Edge computing models for IoT Analytics
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Updated
Jun 11, 2024 - Python
Edge computing models for IoT Analytics
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
Cython bindings and Python interface to Opal, a SIMD-accelerated database search aligner.
Golang metrics for calculating string similarity and other string utility functions
Fuzzy finder algorithms ala Smith-Waterman for Zig.
Easy to use and modify implementation of Smith-Waterman Algorithm.
Benchmark the performance of sequence alignment workflows
A collection of string comparisons algorithms
Fuzzy finder using Smith-Waterman algorithm
Needleman-Wunsch and Smith-Waterman algorithms in python
This notebook does the analysis of a protein sequence dataset in FASTA format. It addresses challenges in data import, alignment, embedding, and classification using bioinformatics tools and machine learning techniques. The solution provides a systematic approach to extract insights from structured data, crucial for bioinformatics research.
SIMD C/C++ library for massive optimal sequence alignment (local/SW, infix, overlap, global)
AlnSeq runs a c coded Smith Waterman, Needleman Wunsch, and an Hirschberg. It can run as a standalone or as a library for python.
Python Implementation of the Smith-Waterman Algorithm for Bioinformatics
Pairwise alignment algorithm testing page
Solidity implementations of well-known pairwise alignment methods such as Needleman-Wunsch's global sequence alignment and the Smith-Waterman local sequence alignment algorithm.
Longest Common Subsequence algorithm. Global Alignment is the Needleman Wunsche algorithm and the local alignment is Smith Waterman algorithm
💻 Project for the course of Algorithms for Bioinformtics
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