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Efficient implementations of Needleman-Wunsch and other sequence alignment algorithms written in Rust with Python bindings via PyO3.
SneakySnake:snake: is the first and the only pre-alignment filtering algorithm that works efficiently and fast on modern CPU, FPGA, and GPU architectures. It greatly (by more than two orders of magnitude) expedites sequence alignment calculation for both short and long reads. Described in the Bioinformatics (2020) by Alser et al. https://arxiv.org/abs/1910.09020.
Needleman-Wunsch and Smith-Waterman algorithms in python
Interactive Visualization of Needleman-Wunsch Algorithm in Javascript
Collection of sequence alignment algorithms.
Generate an accurate, timestamped transcript given an audio file and its text using Google Cloud's Speech-to-Text API via gRPC.
Tool for exploring sequence alignment algorithms
Implementation of Needleman-Wunsch, Smith-Waterman, Hirschberg and affine bioinformatics algorithms for alighning biological sequences
This is the implementation of 3rd Part in 3-Part Series of Algorithms Illuminated Book. All Implementations in this repository are written in both Python and Golang. Single IPython Notebook contains all Algorithms given in this Part 3.
The first work to provide a comprehensive survey of a prominent set of algorithmic improvement and hardware acceleration efforts for the entire genome analysis pipeline used for the three most prominent sequencing data, short reads (Illumina), ultra-long reads (ONT), and accurate long reads (HiFi). Described in arXiv (2022) by Alser et al. https://arxiv.org/abs/2205.07957
Less-wrong single-file Numba-accelerated Python implementation of Gotoh affine gap penalty extensions for the Needleman–Wunsch, Smith-Waterman, and Levenshtein algorithms for sequence alignment
Use the Needleman-Wunsch algorithm to align two sequences: s1 and s2. Assume that a match = +2, mismatch = -2, gap = -2
DNA Sequence Alignment with Dynamic Programming Implementation using the Needleman-Wunsch Algorithm and Smith-Waterman Algorithm.
A systematic survey of algorithmic foundations and methodologies across 107 alignment methods (1988-2021), for both short and long reads. We provide a rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read alignment. Described by Alser et al. at https://arxiv.org/abs/2003.00110.
Sequence alignment algorithms
:microscope: :checkered_flag: Comparison of DNA Sequences
This code is meant for educational purposes only! Sequence alignment in Python 3.x using Needleman–Wunsch algorithm. Reference code from TyMA (2017 - University of Málaga)
Sequence Alignment (Needleman–Wunsch Algorithm using Dynamic Programming) for aligning sequences (words, sentences, DNA etc.)
C# implementation of Needleman-Wunsch global sequence alignment algorithm.
Global and Local Sequence Alignment
An implementation of the Needleman-Wunsch algorithm for finding the best global alignment of two nucleotides.
star alignment method implementation for multiple DNA sequences alignment
This is Mispronunciation detection and diagnosis Score Metric
pairwise sequence alignment in scala
Solidity implementations of well-known pairwise alignment methods such as Needleman-Wunsch's global sequence alignment and the Smith-Waterman local sequence alignment algorithm.
A Pharo classes comparer
Implement parallel Smith Waterman algorithm on CUDA
Bio informatics course materials for NEUB students.