diamond
edlib
| diamond | edlib | |
|---|---|---|
| 3 | 2 | |
| 975 | 484 | |
| - | - | |
| 6.3 | 1.1 | |
| 4 months ago | about 1 year ago | |
| C++ | C++ | |
| GNU General Public License v3.0 only | MIT License |
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diamond
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comparing the similarity between a set of protein sequences
Diamond (https://github.com/bbuchfink/diamond) might help. It has a protein sequence clustering option. You could cluster your sequences and then take the centroids of each cluster. Vary the BLAST parameters to increase/decrease the numbers of clusters.
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which database is best to use on BLAST to identify an unknown protein?
What I usually do is the DIAMOND search (https://github.com/bbuchfink/diamond) on UniRef (50/90) database.
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someone remotely helped me to download and execute this file called a diamond.exe from the following link: https://github.com/bbuchfink/diamond Windows said it could be unsafe so I disabled Windows Defender and pressed run on it but it didn't do anything, is this a virus is it safe?
someone remotely helped me to download and execute this file called a diamond.exe from the following link: https://github.com/bbuchfink/diamond Windows said it could be unsafe so I disabled Windows Defender and pressed run on it but it didn't do anything, is this a virus is it safe?
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
What are some alternatives?
Biopython - Official git repository for Biopython (originally converted from CVS)
seq - A high-performance, Pythonic language for bioinformatics
seqan3 - The modern C++ library for sequence analysis. Contains version 3 of the library and API docs.
bwa-mem2 - The next version of bwa-mem
nanopolish - Signal-level algorithms for MinION data
libnitrokey - Communicate with Nitrokey devices in a clean and easy manner
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 - A lightweight header-only library for using Keras (TensorFlow) models in C++.
edlibtest - Private changes to https://github.com/Martinsos/edlib