kallisto, bustools and kb-python for quantifying bulk, single-cell and single-nucleus RNA-seq.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• RNA sequencing (RNA-seq) is crucial for quantifying RNA species in various biological contexts.
• Existing RNA-seq analysis workflows can be computationally intensive and time-consuming.
• Accurate gene expression quantification is essential for understanding cellular function and disease.

Purpose of the Study:

• To present a streamlined protocol for RNA-seq data preprocessing using open-source software.
• To enable efficient gene expression quantification from raw sequencing reads.
• To facilitate the distinction between nascent and mature RNA species.

Main Methods:

• Utilized kallisto, bustools, and kb-python for RNA-seq data analysis.
• Developed a protocol for preprocessing RNA-seq data using these command-line tools.
• Enabled quantification of gene expression at single-cell or bulk tissue levels.

Main Results:

• Achieved rapid gene expression quantification from raw RNA-seq reads.
• Successfully individualized quantifications for multiple cells and samples.
• Enabled classification of RNA species into nascent or mature categories.

Conclusions:

• The kallisto, bustools, and kb-python workflow provides an efficient and accessible method for RNA-seq data analysis.
• This protocol supports both cell-based and nucleus-based assays.
• The workflow significantly reduces the time required for RNA-seq data quantification.