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bioRxiv · 10.1101/2024.05.20.595042

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

Boughter, C. T.·Chatterjee, B.·Ohta, Y.·Gorga, K.·Blair, C.·Hill, E. M.·Fasana, Z.·Adebamowo, A.·Ammar, F.·Kosik, I.·Murugan, V.·Chen, W. H.·Singh, N. J.·Meier-Schellersheim, M.
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Abstract

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.

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Boughter, C. T., Chatterjee, B., Ohta, Y., Gorga, K., Blair, C., Hill, E. M., Fasana, Z., Adebamowo, A., Ammar, F., Kosik, I., Murugan, V., Chen, W. H., Singh, N. J., Meier-Schellersheim, M.. 2024-05-22. CountASAP: A Lightweight, Easy to Use Python Package for Processing ASAPseq Data. https://doi.org/10.1101/2024.05.20.595042

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