CountASAP: A Lightweight, Easy to Use Python Package for Processing ASAPseq Data.

Autor:	Boughter CT; Computational Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892., Chatterjee B; Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201., Ohta Y; Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201., Gorga K; Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201., Blair C; Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201., Hill EM; Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201., Fasana Z; Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201., Adebamowo A; Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201., Ammar F; Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201., Kosik I; Cellular Biology Section, Laboratory of Viral Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892., Murugan V; Virginia G. Piper Center for Personalized Diagnostics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287., Chen WH; Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD 21201., Singh NJ; Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201., Meier-Schellersheim M; Computational Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892.
Jazyk:	angličtina
Zdroj:	BioRxiv : the preprint server for biology [bioRxiv] 2024 May 22. Date of Electronic Publication: 2024 May 22.
DOI:	10.1101/2024.05.20.595042
Abstrakt:	Declining sequencing costs coupled with the increasing availability of easy-to-use kits for the isolation of DNA and RNA transcripts from single cells have driven a rapid proliferation of studies centered around genomic and transcriptomic data. Simultaneously, a wealth of new techniques have been developed that utilize single cell technologies to interrogate a broad range of cell-biological processes. One recently developed technique, transposase-accessible chromatin with sequencing (ATAC) with select antigen profiling by sequencing (ASAPseq), provides a combination of chromatin accessibility assessments with measurements of cell-surface marker expression levels. While software exists for the characterization of these datasets, there currently exists no tool explicitly designed to reformat ASAP surface marker FASTQ data into a count matrix which can then be used for these downstream analyses. To address this, we created CountASAP, an easy-to-use Python package purposefully designed to transform FASTQ files from ASAP experiments into count matrices compatible with commonly-used downstream bioinformatic analysis packages. CountASAP takes advantage of the independence of the relevant data structures to perform fully parallelized matches of each sequenced read to user-supplied input ASAP oligos and unique cell-identifier sequences. Competing Interests: Competing Interests The authors declare no competing interests.
Databáze:	MEDLINE
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