QMPrimers

A tool to select the best primers to perform quantitative metabarcoding studies. It aims to be used by all kind of users as it comes with a graphical interface. It has two main stages:

• MATCHING: Given a genome file and a primer pairs file, it matches every genome sequence with every primer pair and generates a template containing the best matches found as well as additional information. A part from the template, it also generates a table containing the genomes sequences and primer pairs that didn't match and another file containing statistics. If the output directory was "output", the program would generate respectively output_positive.csv, output_negative.csv and output_stats.txt.

• SIMULATION: Given the template (output_positive.csv) generated by the MATCHING (or any other program as long as it has the required format), performs a simulated PCR-amplification (or in silico PCR) of a random sample of genomes (or species); the effciency of the PCR of each species in the mixture is mediated by its number of template-primer mismatches. The rationale for the model is taken from Piñol et al. (2019). In short:

  1. We generate a random sample of S species from the pool P of species.
  2. We generate the initial (before PCR) abundance of each of the S species in the sample using a geometric distribution of parameter k (equation 3 of Piñol et al., 2019).
  3. We estimate the relative amplification efficiency of each species in the sample using the parameter beta and the number of primer-template mismatches of each species according equation 4 of Piñol et al. (2019).
  4. We calculate the relative final (after PCR) DNA concentration of each species in the mixture using equation 2 of Piñol et al. (2019).
  5. Finally, we calculate the Pearson correlation coefficient between the initial and the final relative abundance of species (pre and post-PCR).
  6. The above steps are repeated N times with a fresh random set of species.

  P is the numer of total genomes for which there was a positive matching in the template file (output_positive.csv). S, k, beta, and N are given by the user.

  The results are summarized in two files. (1) raw results containing a table with the correlation coefficient for each sample and each primer pair; (2) a summary containing the basic statistics of the simulation for each primer pair.

WARNING: This program is still in development, so expect bugs and crashes. Its use is still not recommended!

Installation

QMPrimers can e executed as a Python script or as a one-file executable.

As a Python script

To run it as a Python script it is mandatory to use Python3.x, as well as to install the following dependencies.

Dependencies:

• Biopython
• Numpy
• Pandas

Once fullfilled the requirements, execute the program.

As an executable

Download the program via one of the links below, dependiing on your platform:

• Windows 10
• insert link to Linux executable
• insert link to MacOS executable

Once downloaded the program, execute it.

Wiki

First steps

Run as a Python script

• To execute the program in GUI mode, just type in a terminal:

python QMPrimers.py
#or python3 or python3.x, where X depends on your python version

• To execute the program in command line mode, type in a terminal:

python QMPrimers.py <paramters....>

Run as an executable

• To execute the program (in GUI mode) double click the program file.

• To execute the program in command line mode, type in a terminal:

./QMPrimers --help

How to use the program

Matching

Everything related to the Matching is explained here.

Simulation

Everything related to the Simulation is explained here.

Development

Feel free to colaborate and to send me a message for any doubts. If you have never contributed to any respository, I recommend to take a look on this website

To know what is going on under the hood, you can check this yet-unfinished page