spacer_count is a versatile counter for spacers or barcodes from whole-plasimid (or similar type) sequencing outputs. It uses regex and pairwise alignment for sapcer detection, therefore it reliably count spacers appearing in any position and orientation in the read, and tolerate error (substitution and in-del). Thus, it can be used directly on vairous outputs from whole-plasmid sequencing (long-reads), staggered amplicon, or simply reads without trimming.

The core alignment and parallelization code is now written in Rust for optimal performance.

Quick start

To install, simply run: pip install spacer_count

Input format

The csv file needed for defining known spacer in spacer_count, is the same as the sgRNA file used by MAGeCK. Briefly, a no header, three-column csv file is used as input.

s_10007,TGTTCACAGTATAGTTTGCC,CCNA1
s_10008,TTCTCCCTAATTGCTTGCTG,CCNA1
s_10027,ACATGTTGCTTCCCCTTGCA,CCNC

If no input files are provided, all the spacers will be counted in the unknown output file.

Command line interface

spacer_count's primary interface is commend line. After installation, the package is accessible via spacer-count.

Example:

spacer-count --o data/test --spacer-info-csv data/spacer_info.csv --flanking NGATG-ATGTGGTC -t 4 data/*.fastq

More argument help is accissbile via spacer-count --help

usage: spacer_count [-h] --flanking FLANKING [-t THREADS] [--first-n FIRST_N] [-o OUTPUT] [--spacer-flex SPACER_FLEX]
                    [--spacer-info-csv SPACER_INFO_CSV] [--spacer-length SPACER_LENGTH]
                    path [path ...]

Count spacers in fastq files

positional arguments:
  path                  File path(s) to analyze. Can be a single file, or a glob pattern (e.g., 'data/*.fastq'). For 
                        multiple files, separate them with space (e.g.,'data/file1.fastq data/file2.fastq').

options:
  -h, --help            show this help message and exit
  --flanking FLANKING   Flanking (left and right) sequence to consider for spacer counting, e.g., 'NGATG-ATGTGGTC'
  -t THREADS, --threads THREADS
                        Number of threads to use for alignment (default: 1)
  --first-n FIRST_N     Only process the first N sequences in each file (default: all, if not specified)
  -o OUTPUT, --output OUTPUT
                        Base name for output files (default: 'count_table')
  --spacer-flex SPACER_FLEX
                        Allowable flexibility in spacer length in the extracting step (default: 1).
  --spacer-info-csv SPACER_INFO_CSV
                        Path to CSV file containing spacer information (default: None)
  --spacer-length SPACER_LENGTH
                        Expected spacer length. Will be ignored if spacer-info-csv is provided (default: None)

Python interface

Access directely from python is also provide for easier post-processing. The output are pandas.DataFrame.

Example:

from spacer_count.SpacerCounter import SpacerCounter

if __name__ == "__main__":
    counter = SpacerCounter(['NGATG', 'ATGTGGTC'], spacer_size_flex=1, spacer_info_csv='data/spacer_info.csv')
    known_df, unknown_df = counter.count_spacers('data/lr_test.fastq', basename=None, threads=8)
    # provide basename to enable file output.

    print(known_df.head())