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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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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scPathoQuant: a tool for efficient alignment and quantification of pathogen sequence reads from 10× single cell

Leanne S Whitmore1,2, Jennifer Tisoncik-Go1,2, Michael Gale1,2,3

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A new computational tool, scPathoQuant, enables simultaneous mapping of pathogen and host sequences from single-cell RNA sequencing data. This streamlines analysis for pathogen detection and quantification within infected cells.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Single-cell RNA sequencing (scRNAseq) generates vast amounts of data for studying cellular responses.
  • Analyzing pathogen and host sequences simultaneously from scRNAseq is computationally challenging.
  • Current methods require multiple steps and tools, increasing analysis time.

Purpose of the Study:

  • To develop an efficient computational tool for simultaneous mapping of pathogen and host reads from scRNAseq data.
  • To provide a single-command solution for analyzing pathogen-host interactions at the single-cell level.
  • To facilitate the quantification of pathogen sequences within infected host cells.

Main Methods:

  • Development of a Python package, scPathoQuant.
  • Extraction of unaligned sequences from scRNAseq data.
  • Mapping of extracted sequences to pathogen genomes (demonstrated for viral pathogens).
  • Quantification of pathogen reads at the whole genome and gene levels.
  • Integration of pathogen counts into standard scRNAseq analysis pipelines.

Main Results:

  • scPathoQuant enables direct mapping and quantification of pathogen and host reads in a single operation.
  • The tool successfully quantifies viral reads and integrates them into downstream analysis matrices.
  • Demonstrated ability to differentiate and define multiple viruses within a single scRNAseq sample.
  • Provides genome-wide sequence read abundance analysis for viral and host genomes.

Conclusions:

  • scPathoQuant addresses the need for efficient computational tools in pathogen-host scRNAseq analysis.
  • The package simplifies and accelerates the analysis of pathogen infection dynamics at the single-cell level.
  • scPathoQuant serves as a valuable community resource for infectious disease research using scRNAseq.