kb-python is a python package for processing single-cell RNA-sequencing. It wraps the kallisto | bustools single-cell RNA-seq command line tools in order to unify multiple processing workflows.
kb-python was developed by Kyung Hoi (Joseph) Min and A. Sina Booeshaghi while in Lior Pachter's lab at Caltech. If you use kb-python in a publication please cite*:
Melsted, P., Booeshaghi, A.S., et al.
Modular, efficient and constant-memory single-cell RNA-seq preprocessing.
Nat Biotechnol 39, 813–818 (2021).
https://doi.org/10.1038/s41587-021-00870-2
The latest release can be installed with
pip install kb-pythonThe development version can be installed with
pip install git+https://github.com/pachterlab/kb_pythonThere are no prerequisite packages to install. The kallisto and bustools binaries are included with the package.
kb consists of four subcommands
$ kb
usage: kb [-h] [--list] <CMD> ...
positional arguments:
<CMD>
info Display package and citation information
compile Compile `kallisto` and `bustools` binaries from source
ref Build a kallisto index and transcript-to-gene mapping
count Generate count matrices from a set of single-cell FASTQ filesThe kb ref command takes in a species annotation file (GTF) and associated genome (FASTA) and builds a species-specific index for pseudoalignment of reads. This must be run before kb count. Internally, kb ref extracts the coding regions from the GTF and builds a transcriptome FASTA that is then indexed with kallisto index.
kb ref -i index.idx -g t2g.txt -f1 transcriptome.fa <GENOME> <GENOME_ANNOTATION><GENOME>refers to a genome file (FASTA).<GENOME_ANNOTATION>refers to a genome annotation file (GTF)- Note: The latest genome annotation and genome file for every species on ensembl can be found with the
ggetcommand-line tool.
Prebuilt indices are available at https://github.com/pachterlab/kallisto-transcriptome-indices
# Index the transcriptome from genome FASTA (genome.fa.gz) and GTF (annotation.gtf.gz)
$ kb ref -i index.idx -g t2g.txt -f1 transcriptome.fa genome.fa.gz annotation.gtf.gz
# An example for downloading a prebuilt reference for mouse
$ kb ref -d mouse -i index.idx -g t2g.txtThe kb count command takes in the pseudoalignment index (built with kb ref) and sequencing reads generated by a sequencing machine to generate a count matrix. Internally, kb count runs numerous kallisto and bustools commands comprising a single-cell workflow for the specified technology that generated the sequencing reads.
kb count -i index.idx -g t2g.txt -o out/ -x <TECHNOLOGY> <FASTQ FILE[s]><TECHNOLOGY>refers to the assay that generated the sequencing reads.- For a list of supported assays run
kb --list
- For a list of supported assays run
<FASTQ FILE[s]>refers to the a list of FASTQ files generated- Different assays will have a different number of FASTQ files
- Different assays will place the different features in different FASTQ files
- For example, sequencing a 10xv3 library on a NextSeq Illumina sequencer usually results in two FASTQ files.
- The
R1.fastq.gzfile (colloquially called "read 1") contains a 16 basepair cell barcode and a 12 basepair unique molecular identifier (UMI). - The
R2.fastq.gzfile (colloquially called "read 2") contains the cDNA associated with the cell barcode-UMI pair in read 1.
# Quantify 10xv3 reads read1.fastq.gz and read2.fastq.gz
$ kb count -i index.idx -g t2g.txt -o out/ -x 10xv3 read1.fastq.gz read2.fastq.gzThe kb info command prints out package information including the version of kb-python, kallisto, and bustools along with their installation location.
$ kb info
kb_python 0.28.0 ...
kallisto: 0.50.1 ...
bustools: 0.43.1 ...
...The kb compile command grabs the latest kallisto and bustools source and compiles the binaries. Note: this is not required to run kb-python.
kb-python facilitates fast and uniform pre-processing of single-cell sequencing data to answer relevant research questions.
