# kb-python
[](https://pypi.org/project/kb-python/0.29.1/)
[](https://codecov.io/gh/pachterlab/kb_python)
[](https://pypi.org/project/kb-python/)
[](https://kb-python.readthedocs.io/en/latest/?badge=latest)
[](LICENSE)
`kb-python` is a python package for processing single-cell RNA-sequencing. It wraps the [`kallisto` | `bustools`](https://www.kallistobus.tools) single-cell RNA-seq command line tools in order to unify multiple processing workflows.
`kb-python` was first developed by [Kyung Hoi (Joseph) Min](https://twitter.com/lioscro) and [A. Sina Booeshaghi](https://twitter.com/sinabooeshaghi) while in [Lior Pachter](https://twitter.com/lpachter)'s lab at Caltech. If you use `kb-python` in a publication please [cite*](#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
```
## Installation
The latest release can be installed with
```bash
pip install kb-python
```
The development version can be installed with
```bash
pip install git+https://github.com/pachterlab/kb_python
```
There are no prerequisite packages to install. The `kallisto` and `bustools` binaries are included with the package.
## Usage
`kb` consists of five subcommands
```bash
$ 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 files
extract Extract reads that were pseudoaligned to specific genes/transcripts (or extract all reads that were / were not pseudoaligned)
```
### `kb ref`: generate a pseudoalignment index
The `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`.
```bash
kb ref -i index.idx -g t2g.txt -f1 transcriptome.fa <GENOME> <GENOME_ANNOTATION>
```
- `<GENOME>` refers to a genome file (FASTA).
- For example, the zebrafish genome is hosted by [ensembl](https://uswest.ensembl.org/Danio_rerio/Info/Index) and can be downloaded [here](http://ftp.ensembl.org/pub/release-107/fasta/danio_rerio/dna/Danio_rerio.GRCz11.dna.primary_assembly.fa.gz)
- `<GENOME_ANNOTATION>` refers to a genome annotation file (GTF)
- For example, the zebrafish genome annotation file is hosted by [ensembl](https://uswest.ensembl.org/Danio_rerio/Info/Index) and can be downloaded [here](http://ftp.ensembl.org/pub/release-107/gtf/danio_rerio/Danio_rerio.GRCz11.107.gtf.gz)
- **Note:** The latest genome annotation and genome file for every species on ensembl can be found with the [`gget`](https://github.com/pachterlab/gget) command-line tool.
Prebuilt indices are available at https://github.com/pachterlab/kallisto-transcriptome-indices
#### Examples
```bash
# 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.txt
```
---
### `kb count`: pseudoalign and count reads
The `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`](https://github.com/pachterlab/kallisto) and [`bustools`](https://github.com/BUStools/bustools/) commands comprising a single-cell workflow for the specified technology that generated the sequencing reads.
```bash
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`
- `<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.gz` file (colloquially called "read 1") contains a 16 basepair cell barcode and a 12 basepair unique molecular identifier (UMI).
- The `R2.fastq.gz` file (colloquially called "read 2") contains the cDNA associated with the cell barcode-UMI pair in read 1.
#### Examples
```bash
# 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.gz
```
---
### `kb info`: display package and citation information
The `kb info` command prints out package information including the version of `kb-python`, `kallisto`, and `bustools` along with their installation location.
```bash
$ kb info
kb_python 0.29.1 ...
kallisto: 0.51.1 ...
bustools: 0.44.1 ...
...
```
---
### `kb compile`: compile `kallisto` and `bustools` binaries from source
The `kb compile` command grabs the latest `kallisto` and `bustools` source and compiles the binaries. **Note**: this is not required to run `kb-python`.
## Use cases
`kb-python` facilitates fast and uniform pre-processing of single-cell sequencing data to answer relevant research questions.
```bash
$ 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/ folder
```
Submitted by [@sbooeshaghi](https://github.com/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.
## Tutorials
For a list of tutorials that use `kb-python` please see [https://www.kallistobus.tools/](https://www.kallistobus.tools/).
## Documentation
Developer documentation is hosted on [Read the Docs](https://kb-python.readthedocs.io/en/latest/).
