{"doi":"10.1101/444711","title":"LuxRep: a technical replicate-aware method for bisulfite sequencing data analysis","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>DNA methylation is measured using bisulfite sequencing (BS-seq). Bisulfite conversion can have low efficiency and a DNA sample is then processed multiple times generating DNA libraries with different bisulfite conversion rates. Libraries with low conversion rates are excluded from analysis resulting in reduced coverage and increased costs. We present a method and software, LuxRep, that accounts for technical replicates from different bisulfite-converted DNA libraries. We show that including replicates with low bisulfite conversion rates generates more accurate estimates of methylation levels and differentially methylated sites.</jats:p>\n                <jats:sec>\n                  <jats:title>Availability</jats:title>\n                  <jats:p>\n                    An implementation of the method is available at\n                    <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"https://github.com/tare/LuxGLM/tree/master/LuxRep\">https://github.com/tare/LuxGLM/tree/master/LuxRep</jats:ext-link>\n                  </jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Contact</jats:title>\n                  <jats:p>\n                    <jats:email>maia.malonzo@aalto.fi</jats:email>\n                  </jats:p>\n                </jats:sec>","journal":null,"year":null,"id":631123,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"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":1528921,"name":"Viivi Halla-aho","orcid":null,"position":1,"is_corresponding":false},{"id":1635431,"name":"Mikko Konki","orcid":null,"position":2,"is_corresponding":false},{"id":1635434,"name":"Riikka J. Lund","orcid":null,"position":3,"is_corresponding":false},{"id":657180,"name":"Harri Lähdesmäki","orcid":"0000-0002-0373-139X","position":4,"is_corresponding":false},{"id":1635430,"name":"Maia Malonzo","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"LuxRep: a technical replicate-aware method for bisulfite sequencing data analysis","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>DNA methylation is measured using bisulfite sequencing (BS-seq). Bisulfite conversion can have low efficiency and a DNA sample is then processed multiple times generating DNA libraries with different bisulfite conversion rates. Libraries with low conversion rates are excluded from analysis resulting in reduced coverage and increased costs. We present a method and software, LuxRep, that accounts for technical replicates from different bisulfite-converted DNA libraries. We show that including replicates with low bisulfite conversion rates generates more accurate estimates of methylation levels and differentially methylated sites.</jats:p>\n                <jats:sec>\n                  <jats:title>Availability</jats:title>\n                  <jats:p>\n                    An implementation of the method is available at\n                    <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"https://github.com/tare/LuxGLM/tree/master/LuxRep\">https://github.com/tare/LuxGLM/tree/master/LuxRep</jats:ext-link>\n                  </jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Contact</jats:title>\n                  <jats:p>\n                    <jats:email>maia.malonzo@aalto.fi</jats:email>\n                  </jats:p>\n                </jats:sec>","is_dataset_classified":null,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19910364","pmcid":null,"openalex_id":"https://openalex.org/W2895945844","authors":[],"funders":[{"funder_name":"Research Council of Finland","grant_id":"311584","title":"Quantifying molecular networks at single-cell level"},{"funder_name":"Research Council of Finland","grant_id":"292660","title":"Personalised medicine to predict and prevent Type 1 Diabetes (P4 Diabetes) / Consortium: P4 Diabetes"},{"funder_name":"Research Council of Finland","grant_id":"335436","title":"Immunoregulation and Therapeutic Precision in Rheumatoid Arthritis"}],"total_grants":3,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2019,"count":1},{"year":2020,"count":1}],"oa_status":"green","license":"cc-by-nc","oa_locations":[{"url":"https://www.biorxiv.org/content/biorxiv/early/2018/10/19/444711.full.pdf","host_type":"repository"},{"url":"https://www.biorxiv.org/content/biorxiv/early/2018/10/19/444711.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/444711","host_type":"publisher"},{"url":"https://doi.org/10.1101/444711","host_type":"repository"},{"url":"https://doi.org/10.1186/s12859-021-04546-1","host_type":""},{"url":"https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-021-04546-1","host_type":""},{"url":"https://pubmed.ncbi.nlm.nih.gov/35030989","host_type":""},{"url":"http://dx.doi.org/10.1186/s12859-021-04546-1","host_type":""},{"url":"https://doi.org/10.1186/s12859021045461","host_type":""},{"url":"http://juuli.fi/Record/0389224622","host_type":""},{"url":"https://doaj.org/article/40fccb8f66b345d68f6795ce37fbbde2","host_type":""},{"url":"https://dx.doi.org/10.1101/444711","host_type":""},{"url":"https://aaltodoc.aalto.fi/handle/123456789/112737","host_type":""},{"url":"http://dx.doi.org/10.1101/444711","host_type":""}],"fields_of_study":["Epigenetics and DNA Methylation","RNA modifications and cancer","Cancer-related gene regulation","0301 basic medicine","0206 medical engineering","02 engineering and technology","03 medical and health sciences"],"mesh_terms":[],"keywords":["Bisulfite","Bisulfite sequencing","Replicate","DNA methylation","Computer science","Sodium bisulfite","DNA","Computational biology","Methylation","Methylated DNA immunoprecipitation","Biology","Chemistry","Genetics","Mathematics","Gene","Statistics","Data Analysis","QH301-705.5","Computer applications to medicine. Medical informatics","R858-859.7","High-Throughput Nucleotide Sequencing","Probabilistic","Sequence Analysis, DNA","ta3111","Sulfites","Biology (General)","Software"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-05T22:51:48.428797Z","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":[]}