kallisto and bustools are wrapped in an easy-to-use program called kb which is part of the kb-python package (developer documentation). kb-python can be installed on any machine by typing pip install kb-python on the command line; this installs everything needed to process single-cell RNA-seq reads.
kb has three commands:
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usage: kb [-h] [--list] <CMD> ...
info Display package and citation information
ref Build a kallisto index and transcript-to-gene mapping
count Generate count matrices from a set of single-cell FASTQ files
-h, --help Show this help message and exit
--list Display list of supported single-cell technologies
With two simple commands, kb ref and kb count you can process single-cell RNA-seq reads:
$ kb info
kb is a python package for rapidly pre-processing single-cell RNA-seq data. It
is a wrapper for the methods described in .
The goal of the wrapper is to simplify downloading and running of the kallisto
 and bustools  programs. It was inspired by Sten Linnarsson’s loompy
fromfq command (http://linnarssonlab.org/loompy/kallisto/index.html)
The kb program consists of two parts:
The `kb ref` command builds or downloads a species-specific index for
pseudoalignment of reads. This command must be run prior to `kb count`, and it
runs the `kallisto index` .
The `kb count` command runs the kallisto  and bustools  programs. It can
be used for pre-processing of data from a variety of single-cell RNA-seq
technologies, and for a number of different workflows (e.g. production of gene
count matrices, RNA velocity analyses, etc.). The output can be saved in a
variety of formats including mix and loom. Examples are provided below.
Examples are available at: https://www.kallistobus.tools/tutorials
 Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016). Near-optimal
probabilistic RNA-seq quantification. Nature biotechnology, 34(5), 525.
 Melsted, P., Booeshaghi, A. S., Liu, L., Gao, F., Lu, L., Min, K. H., da
Veiga Beltrame, E., Hjorleifsson, K. E., Gehring, J., & Pachter, L. (2021).
Modular and efficient pre-processing of single-cell RNA-seq. Nature