Skip to main content

PyPI Conda License GitHub Workflow Status (with branch) CircleCI Read the Docs Codecov GitHub Commits Since Latest Release (by date) Zenodo DOI


工欲善其事,必先利其器。—— 论语·卫灵公

fba

Tools for single-cell feature barcoding analysis

Jialei Duan, Gary C Hon, FBA: feature barcoding analysis for single cell RNA-Seq, Bioinformatics, Volume 37, Issue 22, 15 November 2021, Pages 4266–4268. DOI: https://doi.org/10.1093/bioinformatics/btab375. PMID: 33999185.


What is fba?

fba is a flexible and streamlined toolbox for quality control, quantification, demultiplexing of various feature barcoding assays. It can be applied to customized feature barcoding specifications, including different CRISPR constructs or targeted enriched transcripts. fba allows users to customize a wide range of parameters for the quantification and demultiplexing process. fba also has a user-friendly quality control module, which is helpful in troubleshooting feature barcoding experiments.


Installation

fba can be installed with pip:

pip install fba

Alternatively, you can install this package with conda:

conda install -c bioconda fba

Workflow Example


Usage

$ fba

usage: fba [-h]  ...

Tools for single-cell feature barcoding analysis

optional arguments:
  -h, --help        show this help message and exit

functions:

    extract         extract cell and feature barcodes
    map             map enriched transcripts
    filter          filter extracted barcodes
    count           count feature barcodes per cell
    demultiplex     demultiplex cells based on feature abundance
    qc              quality control of feature barcoding assay
    kallisto_wrapper
                    deploy kallisto/bustools for feature barcoding
                    quantification

  • extract: extract cell and feature barcodes from paired fastq files. For single cell assays, read 1 usually contains cell partitioning and UMI information, and read 2 contains feature information.
  • map: quantify enriched transcripts (through hybridization or PCR amplification) from parent single cell libraries. Read 1 contains cell partitioning and UMI information, and read 2 contains transcribed regions of enriched/targeted transcripts of interest. BWA (Li, H. 2013) or Bowtie2 (Langmead, B., et al. 2012) is used for read 2 alignment. The quantification (UMI deduplication) of enriched/targeted transcripts is powered by UMI-tools (Smith, T., et al. 2017).
  • filter: filter extracted cell and feature barcodes (output of extract or qc). Additional fragment filter/selection can be applied through -cb_seq and/or -fb_seq.
  • count: count UMIs per feature per cell (UMI deduplication), powered by UMI-tools (Smith, T., et al. 2017). Take the output of extract or filter as input.
  • demultiplex: demultiplex cells based on the abundance of features (matrix generated by count as input).
  • qc: generate diagnostic information. If -1 is omitted, bulk mode is enabled and only read 2 will be analyzed.
  • kallisto_wrapper: deploy kallisto/bustools for feature barcoding quantification (just a wrapper) (Bray, N.L., et al. 2016).

Metadata

Release files for fba 0.0.13

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for fba 0.0.13
File Size Uploaded
fba-0.0.13.tar.gz 44.8 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for fba 0.0.13
File Interpreter ABI Platform
fba-0.0.13-py3-none-any.whl Python 3 none any Details

Total release size: 88.0 kB

Release files / fba-0.0.13.tar.gz

Download URL fba-0.0.13.tar.gz
Size 44.8 kB
Tags Source
SHA-256 checksum
How to use checksums
5009ba60bc879e4b9bb46ad8d597b9077daf1e9d73379487ca9941ba155c5edf
BLAKE2b-256 checksum
How to use checksums
acbe6e78ff8c9628f45527c8fcd25faaafe00937a42d67a7b5da765ecec381ba
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.2 CPython/3.9.13

Release files / fba-0.0.13-py3-none-any.whl

Download URL fba-0.0.13-py3-none-any.whl
Size 43.2 kB
Tags Python 3
SHA-256 checksum
How to use checksums
7416805bc49e5e6e132c5b2fd9261f21282b212c1cb5aaef41ea0fbdb8c62194
BLAKE2b-256 checksum
How to use checksums
f7daa639ff75d1839741002c4bf9fb42fcdab9060bc641e80887bb7ef2c46bc1
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.2 CPython/3.9.13
Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page