Add initial public release

Author: twrightsman

.gitattributes

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+results/*/*.ipynb filter=lfs diff=lfs merge=lfs -text
+results/*/figures/*.png filter=lfs diff=lfs merge=lfs -text

.github/workflows/manuscript.yaml

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+name: Render manuscript on change
+
+on:
+  push:
+    paths:
+      - 'docs/manuscript/**.typ'
+      - 'docs/manuscript/pixi.lock'
+      - 'results/*/figures/*'
+  workflow_dispatch:
+
+env:
+  PIXI_VERSION: 0.15.1
+
+jobs:
+  render:
+    name: Render manuscript
+    runs-on: ubuntu-22.04
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+        with:
+          lfs: true
+      - name: Cache pixi binary
+        id: cache-pixi
+        uses: actions/cache@v4
+        with:
+          path: ~/.local/bin/pixi
+          key: cache-pixi-${{ env.PIXI_VERSION }}
+      - if: ${{ steps.cache-pixi.outputs.cache-hit != 'true' }}
+        name: Install pixi
+        run: |
+          mkdir --parents $HOME/.local/bin
+          wget https://github.com/prefix-dev/pixi/releases/download/v${PIXI_VERSION}/pixi-x86_64-unknown-linux-musl --output-document=$HOME/.local/bin/pixi
+          chmod u+x $HOME/.local/bin/pixi
+      - name: Cache rattler cache
+        uses: actions/cache@v4
+        with:
+          path: ~/.cache/rattler
+          key: cache-rattler-manuscript-${{ hashFiles('docs/manuscript/pixi.lock') }}
+      - name: Build environment
+        run: $HOME/.local/bin/pixi install
+        working-directory: docs/manuscript
+      - name: Render manuscript
+        run: $HOME/.local/bin/pixi run build
+        working-directory: docs/manuscript
+      - name: Upload manuscript as artifact
+        uses: actions/upload-artifact@v4
+        with:
+          name: manuscript
+          path: docs/manuscript/manuscript.pdf

.gitignore

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+tmp/
+
+# Pixi
+.pixi/
+
+# Python
+*.egg-info/
+__pycache__/
+*.py[cod]
+
+# Vim
+*.swp
+.idea/
+
+# IDE
+.vscode/
+
+# Jupyter
+.ipynb_checkpoints/

LICENSE

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+Copyright © 2024 Travis Wrightsman
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

README.md

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+# Current genomic deep learning architectures generalize across grass species but not alleles
+
+## Dependencies
+
+- [Pixi](https://pixi.sh)
+- [git-lfs](https://git-lfs.github.com)

data/.gitignore

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+genomes/

data/samples.tsv

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+manuscript.pdf
+.pixi/

docs/manuscript/.gitignore

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+# Manuscript
+
+```
+pixi run build
+```

docs/manuscript/README.md

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+Non-coding regions of the genome are just as important as coding regions for understanding the mapping from genotype to phenotype.
+Interpreting deep learning models trained on RNA-seq is an emerging method to highlight functional sites within non-coding regions.
+Most of the work on RNA abundance models has been done within humans and mice, with little attention paid to plants.
+Here, we benchmark four genomic deep learning model architectures with genomes and RNA-seq data from 18 species closely related to maize and sorghum within the Andropogoneae.
+The Andropogoneae are a tribe of C4 grasses that have adapted to a wide range of environments worldwide since diverging 18 million years ago.
+Hundreds of millions of years of evolution across these species has produced a large, diverse pool of training alleles across species sharing a common physiology.
+As model input, we extracted 1,026 base pairs upstream of each gene’s translation start site.
+We held out maize as our test set and two closely related species as our validation set, training each architecture on the remaining Andropogoneae genomes.
+Within a panel of 26 maize lines, all architectures predict expression across genes moderately well but poorly across alleles.
+DanQ consistently ranked highest or second highest among all architectures yet performance was generally very similar across architectures despite orders of magnitude differences in size.
+This suggests that state-of-the-art supervised genomic deep learning models are able to generalize moderately well across related species but not sensitively separate alleles within species, the latter of which agrees with recent work within humans.
+We are releasing the preprocessed data and code for this work as a community benchmark to evaluate new architectures on our across-species and across-allele tasks.