Fast analysis of scATAC-seq data using a predefined set of genomic regions [version 2; peer review: 2 approved]

• Published in: F1000 research Vol. 9; p. 199
• Main Authors: Giansanti, Valentina, Tang, Ming, Cittaro, Davide
• Format: Journal Article
• Language: English
• Published: England Faculty of 1000 Ltd 2020; F1000 Research Limited; F1000 Research Ltd
• Subjects: Computational Biology; Computer applications; Deoxyribonuclease; Genome; Genomes; Genomics; Genomics - methods; Humans; K562 Cells; Labeling; Leukocytes, Mononuclear; Method; Peripheral blood mononuclear cells; Sequence Alignment; Sequence Analysis, DNA; single cell; scATAC-seq; pseudoalignment
• ISSN: 2046-1402; 2046-1402
• DOI: 10.12688/f1000research.22731.2

Background: 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. Methods: 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 kallisto and quantified with bustools. We compared our results with the ones publicly available derived by cellranger-atac. We subsequently tested our approach on scATAC-seq data for K562 cell line. Results: We found that kallisto does not introduce biases in quantification of known peaks; cells groups identified are consistent with the ones identified from standard method. We also found that cell identification is robust when analysis is performed using DHS-derived reference in place of de novo 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. Conclusions: Analysis of scATAC-seq data by means of kallisto 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.