AptaMat is a simple script which aims to measure differences between DNA or RNA secondary structures.

Last update: Nov 03, 2022

Overview

AptaMAT

Purpose

AptaMat is a simple script which aims to measure differences between DNA or RNA secondary structures. The method is based on the comparison of the matrices representing the two secondary structures to analyze, assimilable to dotplots. The dot-bracket notation of the structure is converted in a half binary matrix showing width equal to structure's length. Each matrix case (i,j) is filled with '1' if the nucleotide in position i is paired with the nucleotide in position j, with '0' otherwise.

The differences between matrices is calculated by applying Manhattan distance on each point in the template matrix against all the points from the compared matrix. This calculation is repeated between compared matrix and template matrix to handle all the differences. Both calculation are then sum up and divided by the sum of all the points in both matrices.

Dependencies

AptaMat have been written in Python 3.8+

Two Python modules are needed :

NumPy
scipy

These can be installed by typing in the command prompt either :

./setup

pip install numpy
pip install scipy

Use of Anaconda is highly recommended.

Usage

AptaMat is a flexible Python script which can take several arguments:

structures followed by secondary structures written in dotbracket format
files followed by path to formatted files containing one, or several secondary structures in dotbracket format

Both structures and files are independent functions in the script and cannot be called at the same time.

usage: AptaMAT.py [-h] [-structures STRUCTURES [STRUCTURES ...]] [-files FILES [FILES ...]]

The structures argument must be a string formatted secondary structures. The first input structure is the template structure for the comparison. The following input are the compared structures. There are no input limitations. Quotes are necessary.

usage: AptaMat.py structures [-h] "struct_1" "struct_2" ["struct_n" ...]

The files argument must be a formatted file. Multiple files can be parsed. The first structure encountered during the parsing is used as the template structure. The others are the compared structures.

usage: AptaMat.py -files [-h] struct_file_1 [struct_file_n ...]

The input must be a text file, containing at least secondary structures, and accept additional information such as Title, Sequence or Structure index. If several files are provided, the function parses the files one by one and always takes the first structure encountered as the template structure. Files must be formatted as follows:

>5HRU
TCGATTGGATTGTGCCGGAAGTGCTGGCTCGA
--Template--
((((.........(((((.....)))))))))
--Compared--
.........(((.(((((.....))))).)))

Examples

structures function

First introducing a simple example with 2 structures:

AptaMat : 0.08 ">

$ AptaMat.py -structures "(((...)))" "((.....))"
 (((...)))
 ((.....))
> AptaMat : 0.08

Then, it is possible to input several structures:

AptaMat : 0.08 (((...))) .(.....). > AptaMat : 0.2 (((...))) (.......) > AptaMat : 0.3 ">

$ AptaMat.py -structures "(((...)))" "((.....))" ".(.....)." "(.......)"
 (((...)))
 ((.....))
> AptaMat : 0.08

 (((...)))
 .(.....).
> AptaMat : 0.2

 (((...)))
 (.......)
> AptaMat : 0.3

files function

Taking the above file example:

$ AptaMat.py -files example.fa
5HRU
Template - Compared
 ((((.........(((((.....)))))))))
 .........(((.(((((.....))))).)))
> AptaMat : 0.1134453781512605

Note

Compared structures need to have the same length as the Template structure.

For the moment, no features have been included to check whether the base pair is able to exist or not, according to literature. You must be careful about the sequence input and the base pairing associate.

The script accepts the extended dotbracket notation useful to compare pseudoknots or Tetrad. However, the resulting distance might not be accurate.

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Comments

Allow comparison with not folded secondary structure
User may want to perform quantitative analysis and attribute distance to non folded oligonucleotides against folded anyway for example in pipeline. Different solution can be considered:

Give a default distance value to unfolded vs folded structure (worst solution)

Distance must be equal to the maximum number of base pair observable : len(structrure)//2. Several issues could arise from this:

How to manage with enhancement #7 ? Take the largest ? Shortest ?

It would give abnormally high distance value and will remains constistent even though different structure folding are compared to the same unfolded structure. Considering our main advantage over others algorithm, failed to rank at this point is not good.

Assign Manhattan Distance for each point in matrix ( the one showing folding) the farthest theoretical + 1 in the structure. This may give a large distance between the two structures no matter the size and the + 1 prevent an equality one distance with an actually folded structure showing the same coordinate than the farthest theoretical point. Moreover, we can obtain different score when comparing different folding to the same unfolded structure.

enhancement
opened by GitHuBinet 0
Different length support and optimal alignment

Allow different structure length alignment. This would surely needs an optimal structure alignment to make AptaMat distance the lowest for a shared motif. Maybe we should consider the missing bases in the score calculation.
enhancement

opened by GitHuBinet 0
Is the algorithm time consuming ?
Considering the expected structure size (less than 100n) the calculation run quite fast. However, theoretically the calculation can takes time when the structure is larger with complexity around log(n^2). Possible improvement can be considered as this time complexity is linked with the double browsing of dotbracket input

[ ] Think about the possibility of improving this bracket search.

[ ] Study the .ct notation for ssNA secondary structure (see in ".ct notation" enhancement)

[x] #6

[ ] Test the algorithm with this new feature

question
opened by GEC-git 0
G-quadruplex/pseudoknot comprehension
Add features with G-quadruplex and pseudoknot comprehension. This kind of secondary structures requires extended dotbracket notation. https://www.tbi.univie.ac.at/RNA/ViennaRNA/doc/html/rna_structure_notations.html

The '([{<' & string.ascii_uppercase is already included but some doubt remain about the comparison accuracy because no test have been done on this kind of secondary structure

[ ] Perform some try on Q-quadruplex & pseudoknots and conclude about comparison reliability. /!\ The complexity comes from the G-quadruplex structures. The tetrad can form base pair in many different way and some secondary structure notation can be similar. Here is an exemple of case with the same interacting Guanine GGTTGGTGTGGTTGG ([..[)...(]..]) ((..)(...)(..))

[x] #5

enhancement invalid
opened by GEC-git 0

Releases(v0.9-pre-release)

v0.9-pre-release(Oct 28, 2022)
Pre-release content

https://github.com/GEC-git/AptaMat

Create LICENSE by @GEC-git in https://github.com/GEC-git/AptaMat/pull/2

main script AptaMat.py

README.MD edited and published

Beta AptaMat logo edited and published

Contributors

@GEC-git contributed in https://github.com/GEC-git/AptaMat

@GitHuBinet contributed in https://github.com/GEC-git/AptaMat

Full Changelog: https://github.com/GEC-git/AptaMat/commits/v0.9-pre-release
Source code(tar.gz)
Source code(zip)

Owner

GEC UTC

We are the "Genie Enzymatique et Cellulaire" CNRS UMR 7025 research unit.

GitHub Repository

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