{"doi":"10.12688/f1000research.22731.1","title":"Fast analysis of scATAC-seq data using a predefined set of genomic regions","abstract":"<ns4:p> <ns4:bold>Background:</ns4:bold> Analysis of scATAC-seq data has been recently scaled to thousands of cells. While processing of other types of single cell data was boosted by the implementation of alignment-free techniques, pipelines available to process scATAC-seq data still require large computational resources. We propose here an approach based on pseudoalignment, which reduces the execution times and hardware needs at little cost for precision. </ns4:p> <ns4:p> <ns4:bold>Methods:</ns4:bold> Public data for 10k PBMC were downloaded from 10x Genomics web site. Reads were aligned to various references derived from DNase I Hypersensitive Sites (DHS) using <ns4:italic>kallisto</ns4:italic> and quantified with <ns4:italic>bustools</ns4:italic> . We compared our results with the ones publicly available derived by <ns4:italic>cellranger-atac</ns4:italic> . </ns4:p> <ns4:p> <ns4:bold>Results:</ns4:bold> We found that <ns4:italic>kallisto</ns4:italic> does not introduce biases in quantification of known peaks and cells groups are identified in a consistent way. We also found that cell identification is robust when analysis is performed using DHS-derived reference in place of <ns4:italic>de novo</ns4:italic> identification of ATAC peaks. Lastly, we found that our approach is suitable for reliable quantification of gene activity based on scATAC-seq signal, thus allows for efficient labelling of cell groups based on marker genes. </ns4:p> <ns4:p> <ns4:bold>Conclusions:</ns4:bold> Analysis of scATAC-seq data by means of <ns4:italic>kallisto</ns4:italic> produces results in line with standard pipelines while being considerably faster; using a set of known DHS sites as reference does not affect the ability to characterize the cell populations </ns4:p>","journal":"F1000Research","year":2020,"id":110768,"datarank":0.29188652235829704,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"self_citation_contribution":0.29188652235829704,"citation_network_contribution":0.0,"self_endowment_contribution":0.29188652235829704,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9544,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":482574,"name":"Ming Tang","orcid":"0000-0002-6451-8648","position":1,"is_corresponding":false},{"id":438331,"name":"Davide Cittaro","orcid":"0000-0003-0384-3700","position":2,"is_corresponding":false},{"id":438330,"name":"Valentina Giansanti","orcid":"0000-0003-0617-6245","position":0,"is_corresponding":true}],"reference_count":29,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:13:01.914939Z","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":[]}