biostudies-arrayexpressBudha ChatterjeeMus musculushttps://www.ebi.ac.uk/biostudies/studies/E-MTAB-15786A 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.biostudies-arrayexpressSequencing - hardware: Illumina NovaSeq 6000 performer: JHU SCTCLibrary Construction - 10x Genomics ATAC protocol (CG000496 Rev B).Sample Collection - Experimental protocols for ASAPseq data acquisition followed the following design: dissociated single cell suspensions from each mouse were dispensed into two sets of 96-well plates, with each well containing approximately 150,000 cells. Then cells were blocked with an anti-mouse CD16/32 antibody (Trustain FcX Plus: BioLegend) to prevent non-specific binding via the Fc receptor. After blocking, the cells were stained with Total Seq-B anti-mouse hashtag antibody (BioLegend). Hash-tagged cells were then pooled together (approx. 2 million cells) and stained with a mixture of sixty-two TotalSeq-B anti-mouse surface protein panel antibodies at concentrations ranging from 12.5ng to 100ng per pooled sample. Stained cells were washed with Cell Staining Buffer (BioLegend). Cell numbers were counted, along with viability.Sample Treatment - Pre-exposed mice (now 12-15 weeks old) were injected intraperitoneally with 3mg anti-mouse IFNAR-1 to prevent rapid clearance of the vaccine’s virus by Type I interferon responses. One day later, mice were vaccinated subcutaneously in one caudal thigh muscle with live-attenuated virus yellow fever vaccine YF17D (Sanofi Pasteur) or the same vaccine inactivated with formalin.Nucleic Acid Extraction - Serum was removed from whole blood, after which red blood cells were lysed with Ack lysing buffer. Lymphocytes were isolated from all tissues using density gradient centrifugation. Cells were stained with CITE-seq antibodies (HTOs and ADTs), after which single cells were encapsulated in gel-beads-in-emulsion.Growth Protocol - 5-week-old C57BL/6J mice were pre-exposed sequentially to Influenza A/Puerto Rico/8/34 (PR8) virus and Plasmodium yoelii strain 17XNL. Four doses of 1mg chloroquine were administered intraperitoneally over the course of 3 weeks to clear the malaria infection.OrganizationMINSEQE ScoreAssays and DataProcessed DataMAGE-TAB FilesSequence Alignment - alignment and quantification; cellranger-atac count v2.1.0, mm10 alignment and quantification; countASAP (asapSeq_barcodes.csv)Data Transformation - Processing of the ASAP reads is done with the tool countASAP that we report in the accompanying manuscript. The aligned ATAC outputs from CellRanger ATAC were further processed using standard Signac pipeline.MetabolomicsUnknownTranscriptomicsGenomicsProteomicsIllumina NovaSeq 6000Hospitals preparing for Joint Commission surveys should pay close attention to improving organizational performance, says Eric Silfen, former chief medical officer at Reston (VA) Hospital Center, who now oversees the hospital's outcomes research division.ATAC-seqMus musculusCountASAP: A Lightweight, Easy to Use Python Package for Processing ASAPseq DataBudha ChatterjeeChristopher T Boughter, Budhaditya Chatterjee, Yuko Ohta, Katrina Gorga, Carly Blair, Elizabeth M Hill, Zachary Fasana, Adedola Adebamowo, Farah Ammar, Ivan Kosik, Vel Murugan, Wilbur H Chen, Nevil J Singh, Martin Meier-SchellersheimNevil SinghChristopher BougterfalseTest dataset for countASAP package: a lightweight easy to use Python package for processing ASAPseq dataA 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.2025-10-24T00:00:00Z2026-05-27T12:54:52.995Z2025-10-22T15:04:44.208ZE-MTAB-1578611188107ERP182720EFO_0002944EFO_0007045EFO_0004170EFO_0003789EFO_0004917EFO_0005518EFO_0003816EFO_0004184EFO_000396910.1101/2024.05.20.595042