{"doi":"10.1101/558395","title":"Toward perfect reads: short reads correction via mapping on compacted de Bruijn graphs","abstract":"Abstract\n \n Motivations\n Short-read accuracy is important for downstream analyses such as genome assembly and hybrid long-read correction. Despite much work on short-read correction, present-day correctors either do not scale well on large data sets or consider reads as mere suites of k-mers, without taking into account their full-length read information.\n \n \n Results\n \n We propose a new method to correct short reads using de Bruijn graphs, and implement it as a tool called Bcool. As a first step, Bcool constructs a compacted de Bruijn graph from the reads. This graph is filtered on the basis of\n k\n -mer abundance then of unitig abundance, thereby removing most sequencing errors. The cleaned graph is then used as a reference on which the reads are mapped to correct them. We show that this approach yields more accurate reads than\n k\n -mer-spectrum correctors while being scalable to human-size genomic datasets and beyond.\n \n \n \n Availability and Implementation\n \n The implementation is open source and available at\n http://github.com/Malfoy/BCOOL\n under the Affero GPL license and as a Bioconda package.\n \n \n \n Contact\n \n Antoine Limasset\n antoine.limasset@gmail.com\n & Jean-François Flot\n jflot@ulb.ac.be\n & Pierre Peterlongo\n pierre.peterlongo@inria.fr\n \n ","journal":null,"year":null,"id":594198,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1169936,"name":"Jean‐François Flot","orcid":"0000-0003-4091-7916","position":1,"is_corresponding":false},{"id":1520976,"name":"Pierre Peterlongo","orcid":"0000-0003-0776-6407","position":2,"is_corresponding":false},{"id":632338,"name":"Antoine Limasset","orcid":"0000-0002-0669-4141","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Toward perfect reads: short reads correction via mapping on compacted de Bruijn graphs","abstract":"Abstract\n \n Motivations\n Short-read accuracy is important for downstream analyses such as genome assembly and hybrid long-read correction. Despite much work on short-read correction, present-day correctors either do not scale well on large data sets or consider reads as mere suites of k-mers, without taking into account their full-length read information.\n \n \n Results\n \n We propose a new method to correct short reads using de Bruijn graphs, and implement it as a tool called Bcool. As a first step, Bcool constructs a compacted de Bruijn graph from the reads. This graph is filtered on the basis of\n k\n -mer abundance then of unitig abundance, thereby removing most sequencing errors. The cleaned graph is then used as a reference on which the reads are mapped to correct them. We show that this approach yields more accurate reads than\n k\n -mer-spectrum correctors while being scalable to human-size genomic datasets and beyond.\n \n \n \n Availability and Implementation\n \n The implementation is open source and available at\n http://github.com/Malfoy/BCOOL\n under the Affero GPL license and as a Bioconda package.\n \n \n \n Contact\n \n Antoine Limasset\n antoine.limasset@gmail.com\n & Jean-François Flot\n jflot@ulb.ac.be\n & Pierre Peterlongo\n pierre.peterlongo@inria.fr\n \n ","is_dataset_classified":null,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"23304386","pmcid":null,"openalex_id":"https://openalex.org/W2915362964","authors":[],"funders":[],"total_grants":0,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2019,"count":1},{"year":2020,"count":2}],"oa_status":"green","license":"cc-by","oa_locations":[{"url":"https://www.biorxiv.org/content/biorxiv/early/2019/02/28/558395.full.pdf","host_type":"repository"},{"url":"https://www.biorxiv.org/content/biorxiv/early/2019/02/28/558395.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/558395","host_type":"publisher"},{"url":"https://doi.org/10.1101/558395","host_type":"repository"}],"fields_of_study":["Genomics and Phylogenetic Studies","Molecular Biology Techniques and Applications","Environmental DNA in Biodiversity Studies"],"mesh_terms":[],"keywords":["De Bruijn sequence","De Bruijn graph","Computer science","Scalability","k-mer","Graph","Sequence assembly","Theoretical computer science","Genome","Biology","Combinatorics","Database","Mathematics","Gene","Genetics"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-27T13:51:34.324703Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}