edlib
libnitrokey
edlib | libnitrokey | |
---|---|---|
2 | 1 | |
484 | 64 | |
- | - | |
1.1 | 3.3 | |
about 1 year ago | 4 months ago | |
C++ | C++ | |
MIT License | GNU Lesser General Public License v3.0 only |
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edlib
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What's an efficient way to find multiple subsequences in several FASTQs?
I’ve got a similar situation. I was implementing the Smith-Waterman algorithm when I figured someone had to have already written a “fast” version of this. I found the edlib package (https://github.com/Martinsos/edlib) which does sequence alignment using Levenshtein distance. Essentially same DP algorithm as your traditional NW or SW only this is a C++ implementation with a Python wrapper. (I’m assuming you’re using Python, could be wrong though). The pertinent aspects of the output of this function contains the distance (dissimilarity) and the location (what index does the alignment start and end). This tool may go a ways to helping your pipeline. You could also look to metagenomic papers for inspiration as this is a problem (find a substring in a huge amount of data) that the community contends with all the time. Kmer based approach may also be useful if you want to attempt the alignment free path. Cheers.
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ModuleNotFoundError after running `pip install -e .` locally
I appear to get that error with the original source as well. https://github.com/Martinsos/edlib
libnitrokey
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Problems using NitroKey Pro
You could use the old rules with the plugdev group if you are uninterested in elogind.
What are some alternatives?
seq - A high-performance, Pythonic language for bioinformatics
QtPass - QtPass is a multi-platform GUI for pass, the standard unix password manager.
bwa-mem2 - The next version of bwa-mem
plog - Portable, simple and extensible C++ logging library
nanopolish - Signal-level algorithms for MinION data
casadi - CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python or Matlab/Octave.
frugally-deep - Header-only library for using Keras (TensorFlow) models in C++.
edlibtest - Private changes to https://github.com/Martinsos/edlib