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
Fast, flexible and easy to use probabilistic modelling in Python.

Please consider citing the JMLR-MLOSS Manuscript if you've used pomegranate in your academic work! pomegranate is a package for building probabilistic

Jacob Schreiber 3k Jan 02, 2023
Gathering data of likes on Tinder within the past 7 days

tinder_likes_data Gathering data of Likes Sent on Tinder within the past 7 days. Versions November 25th, 2021 - Functionality to get the name and age

Alex Carter 12 Jan 05, 2023
track your GitHub statistics

GitHub-Stalker track your github statistics 👀 features find new followers or unfollowers find who got a star on your project or remove stars find who

Bahadır Araz 34 Nov 18, 2022
Reading streams of Twitter data, save them to Kafka, then process with Kafka Stream API and Spark Streaming

Using Streaming Twitter Data with Kafka and Spark Reading streams of Twitter data, publishing them to Kafka topic, process message using Kafka Stream

Rustam Zokirov 1 Dec 06, 2021
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
Ejercicios Panda usando Pandas

Readme Below we add configuration details to locally test your application To co

1 Jan 22, 2022
Flexible HDF5 saving/loading and other data science tools from the University of Chicago

deepdish Flexible HDF5 saving/loading and other data science tools from the University of Chicago. This repository also host a Deep Learning blog: htt

UChicago - Department of Computer Science 255 Dec 10, 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
collect training and calibration data for gaze tracking

Collect Training and Calibration Data for Gaze Tracking This tool allows collecting gaze data necessary for personal calibration or training of eye-tr

Pascal 5 Dec 17, 2022
Efficient matrix representations for working with tabular data

Efficient matrix representations for working with tabular data

QuantCo 70 Dec 14, 2022
vartests is a Python library to perform some statistic tests to evaluate Value at Risk (VaR) Models

gg I wasn't satisfied with any of the other available Gemini clients, so I wrote my own. Requires Python 3.9 (maybe older, I haven't checked) and opti

RAFAEL RODRIGUES 5 Jan 03, 2023
ASTR 302: Python for Astronomy (Winter '22)

ASTR 302, Winter 2022, University of Washington: Python for Astronomy Mario Jurić Location When: 2:30-3:50, Monday & Wednesday, Winter quarter 2022 Wh

UW ASTR 302: Python for Astronomy 4 Jan 12, 2022
Pandas and Spark DataFrame comparison for humans

DataComPy DataComPy is a package to compare two Pandas DataFrames. Originally started to be something of a replacement for SAS's PROC COMPARE for Pand

Capital One 259 Dec 24, 2022
Python dataset creator to construct datasets composed of OpenFace extracted features and Shimmer3 GSR+ Sensor datas

Python dataset creator to construct datasets composed of OpenFace extracted features and Shimmer3 GSR+ Sensor datas

Gabriele 3 Jul 05, 2022
Larch: Applications and Python Library for Data Analysis of X-ray Absorption Spectroscopy (XAS, XANES, XAFS, EXAFS), X-ray Fluorescence (XRF) Spectroscopy and Imaging

Larch: Data Analysis Tools for X-ray Spectroscopy and More Documentation: http://xraypy.github.io/xraylarch Code: http://github.com/xraypy/xraylarch L

xraypy 95 Dec 13, 2022
Bearsql allows you to query pandas dataframe with sql syntax.

Bearsql adds sql syntax on pandas dataframe. It uses duckdb to speedup the pandas processing and as the sql engine

14 Jun 22, 2022
Pizza Orders Data Pipeline Usecase Solved by SQL, Sqoop, HDFS, Hive, Airflow.

PizzaOrders_DataPipeline There is a Tony who is owning a New Pizza shop. He knew that pizza alone was not going to help him get seed funding to expand

Melwin Varghese P 4 Jun 05, 2022
Stochastic Gradient Trees implementation in Python

Stochastic Gradient Trees - Python Stochastic Gradient Trees1 by Henry Gouk, Bernhard Pfahringer, and Eibe Frank implementation in Python. Based on th

John Koumentis 2 Nov 18, 2022
Universal data analysis tools for atmospheric sciences

U_analysis Universal data analysis tools for atmospheric sciences Script written in python 3. This file defines multiple functions that can be used fo

Luis Ackermann 1 Oct 10, 2021
Analyse the limit order book in seconds. Zoom to tick level or get yourself an overview of the trading day.

Analyse the limit order book in seconds. Zoom to tick level or get yourself an overview of the trading day. Correlate the market activity with the Apple Keynote presentations.

2 Jan 04, 2022