[232],"estimates":[233],"k-mer-based":[235],"between":[237],"Overall,":[242],"efficient":[246],"tool":[249],"significant":[255],"speedups":[256],"analysis":[259],"large":[261]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2998137959","counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2025-04-04T15:26:23.123586","created_date":"2020-01-10"}