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Tools for feature barcoding analysis

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fba

Tools for feature barcoding analysis

Jialei Duan, Gary Hon. FBA: feature barcoding analysis for single cell RNA-Seq. Bioinformatics. 2021 May 17:btab375. doi: 10.1093/bioinformatics/btab375. Epub ahead of print. 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 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).

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