## Contributing
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.
# Cite
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`
```tex
@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`
```tex
@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`
```tex
@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
```tex
@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)
Raw data
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"description": "# kb-python\n\n[](https://pypi.org/project/kb-python/0.29.1/)\n\n\n[](https://codecov.io/gh/pachterlab/kb_python)\n[](https://pypi.org/project/kb-python/)\n[](https://kb-python.readthedocs.io/en/latest/?badge=latest)\n[](LICENSE)\n\n`kb-python` is a python package for processing single-cell RNA-sequencing. It wraps the [`kallisto` | `bustools`](https://www.kallistobus.tools) single-cell RNA-seq command line tools in order to unify multiple processing workflows. \n\n`kb-python` was first developed by [Kyung Hoi (Joseph) Min](https://twitter.com/lioscro) and [A. Sina Booeshaghi](https://twitter.com/sinabooeshaghi) while in [Lior Pachter](https://twitter.com/lpachter)'s lab at Caltech. If you use `kb-python` in a publication please [cite*](#cite):\n```\nMelsted, P., Booeshaghi, A.S., et al. \nModular, efficient and constant-memory single-cell RNA-seq preprocessing. \nNat Biotechnol 39, 813\u2013818 (2021). \nhttps://doi.org/10.1038/s41587-021-00870-2\n```\n\n## Installation\nThe latest release can be installed with\n\n```bash\npip install kb-python\n```\n\nThe development version can be installed with\n```bash\npip install git+https://github.com/pachterlab/kb_python\n```\n\nThere are no prerequisite packages to install. The `kallisto` and `bustools` binaries are included with the package.\n\n## Usage\n\n`kb` consists of five subcommands\n```bash\n$ kb\nusage: kb [-h] [--list] <CMD> ...\npositional arguments:\n <CMD>\n info Display package and citation information\n compile Compile `kallisto` and `bustools` binaries from source\n ref Build a kallisto index and transcript-to-gene mapping\n count Generate count matrices from a set of single-cell FASTQ files\n extract Extract reads that were pseudoaligned to specific genes/transcripts (or extract all reads that were / were not pseudoaligned)\n```\n\n### `kb ref`: generate a pseudoalignment index\n\nThe `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`.\n\n```bash\nkb ref -i index.idx -g t2g.txt -f1 transcriptome.fa <GENOME> <GENOME_ANNOTATION>\n```\n- `<GENOME>` refers to a genome file (FASTA).\n\t- For example, the zebrafish genome is hosted by [ensembl](https://uswest.ensembl.org/Danio_rerio/Info/Index) and can be downloaded [here](http://ftp.ensembl.org/pub/release-107/fasta/danio_rerio/dna/Danio_rerio.GRCz11.dna.primary_assembly.fa.gz)\n- `<GENOME_ANNOTATION>` refers to a genome annotation file (GTF)\n\t- For example, the zebrafish genome annotation file is hosted by [ensembl](https://uswest.ensembl.org/Danio_rerio/Info/Index) and can be downloaded [here](http://ftp.ensembl.org/pub/release-107/gtf/danio_rerio/Danio_rerio.GRCz11.107.gtf.gz)\n- **Note:** The latest genome annotation and genome file for every species on ensembl can be found with the [`gget`](https://github.com/pachterlab/gget) command-line tool.\n\nPrebuilt indices are available at https://github.com/pachterlab/kallisto-transcriptome-indices\n\n#### Examples\n```bash\n# Index the transcriptome from genome FASTA (genome.fa.gz) and GTF (annotation.gtf.gz)\n$ kb ref -i index.idx -g t2g.txt -f1 transcriptome.fa genome.fa.gz annotation.gtf.gz\n# An example for downloading a prebuilt reference for mouse\n$ kb ref -d mouse -i index.idx -g t2g.txt\n```\n---\n### `kb count`: pseudoalign and count reads\n\nThe `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`](https://github.com/pachterlab/kallisto) and [`bustools`](https://github.com/BUStools/bustools/) commands comprising a single-cell workflow for the specified technology that generated the sequencing reads.