Addition of pseudotorsion caclulation eta, theta, eta', and theta' to barnaba package

Overview

Addition to Original Barnaba Code:

This is modified version of Barnaba package to calculate RNA pseudotorsion angles eta, theta, eta', and theta'.

Please refer original github here: https://github.com/srnas/barnaba

Following files are modified to include calculation of RNA pseudotorsion angles:

nucleic.py, functions.py, definitions.py, and commandline.py

Definitions of RNA pseudotorsion angles:

Please refer to this nice blog by Dr. Xiang-Jun Lu (x3DNA-DSSR software page) for definitions of pseudotorsions:

https://x3dna.org/highlights/pseudo-torsions-to-simplify-the-representation-of-dna-rna-backbone-conformation

Requirements

Barnaba requires:

  • Python 2.7.x or > 3.3
  • Numpy
  • Scipy
  • Mdtraj 1.9
  • future

Barnaba requires mdtraj (http://mdtraj.org/) for manipulating structures and trajectories. To perform cluster analysis, scikit-learn is required too.

Required packages can be installed using pip, e.g.:

pip install mdtraj

Installation (if you want RNA pseudotorsions !!!)

git clone https://github.com/mandar5335/barnaba_pseudotor

then move to the barnaba directory and run the command

pip install -e .

Usage:

RNA pseudotorsions can be calculated using the command line or in jupyter-notebook.

command line:

barnaba TORSION --pseudo --pdb foo.pdb

Jupyter Notebook:

import barnaba as bb
from barnaba import definitions
angle, res =  bb.eta_theta_angles("foo.pdb")
definitions.pseudo_angles

This will calculate four pseudotorsions: ['eta', 'theta', 'eta_prime', 'theta_prime']

References

[1] Bottaro, Sandro, Francesco Di Palma, and Giovanni Bussi.
"The role of nucleobase interactions in RNA structure and dynamics."
Nucleic acids research 42.21 (2014): 13306-13314.

[2] Pinamonti, Giovanni, et al.
"Elastic network models for RNA: a comparative assessment with molecular dynamics and SHAPE experiments."
Nucleic acids research 43.15 (2015): 7260-7269.

If you use Barnaba in your work, please cite the following paper::

@article{bottaro2019barnaba,
	title={Barnaba: software for analysis of nucleic acid structures and trajectories},
	author={Bottaro, Sandro and Bussi, Giovanni and Pinamonti, Giovanni and Rei{\ss}er, Sabine and Boomsma, Wouter and Lindorff-Larsen, Kresten},
	journal={RNA},
	volume={25},
	number={2},
	pages={219--231},
	year={2019},
	publisher={Cold Spring Harbor Lab}
}
Owner
Mandar Kulkarni
Mandar Kulkarni
This repository contains the source code of an efficient 1D probabilistic model for music time analysis proposed in ICASSP2022 venue.

Jump Reward Inference for 1D Music Rhythmic State Spaces An implementation of the probablistic jump reward inference model for music rhythmic informat

Mojtaba Heydari 25 Dec 16, 2022
Efficiently Disentangle Causal Representations

Efficiently Disentangle Causal Representations Install dependency pip install -r requirements.txt Main experiments Causality direction prediction cd

4 Apr 01, 2022
Deeplab-resnet-101 in Pytorch with Jaccard loss

Deeplab-resnet-101 Pytorch with Lovász hinge loss Train deeplab-resnet-101 with binary Jaccard loss surrogate, the Lovász hinge, as described in http:

Maxim Berman 95 Apr 15, 2022
Implementation of ML models like Decision tree, Naive Bayes, Logistic Regression and many other

ML_Model_implementaion Implementation of ML models like Decision tree, Naive Bayes, Logistic Regression and many other dectree_model: Implementation o

Anshuman Dalai 3 Jan 24, 2022
Walk with fastai

Shield: This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Walk with fastai What is this p

