A pacakge which provides various ways to analyze NGS data from phage display campaigns
Project description
Welcome to ExpoSeq
ExpoSeq is a powerful pipeline for processing and analyzing FASTQ files from sequencing phage Display panning samples.It utilizes MiXCR to align and assemble the data which you can subsequently analyze in multiple plots. The pipeline focuses on analysing the identity between samples but also applies various clustering techniques to analyse the relation between the sequences. Besides, you can add binding data to relate the clusters to affinity.
Installation
Open a virtual environment and type pip install ExpoSeq
. Ensure that you have python > 3.11 installed.
To get started, please download and follow the instructions for MiXCR under the following link: https://docs.milaboratories.com/mixcr/getting-started/installation/ You can also only use the test version without installing it.
Importing the Plotting Tool
To access the plotting tool, you will need to import it into your console by running the following command:
from ExpoSeq.pipeline import PlotManager
If you want to use the fast and easy version you can ignore the rest of the instructions and just import in your Console:
import ExpoSeq.run
Using the PlotManager
The PlotManager is the main interface for creating various plots using your FASTQ data. You can create an instance of the PlotManager by running the following command:
plot = PlotManager()
To use the PlotManager to create plots, you will need to upload your FASTQ data to the pipeline. This will automatically happen as soon as you have called the PlotManager. In the following you can obtain an insight in the worklow of the pipeline after the initial call. There, the blue boxes indicate your input, gray are optional inputs while black and red are processing steps and output, respectively.
If you just want to test the pipeline and see its functions you can call: plot = PlotManager(test_version = True)
Once you have called the test version or have finished the data processing, you can use the PlotManager to create a variety of plots, such as an identity plot based on the jaccard similarity. Here is an example of how to create this type of plot:
plot.jaccard()
If you want to change the style of the plot you can use the PlotManager. If you called it plot
you can do for instance the following: plot.style.title_xaxis("your_title")
If you want to implement further plot change you can also refer to the matplotlib.pyplot library and change it in the same way as following:
import matplotlib.pyplot as plt
plt.xlabel("your_title")
If you would like to have details about the inputs and functions of the PlotManager call: help(plot)
. You can also call help(plot.jaccard)
Processing on a server with multithreading
If you wish to process your data on a server to utilize multithreading, or to process on a screen in the background, use the scripts located in the bash_processing
folder.
Start by copying the folder and the directory with the mixcr.jar
file to the corresponding directory. You can use the run_mixcr.sh
script to generate a sequencing report, which is the input of the pipeline. The script requires certain inputs and can accept optional ones as well, such as the number of threads you wish to use. The inputs are listed below:
Required Inputs
--fastq_directory
: Directory path to your fastq files.--path_to_mixcr
: Filepath (ending with .jar) to mixcr.
Optional Inputs
--save_dir
: Directory where you would like to store the sequencing report (default is the working directory).--paired_end_sequencing
: Boolean indicating whether you have paired end sequencing data or not (default isFalse
).--threads
: Number of threads you would like to utilize (default is1
).--method
: Mixcr method to assemble and align your sequences (default ismilab-human-tcr-dna-multiplex-cdr3
).--trim_div_by
: Trims all sequences that are divisible by the integer you input (default is3
).--trim_min_count
: Trims all sequences that are shorter than the given integer (default is3
).
References
[1] Dmitriy A. Bolotin, Stanislav Poslavsky, Igor Mitrophanov, Mikhail Shugay, Ilgar Z. Mamedov, Ekaterina V. Putintseva, and Dmitriy M. Chudakov. "MiXCR: software for comprehensive adaptive immunity profiling." Nature methods 12, no. 5 (2015): 380-381.
[2] Dmitriy A. Bolotin, Stanislav Poslavsky, Alexey N. Davydov, Felix E. Frenkel, Lorenzo Fanchi, Olga I. Zolotareva, Saskia Hemmers, Ekaterina V. Putintseva, Anna S. Obraztsova, Mikhail Shugay, Ravshan I. Ataullakhanov, Alexander Y. Rudensky, Ton N. Schumacher & Dmitriy M. Chudakov. "Antigen receptor repertoire profiling from RNA-seq data." Nature Biotechnology 35, 908–911 (2017)
[3] (1, 2) Tareen A, Kinney JB (2019) Logomaker: beautiful sequence logos in Python. Bioinformatics btz921. bioRxiv doi:10.1101/635029.
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