In directed evolution, sequencing every variant enhances data insight and creates datasets suitable for AI/ML methods. This method is presented as an extension of the original Every Variant Sequencer using Illumina technology. With this approach, sequence variants can be generated within a day at an extremely low cost.
Figure 1: Overview of the LevSeq variant sequencing workflow using Nanopore technology. This diagram illustrates the key steps in the process, from sample preparation to data analysis and visualization.
A beta website is available here you just load directly your output from LevSeq and your LCMS results and get visualisations and per plate normalizations.
- Data to reproduce the results and to test are available on zenodo
- A dockerized website and database for labs to locally host and visualize their data: website is available here and code to host locally here
For setting up the experimental side of LevSeq we suggest the following preparations:
- Order forward and reverse primers compatible with the desired plasmid, see methods section of our paper.
- Successfully install Oxford Nanopore's software (this is only for if you are doing basecalling/minION processing). Link to installation guide.
The wet lab part is detailed in the method section of the paper or via the wiki.
Once samples are prepared, the multiplexed sample is used for sequencing, and the sequencing data is stored in the ../data folder as per the typical Nanopore flow (refer to Nanopore documentation for this).
After sequencing, you can identify variants, demultiplex, and combine with your variant function here! For simple applications, we recommend using the notebook example/Example.ipynb.
We aimed to make LevSeq as simple to use as possible, this means you should be able to run it all using pip (note you need samtools
and minimap2 installed on your path. However, if you have issues we recommend using the Docker instance!
(the pip version doesn't work well with mac M3 but docker does.)
We recommend using terminal and a conda environment for installation:
conda create --name levseq python=3.12 -y
conda activate levseq
pip install levseq
- Samtools: https://www.htslib.org/download/
conda install -c bioconda -c conda-forge samtools
- Minimap2: https://github.com/lh3/minimap2
conda install -c bioconda -c conda-forge minimap2
- gcc 13 and 14 on Mac M1 through M4 chips
brew install gcc@13
brew install gcc@14
For installing the whole pipeline, you'll need to use the docker image. For this, install docker as required for your operating system (https://docs.docker.com/engine/install/).
levseq <name of the run you can make this whatever> <location to data folder> <location of reference csv file>
If using linux system
docker pull yueminglong/levseq:levseq-1.2.5-x86
If using Mac M chips (image tested on M1, M3, and M4)
docker pull yueminglong/levseq:levseq-1.2.5-arm64
docker run --rm -v "$(pwd):/levseq_results" yueminglong/levseq:levseq-1.2.5-<architecture> <name> <location to data folder> <location of reference csv file>
Explanation:
--rm: Automatically removes the container after the command finishes.
-v "$(pwd):/levseq_results": Mounts the current directory ($(pwd)) to /levseq_results inside the container, ensuring the results are saved to your current directory.
yueminglong/levseq:levseq-1.2.5-<architecture>: Specifies the Docker image to run. Replace <architecture> with the appropriate platform (e.g., x86).
<name>: The name or identifier for the analysis.
<location to data folder>: Path to the folder containing input data.
<location of reference csv file>: Path to the reference .csv file.
Important Notes:
If the current directory is mounted to the container (via -v "$(pwd):/levseq_results"), the basecalled result in FASTQ format and the ref.csv file must be located in the current directory.
If these files are not present in the current directory, they will not be processed by the tool.
Output:
The results of the analysis will be saved to your current working directory.
See the manuscrtipt notebook for an example. *Note: if using docker, the html and csv final output will be saved in the directory that you are running from instead of in the Platemaps or Results subfolder.
- Name of the experiment, this will be the name of the output folder
- Location of basecalled fastq files, this is the direct output from using the MinKnow software for sequencing
- Location of reference csv file, this file includes information of barcodes used for each plate and the DNA sequence used for reference for each plate
--skip_demultiplexing If enabled, demultiplexing step will be skipped
--skip_variantcalling If enabled, variant valling step will be skipped
--output Save location for output, if not provided default to where the program is executed
--show_msa Showing multiple sequence alignment for each well
Great you should be all done!
For more details or trouble shooting please look at our computational_protocols.
If you have found LevSeq useful, please cite our paper.
@article{long2024levseq,
title={LevSeq: Rapid Generation of Sequence-Function Data for Directed Evolution and Machine Learning},
author={Long, Yueming and Mora, Ariane and Li, Francesca-Zhoufan and Gürsoy, Emre and Johnston, Kadina E and Arnold, Frances H},
journal={ACS Synthetic Biology},
year={2024},
publisher={American Chemical Society}
}Leave a feature request in the issues or reach us via email.