Walk with fastai 124 Dec 10, 2022
Film review classification

Film review classification Решение задачи классификации отзывов на фильмы на положительные и отрицательные с помощью рекуррентных нейронных сетей 1. З

Nikita Dukin 3 Jan 21, 2022
[CVPR 2022] Back To Reality: Weak-supervised 3D Object Detection with Shape-guided Label Enhancement

Back To Reality: Weak-supervised 3D Object Detection with Shape-guided Label Enhancement Announcement 🔥 We have not tested the code yet. We will fini

Xiuwei Xu 7 Oct 30, 2022
INSPIRED: A Transparent Dialogue Dataset for Interactive Semantic Parsing

INSPIRED: A Transparent Dialogue Dataset for Interactive Semantic Parsing Existing studies on semantic parsing focus primarily on mapping a natural-la

7 Aug 22, 2022
Information Gain Filtration (IGF) is a method for filtering domain-specific data during language model finetuning. IGF shows significant improvements over baseline fine-tuning without data filtration.

Information Gain Filtration Information Gain Filtration (IGF) is a method for filtering domain-specific data during language model finetuning. IGF sho

4 Jul 28, 2022
Localization Distillation for Object Detection

Localization Distillation for Object Detection This repo is based on mmDetection. This is the code for our paper: Localization Distillation

274 Dec 26, 2022
LSTC: Boosting Atomic Action Detection with Long-Short-Term Context

LSTC: Boosting Atomic Action Detection with Long-Short-Term Context This Repository contains the code on AVA of our ACM MM 2021 paper: LSTC: Boosting

Tencent YouTu Research 9 Oct 11, 2022
Unified MultiWOZ evaluation scripts for the context-to-response task.

MultiWOZ Context-to-Response Evaluation Standardized and easy to use Inform, Success, BLEU ~ See the paper ~ Easy-to-use scripts for standardized eval

Tomáš Nekvinda 38 Dec 13, 2022
Open-Set Recognition: A Good Closed-Set Classifier is All You Need

Open-Set Recognition: A Good Closed-Set Classifier is All You Need Code for our paper: "Open-Set Recognition: A Good Closed-Set Classifier is All You

194 Jan 03, 2023
Implementation for our AAAI2021 paper (Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction).

SSAN Introduction This is the pytorch implementation of the SSAN model (see our AAAI2021 paper: Entity Structure Within and Throughout: Modeling Menti

benfeng 69 Nov 15, 2022
Official repo for our 3DV 2021 paper "Monocular 3D Reconstruction of Interacting Hands via Collision-Aware Factorized Refinements".

Monocular 3D Reconstruction of Interacting Hands via Collision-Aware Factorized Refinements Yu Rong, Jingbo Wang, Ziwei Liu, Chen Change Loy Paper. Pr

Yu Rong 41 Dec 13, 2022
Learning where to learn - Gradient sparsity in meta and continual learning

Learning where to learn - Gradient sparsity in meta and continual learning In this paper, we investigate gradient sparsity found by MAML in various co

Johannes Oswald 28 Dec 09, 2022
Official Implementation of Neural Splines

Neural Splines: Fitting 3D Surfaces with Inifinitely-Wide Neural Networks This repository contains the official implementation of the CVPR 2021 (Oral)

Francis Williams 56 Nov 29, 2022
Neural-net-from-scratch - A simple Neural Network from scratch in Python using the Pymathrix library

A Simple Neural Network from scratch A Simple Neural Network from scratch in Pyt

Youssef Chafiqui 2 Jan 07, 2022
EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation

EFENet EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation Code is a bit messy now. I woud clean up soon. For training the EF

Yaping Zhao 19 Nov 05, 2022
A Keras implementation of YOLOv3 (Tensorflow backend)

keras-yolo3 Introduction A Keras implementation of YOLOv3 (Tensorflow backend) inspired by allanzelener/YAD2K. Quick Start Download YOLOv3 weights fro

7.1k Jan 03, 2023