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Pipeline for analyzing results of in-vivo nanoparticle experiments from the Dahlman Lab at Georgia Tech.
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Jack Feldman
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README.md
barcode_count_widget.py
barcodes.csv
requirements.txt

README.md

barcode_count

Pipeline for analyzing results of in-vivo nanoparticle experiments from the Dahlman Lab at Georgia Tech.

How to run

  1. Clone this repository into your home directory

  2. Open a terminal window

  3. Run the following command: python ~/barcode_count/barcode_count_widget.py

Input

File path to fastq.gz files

Output directory location

Barcode list (csv file, one 8-nt barcode per line)

Output

rawcounts.csv

Raw counts of each barcode per read file

normcounts.csv

Barcode counts normalized to input

cell_type_variance.csv

Average and standard deviation of normalized barcode counts between replicates of the same cell type

normalized_compiled.csv

Same as cell_type_variance.csv except replicates are included next to their aggregate metrics

pcr_bias.csv

Base percentages for each position in the three randomized pcr regions

Rplots.pdf

Graphs generated by the normalization.R script

logfile_YYYYMMDD.log

Counts where there was an 'N' in the barcode region and number of instances where no barcode was found in the expected region for each fastq file

Notes

The following probe binding site was used to find barcodes in each read: 'CCTGCTAGTCCACGTCCATGTCCACC'. In instances where the probe region was not found, the read was skipped. This was likely due to an 'N' being assigned to a position in the probe binding site because a base could not be confidently called. Similarly, if there was an 'N' in the barcode region, this read was skipped as well.

This pipeline requires Python version 3.7.x. Important note: MacOS Mojave version 10.14.6 and python 3.7.3 will cause Tkinter to crash the OS. This is a known Mac bug. If you are using MacOS 10.14.6, downgrade your python version to 3.7.0.

Author: Jack Feldman

Dahlman Lab Georgia Tech

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