\n\n```bash\nkb count -i index.idx -g t2g.txt -o out/ -x <TECHNOLOGY> <FASTQ FILE[s]>\n```\n- `<TECHNOLOGY>` refers to the assay that generated the sequencing reads.\n\t- For a list of supported assays run `kb --list`\n- `<FASTQ FILE[s]>` refers to the a list of FASTQ files generated \n\t- Different assays will have a different number of FASTQ files \n\t- Different assays will place the different features in different FASTQ files\n\t\t- For example, sequencing a 10xv3 library on a NextSeq Illumina sequencer usually results in two FASTQ files. \n\t\t- The `R1.fastq.gz` file (colloquially called \"read 1\") contains a 16 basepair cell barcode and a 12 basepair unique molecular identifier (UMI). \n\t\t- The `R2.fastq.gz` file (colloquially called \"read 2\") contains the cDNA associated with the cell barcode-UMI pair in read 1.\n\n#### Examples\n```bash\n# Quantify 10xv3 reads read1.fastq.gz and read2.fastq.gz\n$ kb count -i index.idx -g t2g.txt -o out/ -x 10xv3 read1.fastq.gz read2.fastq.gz\n```\n---\n### `kb info`: display package and citation information\n\nThe `kb info` command prints out package information including the version of `kb-python`, `kallisto`, and `bustools` along with their installation location.\n\n```bash\n$ kb info\nkb_python 0.29.1 ...\nkallisto: 0.51.1 ...\nbustools: 0.44.1 ...\n...\n```\n---\n### `kb compile`: compile `kallisto` and `bustools` binaries from source\nThe `kb compile` command grabs the latest `kallisto` and `bustools` source and compiles the binaries. **Note**: this is not required to run `kb-python`.\n\n## Use cases\n`kb-python` facilitates fast and uniform pre-processing of single-cell sequencing data to answer relevant research questions. \n```bash\n$ pip install kb-python gget ffq\n\n# Goal: quantify publicly available scRNAseq data\n$ kb ref -i index.idx -g t2g.txt -f1 transcriptome.fa $(gget ref --ftp -w dna,gtf homo_sapiens)\n$ kb count -i index.idx -g t2g.txt -x 10xv3 -o out $(ffq --ftp SRR10668798 | jq -r '.[] | .url' | tr '\\n' ' ')\n# -> count matrix in out/ folder\n\n# Goal: quantify 10xv2 feature barcode data, feature_barcodes.txt is a tab-delimited file\n# containing barcode_sequence<tab>barcode_name\n$ kb ref -i index.idx -g f2g.txt -f1 features.fa --workflow kite feature_barcodes.txt\n$ kb count -i index.idx -g f2b.txt -x 10xv2 -o out/ --workflow kite --h5ad R1.fastq.gz R2.fastq.gz\n# -> count matrix in out/ folder\n```\nSubmitted by [@sbooeshaghi](https://github.com/sbooeshaghi/).\n\nDo 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.\n\n## Tutorials\nFor a list of tutorials that use `kb-python` please see [https://www.kallistobus.tools/](https://www.kallistobus.tools/).\n\n## Documentation\nDeveloper documentation is hosted on [Read the Docs](https://kb-python.readthedocs.io/en/latest/).\n\n## Contributing\nThank you for wanting to improve `kb-python`! If you have believe you've found a bug, please submit an issue. \n\nIf 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.\n\n# Cite\nIf you use `kb-python` in a publication, please cite the following papers:\n\n`kb-python` & `kallisto` and/or `bustools`\n```\n@article{sullivan2023kallisto,\n title={kallisto, bustools, and kb-python for quantifying bulk, single-cell, and single-nucleus RNA-seq},\n 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},\n journal={bioRxiv},\n pages={2023--11},\n year={2023},\n publisher={Cold Spring Harbor Laboratory}\n}\n```\n\n`bustools` \n```tex\n@article{melsted2021modular,\n title={\\href{https://doi.org/10.1038/s41587-021-00870-2}{Modular, efficient and constant-memory single-cell RNA-seq preprocessing}},\n author={Melsted, P{\\'a}ll and Booeshaghi, A. 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JSON