$ pip install kb-python gget ffq
# Goal: quantify publicly available scRNAseq data
$ kb ref -i index.idx -g t2g.txt -f1 transcriptome.fa $(gget ref --ftp -w dna,gtf homo_sapiens)
$ kb count -i index.idx -g t2g.txt -x 10xv3 -o out $(ffq --ftp SRR10668798 | jq -r '.[] | .url' | tr '\n' ' ')
# -> count matrix in out/ folder
# Goal: quantify 10xv2 feature barcode data, feature_barcodes.txt is a tab-delimited file
# containing barcode_sequence<tab>barcode_name
$ kb ref -i index.idx -g f2g.txt -f1 features.fa --workflow kite feature_barcodes.txt
$ kb count -i index.idx -g f2b.txt -x 10xv2 -o out/ --workflow kite --h5ad R1.fastq.gz R2.fastq.gz
# -> count matrix in out/ folderSubmitted by @sbooeshaghi.
Do you have a cool use case for kb-python? Submit a PR (including the goal, code snippet, and your username) so that we can feature it here.
For a list of tutorials that use kb-python please see https://www.kallistobus.tools/.
Developer documentation is hosted on Read the Docs.
Thank you for wanting to improve kb-python! If you have believe you've found a bug, please submit an issue.
If you have a new feature you'd like to add to kb-python please create a pull request. Pull requests should contain a message detailing the exact changes made, the reasons for the change, and tests that check for the correctness of those changes.
If you use kb-python in a publication, please cite the following papers:
kb-python & kallisto and/or bustools
@article{sullivan2023kallisto,
title={kallisto, bustools, and kb-python for quantifying bulk, single-cell, and single-nucleus RNA-seq},
author={Sullivan, Delaney K and Min, Kyung Hoi and Hj{\"o}rleifsson, Kristj{\'a}n Eldj{\'a}rn and Luebbert, Laura and Holley, Guillaume and Moses, Lambda and Gustafsson, Johan and Bray, Nicolas L and Pimentel, Harold and Booeshaghi, A Sina and others},
journal={bioRxiv},
pages={2023--11},
year={2023},
publisher={Cold Spring Harbor Laboratory}
}
bustools
@article{melsted2021modular,
title={\href{https://doi.org/10.1038/s41587-021-00870-2}{Modular, efficient and constant-memory single-cell RNA-seq preprocessing}},
author={Melsted, P{\'a}ll and Booeshaghi, A. Sina and Liu, Lauren and Gao, Fan and Lu, Lambda and Min, Kyung Hoi Joseph and da Veiga Beltrame, Eduardo and Hj{\"o}rleifsson, Kristj{\'a}n Eldj{\'a}rn and Gehring, Jase and Pachter, Lior},
author+an={1=first;2=first,highlight},
journal={Nature biotechnology},
year={2021},
month={4},
day={1},
doi={https://doi.org/10.1038/s41587-021-00870-2}
}kallisto
@article{bray2016near,
title={Near-optimal probabilistic RNA-seq quantification},
author={Bray, Nicolas L and Pimentel, Harold and Melsted, P{\'a}ll and Pachter, Lior},
journal={Nature biotechnology},
volume={34},
number={5},
pages={525--527},
year={2016},
publisher={Nature Publishing Group}
}kITE
@article{booeshaghi2024quantifying,
title={Quantifying orthogonal barcodes for sequence census assays},
author={Booeshaghi, A Sina and Min, Kyung Hoi and Gehring, Jase and Pachter, Lior},
journal={Bioinformatics Advances},
volume={4},
number={1},
pages={vbad181},
year={2024},
publisher={Oxford University Press}
}BUS format
@article{melsted2019barcode,
title={The barcode, UMI, set format and BUStools},
author={Melsted, P{\'a}ll and Ntranos, Vasilis and Pachter, Lior},
journal={Bioinformatics},
volume={35},
number={21},
pages={4472--4473},
year={2019},
publisher={Oxford University Press}
}kb-python was inspired by Sten Linnarsson’s loompy fromfq command (http://linnarssonlab.org/loompy/kallisto/index.html)
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