Omnigraph
그래프 엔진으로 컨텍스트를 조립하고, 객체 저장소 기반에서 에이전트가 코드처럼 그래프 데이터를 분기·타입·검토·병합·감사·거버넌스합니다
저장소 정보는 아직 수집하지 않았습니다.
아직 감지된 업데이트가 없습니다. GitHub 릴리스가 나오면 여기에 보입니다.
메이커가 쓴 소개는 위의 한 줄이 전부입니다. 더 자세한 내용은 저장소 README에서 읽을 수 있습니다. 저장소 README ↗
README 전체를 불러오는 중입니다.
README 전문 보기 ↗Code repository to generate an automatically updated Fern Tree of Life.
Please see the accompanying paper:
All code is in R, and workflow is controlled with the targets package.
A docker image is available to run the code.
Docker tags match the version of FTOL; e.g., the image with tag 1.0.1 is the image used to generate FTOL v1.0.1.
Run the setup.R script to create the folder structure needed to store external files and download most of the data files automatically.
source("R/setup.R")
Alternatively, you can manually create the following folder hierarchy yourself:
_targets
└── user
├── data_raw
│ ├── ref_aln
│ └── restez
│ └── sql_db
├── intermediates
│ ├── blast_sanger
│ ├── iqtree
│ │ ├── plastome
│ │ ├── sanger
│ │ ├── sanger_1
│ │ ├── sanger_10
│ │ ├── sanger_2
│ │ ├── sanger_3
│ │ ├── sanger_4
│ │ ├── sanger_5
│ │ ├── sanger_6
│ │ ├── sanger_7
│ │ ├── sanger_8
│ │ ├── sanger_9
│ │ └── sanger_fast
│ ├── ref_seqs
│ └── treepl
│ ├── con
│ ├── ml
│ └── ts
└── results
Another folder called ftol_data (to store data files generated by this workflow that will be made available via the ftolr R package) also needs to be created in the project root. This folder is itself a repo that can be cloned from https://github.com/fernphy/ftol_data.
If setup.R was run successfully, it will have already downloaded and unzipped the input data files from FigShare.
Alternatively, you can do so manually following these instructions:
ref_aln.tar.gz (reference alignments) and restez_sql_db.tar.gz (local GenBank database) from FigShare (https://doi.org/10.6084/m9.figshare.19474316)ref_aln.tar.gz and put the ref_aln folder in _targets/user/data_raw/restez_sql_db.tar.gz and put the bat and sql_logs folders in _targets/user/data_raw/restez/sql_db.The script to generate the local GenBank database (restez_sql_db.tar.gz) is setup_gb.R.
The script (targets workflow) to generate the reference FASTA files for extracting target gene regions (ref_aln.tar.gz) is prep_ref_seqs_plan.R
The data that result from these scripts have been made available on FigShare as described above, so these generally shouldn't need to be run.
targets::tar_make().Note that this code was designed to be run on a multi-core machine, so the number of cores specified (e.g., here) may need to be changed.
The complete workflow takes 1-2 weeks to complete, with phylogenetic analysis taking up by far most of the time.
The container entrypoint accepts HOST_UID and HOST_GID environment
variables. When provided, it remaps the internal container user to your host
UID/GID before running any process. This ensures that files written to mounted
volumes (including git objects created by the targets pipeline) are owned by
your host user rather than root.
docker build -t joelnitta/ftol:latest .
run.sh runs the targets pipeline inside a container and exits when done:
bash run.sh
HOST_UID and HOST_GID are set automatically from the calling user.
A .devcontainer/devcontainer.json is provided for VS Code's Dev Containers workflow.
Create a .env file in the project root supplying your host UID and GID (check
with id -u and id -g):
UID=1001
GID=1001
Open the project in VS Code and run Dev Containers: Reopen in Container from the Command Palette. VS Code manages the container lifecycle and opens the project workspace automatically.
The container user owns /renv, so R packages can be installed directly
without root. To perform root-level operations (e.g. apt-get), open a
separate shell as root:
docker exec -u root -it <container_name> bash