biostudies-arrayexpressBudha ChatterjeeMus musculushttps://www.ebi.ac.uk/biostudies/studies/E-MTAB-15923A 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. This dataset is only the CSP layer of a CITE-seq well. We use this data to benchmark our tool (countASAP) against existing tools such as Kallisto and Cell Ranger. ASAP-seq data could not be directly used for this cross platform benchmarking since the latter tools are explicitly designed to support alignment of CITE-seq feature barcodes but not those from ASAP-seq.biostudies-arrayexpressSample Collection - On day 0 (D0), groups of 6 unavaccinated mice (3 male (M) and 3 female (F)) were sampled which were either exposed to infections (VIPEX) or were specific pathogen free (naive). Groups of 6 mice (3 male (M) and 3 female (F)) were sampled on day 1 after vaccination with YF17D or InYF. Tissues were isolated and processed for CITEseq. SRA acession: SRS23273791; BioSample accession: SAMN44846749;Library Construction - 10x GENOMICS; Chromium Next GEM Single Cell 5'HT reagent kits v2 (dual index)Nucleic Acid Extraction - The PBMC samples of the individual mice received hashtags and were pooled together before the cells were profiled using a droplet based system (10x Genomics).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. Samples were collected at the following timepoints: Day 0 (pre-vaccination control) and Days 1, 3, 5, 7, and 30 post-vaccination. Mice used for all timepoints later than Day 3 received additional i.p. injections of 0.6mg anti-mouse IFNAR-1 on Days 2 and 3 post-vaccination.Sequencing - hardware: Illumina NovaSeq 6000 Org: JHU SCTC Run accession: SRR31418622OrganizationMINSEQE ScoreAssays and DataProcessed DataMAGE-TAB FilesData Transformation - Counts were derived using countASAP software (assay = CITE).UnknownTranscriptomicsGenomicsProteomicsIllumina 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.RNA-seq of coding RNA from single cellsMus musculusCountASAP: A Lightweight, Easy to Use Python Package for Processing ASAPseq DataBudha ChatterjeeChristopher BoughterNevil SinghfalseCITE-seq dataset for bench marking countASAP: 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. This dataset is only the CSP layer of a CITE-seq well. We use this data to benchmark our tool (countASAP) against existing tools such as Kallisto and Cell Ranger. ASAP-seq data could not be directly used for this cross platform benchmarking since the latter tools are explicitly designed to support alignment of CITE-seq feature barcodes but not those from ASAP-seq.2025-10-22T00:00:00Z2026-05-27T16:00:22.438Z2025-10-22T17:38:19.433ZE-MTAB-1592311188107EFO_0002944EFO_0004170EFO_0005684EFO_0005518EFO_0003816EFO_0004184EFO_000396910.1101/2024.05.20.595042