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

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 :

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

./setup

or

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.

You might also like...
The Spark Challenge Student Check-In/Out Tracking Script

The Spark Challenge Student Check-In/Out Tracking Script This Python Script uses the Student ID Database to match the entries with the ID Card Swipe a

Python script to automate the plotting and analysis of percentage depth dose and dose profile simulations in TOPAS.

topas-create-graphs A script to automatically plot the results of a topas simulation Works for percentage depth dose (pdd) and dose profiles (dp). Dep

Flenser is a simple, minimal, automated exploratory data analysis tool.

Flenser Have you ever been handed a dataset you've never seen before? Flenser is a simple, minimal, automated exploratory data analysis tool. It runs

Datashredder is a simple data corruption engine written in python. You can corrupt anything text, images and video.
Datashredder is a simple data corruption engine written in python. You can corrupt anything text, images and video.

Datashredder is a simple data corruption engine written in python. You can corrupt anything text, images and video. You can chose the cha

WithPipe is a simple utility for functional piping in Python.

A utility for functional piping in Python that allows you to access any function in any scope as a partial.

Data Scientist in Simple Stock Analysis of PT Bukalapak.com Tbk for Long Term Investment
Data Scientist in Simple Stock Analysis of PT Bukalapak.com Tbk for Long Term Investment

Data Scientist in Simple Stock Analysis of PT Bukalapak.com Tbk for Long Term Investment Brief explanation of PT Bukalapak.com Tbk Bukalapak was found

My first Python project is a simple Mad Libs program.
My first Python project is a simple Mad Libs program.

Python CLI Mad Libs Game My first Python project is a simple Mad Libs program. Mad Libs is a phrasal template word game created by Leonard Stern and R

simple way to build the declarative and destributed data pipelines with python

unipipeline simple way to build the declarative and distributed data pipelines. Why you should use it Declarative strict config Scaffolding Fully type

Generates a simple report about the current Covid-19 cases and deaths in Malaysia

Generates a simple report about the current Covid-19 cases and deaths in Malaysia. Results are delay one day, data provided by the Ministry of Health Malaysia Covid-19 public data.

Comments
  • Allow comparison with not folded secondary structure

    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

    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 ?

    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

    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.
GEC UTC
Pipeline to convert a haploid assembly into diploid

HapDup (haplotype duplicator) is a pipeline to convert a haploid long read assembly into a dual diploid assembly. The reconstructed haplotypes

Mikhail Kolmogorov 50 Jan 05, 2023
Time ranges with python

timeranges Time ranges. Read the Docs Installation pip timeranges is available on pip: pip install timeranges GitHub You can also install the latest v

Micael Jarniac 2 Sep 01, 2022
Collections of pydantic models

pydantic-collections The pydantic-collections package provides BaseCollectionModel class that allows you to manipulate collections of pydantic models

Roman Snegirev 20 Dec 26, 2022
🌍 Create 3d-printable STLs from satellite elevation data 🌏

mapa 🌍 Create 3d-printable STLs from satellite elevation data Installation pip install mapa Usage mapa uses numpy and numba under the hood to crunch

Fabian Gebhart 13 Dec 15, 2022
Stream-Kafka-ELK-Stack - Weather data streaming using Apache Kafka and Elastic Stack.

Streaming Data Pipeline - Kafka + ELK Stack Streaming weather data using Apache Kafka and Elastic Stack. Data source: https://openweathermap.org/api O

Felipe Demenech Vasconcelos 2 Jan 20, 2022
A set of procedures that can realize covid19 virus detection based on blood.

A set of procedures that can realize covid19 virus detection based on blood.

Nuyoah-xlh 3 Mar 07, 2022
OpenARB is an open source program aiming to emulate a free market while encouraging players to participate in arbitrage in order to increase working capital.

Overview OpenARB is an open source program aiming to emulate a free market while encouraging players to participate in arbitrage in order to increase

Tom 3 Feb 12, 2022
Analyzing Covid-19 Outbreaks in Ontario

My group and I took Covid-19 outbreak statistics from ontario, and analyzed them to find different patterns and future predictions for the virus

Vishwaajeeth Kamalakkannan 0 Jan 20, 2022
A model checker for verifying properties in epistemic models

Epistemic Model Checker This is a model checker for verifying properties in epistemic models. The goal of the model checker is to check for Pluralisti

Thomas Träff 2 Dec 22, 2021
BIGDATA SIMULATION ONE PIECE WORLD CENSUS

ONE PIECE is a Japanese manga of great international success. The story turns inhabited in a fictional world, tells the adventures of a young man whose body gained rubber properties after accidentall

Maycon Cypriano 3 Jun 30, 2022
Mortgage-loan-prediction - Show how to perform advanced Analytics and Machine Learning in Python using a full complement of PyData utilities

Mortgage-loan-prediction - Show how to perform advanced Analytics and Machine Learning in Python using a full complement of PyData utilities. This is aimed at those looking to get into the field of D

Joachim 1 Dec 26, 2021
Generates a simple report about the current Covid-19 cases and deaths in Malaysia

Generates a simple report about the current Covid-19 cases and deaths in Malaysia. Results are delay one day, data provided by the Ministry of Health Malaysia Covid-19 public data.

Yap Khai Chuen 7 Dec 15, 2022
Python package for analyzing sensor-collected human motion data

Python package for analyzing sensor-collected human motion data

Simon Ho 71 Nov 05, 2022
[CVPR2022] This repository contains code for the paper "Nested Collaborative Learning for Long-Tailed Visual Recognition", published at CVPR 2022

Nested Collaborative Learning for Long-Tailed Visual Recognition This repository is the official PyTorch implementation of the paper in CVPR 2022: Nes

Jun Li 65 Dec 09, 2022
Deep universal probabilistic programming with Python and PyTorch

Getting Started | Documentation | Community | Contributing Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notab

7.7k Dec 30, 2022
Data Intelligence Applications - Online Product Advertising and Pricing with Context Generation

Data Intelligence Applications - Online Product Advertising and Pricing with Context Generation Overview Consider the scenario in which advertisement

Manuel Bressan 2 Nov 18, 2021
The OHSDI OMOP Common Data Model allows for the systematic analysis of healthcare observational databases.

The OHSDI OMOP Common Data Model allows for the systematic analysis of healthcare observational databases.

Bell Eapen 14 Jan 02, 2023
A fast, flexible, and performant feature selection package for python.

linselect A fast, flexible, and performant feature selection package for python. Package in a nutshell It's built on stepwise linear regression When p

88 Dec 06, 2022
Tuplex is a parallel big data processing framework that runs data science pipelines written in Python at the speed of compiled code

Tuplex is a parallel big data processing framework that runs data science pipelines written in Python at the speed of compiled code. Tuplex has similar Python APIs to Apache Spark or Dask, but rather

Tuplex 791 Jan 04, 2023
This creates a ohlc timeseries from downloaded CSV files from NSE India website and makes a SQLite database for your research.

NSE-timeseries-form-CSV-file-creator-and-SQL-appender- This creates a ohlc timeseries from downloaded CSV files from National Stock Exchange India (NS

PILLAI, Amal 1 Oct 02, 2022