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Related Concept Videos

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Multi-species Conserved Sequences

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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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CountASAP: A Lightweight, Easy to Use Python Package for Processing ASAPseq Data.

Christopher T Boughter1, Budhaditya Chatterjee2, Yuko Ohta2

  • 1Computational Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892.

Biorxiv : the Preprint Server for Biology
|June 21, 2024
PubMed
Summary
This summary is machine-generated.

CountASAP is a new Python package that converts transposase-accessible chromatin with sequencing (ATAC) with select antigen profiling by sequencing (ASAPseq) FASTQ data into count matrices. This tool facilitates downstream bioinformatic analysis of single-cell genomics and transcriptomics data.

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Area of Science:

  • Single-cell genomics and transcriptomics
  • Bioinformatics tool development
  • Epigenomics and cell surface marker analysis

Background:

  • Single-cell sequencing technologies are rapidly advancing, enabling deeper biological insights.
  • Transposase-accessible chromatin with sequencing (ATAC) combined with select antigen profiling by sequencing (ASAPseq) offers multi-omic single-cell data.
  • Existing software does not specifically reformat ASAPseq surface marker FASTQ data for downstream analysis.

Purpose of the Study:

  • To develop a user-friendly Python package for processing ASAPseq FASTQ data.
  • To generate count matrices essential for downstream bioinformatic analyses of ASAPseq experiments.
  • To bridge the gap in computational tools for multi-omic single-cell data integration.

Main Methods:

  • Developed CountASAP, a Python package for FASTQ data reformatting.
  • Implemented parallelized read matching to cell identifiers and oligos.
  • Ensured compatibility with standard downstream bioinformatic analysis packages.

Main Results:

  • CountASAP successfully transforms ASAPseq FASTQ files into usable count matrices.
  • The package efficiently handles large datasets through parallel processing.
  • Generated count matrices are compatible with common bioinformatics workflows.

Conclusions:

  • CountASAP addresses a critical need for specialized software in ASAPseq data analysis.
  • The tool simplifies and accelerates the processing of single-cell multi-omic data.
  • Facilitates broader adoption and analysis of ASAPseq technology in biological research.