NanoSim
DNABERT
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NanoSim | DNABERT | |
---|---|---|
1 | 1 | |
213 | 543 | |
3.3% | - | |
5.6 | 3.1 | |
about 2 months ago | about 2 months ago | |
Python | Python | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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NanoSim
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Raw nanowire sequencer data
Alternatively, you can make your own data using NanoSim. I haven't used it myself yet but it generates signal level files with similar error profiles to the real thing. https://github.com/bcgsc/NanoSim
DNABERT
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[D] New to DNABERT
If I want to get started, they said it's optional to pre-train (so you can skip to step 3). This is where I got tripped up: "Note that the sequences are in kmer format, so you will need to convert your sequences into that." From what I understand, you need to do this so that all of the sequences are the same length? So kmer=6 means all of the sequences are length 6? Someone suggested that I take the first nucleotide in the promoter and grab 3 nucleotides before and 3 nucleotides after (+/-3 bases). I don't think that's how the kmer thing works though? I tried replicating how I think it works down below (I got confused on the last row of the 'after' df). Please correct me if I'm wrong!
What are some alternatives?
bonito - A PyTorch Basecaller for Oxford Nanopore Reads
courses - This repository is a curated collection of links to various courses and resources about Artificial Intelligence (AI)
bioinformatics - Bioinformatic algorithms for the UCLA Bioinformatics Specialization
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
datasets - 🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools
stanford-tensorflow-tutorials - This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
nlp-recipes - Natural Language Processing Best Practices & Examples
bioconvert - Bioconvert is a collaborative project to facilitate the interconversion of life science data from one format to another.
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
OOK_Audio - De Bruijn Sequence WAV File Generator for the HackRF