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docs: hiv #42

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ba25546
refactor: begin
tristan-f-r Jul 30, 2025
c80c561
updated documentation
AMINOexe Jul 30, 2025
a38177b
chore: remove other readme
tristan-f-r Jul 30, 2025
e29e737
fix: use correct file names
tristan-f-r Jul 30, 2025
70500b3
fix: more file path changes
tristan-f-r Jul 30, 2025
d1d3cf7
fix: typo in prize 060
tristan-f-r Jul 30, 2025
d72db21
docs: correct exceptions to copied code
tristan-f-r Jul 31, 2025
e07d6d4
docs: be clearer about paper
tristan-f-r Aug 8, 2025
97d3cfa
remove redundant docs cmt
tristan-f-r Aug 8, 2025
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4 changes: 2 additions & 2 deletions configs/dmmm.yaml
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Expand Up @@ -46,12 +46,12 @@ datasets:
# TODO: use old paramaters for datasets
# HIV: https://github.com/Reed-CompBio/spras-benchmarking/blob/0293ae4dc0be59502fac06b42cfd9796a4b4413e/hiv-benchmarking/spras-config/config.yaml
- label: dmmmhiv060
node_files: ["processed_prize_060.txt"]
node_files: ["processed_prizes_060.txt"]
edge_files: ["phosphosite-irefindex13.0-uniprot.txt"]
other_files: []
data_dir: "datasets/hiv/processed"
- label: dmmmhiv05
node_files: ["processed_prize_05.txt"]
node_files: ["processed_prizes_05.txt"]
edge_files: ["phosphosite-irefindex13.0-uniprot.txt"]
other_files: []
data_dir: "datasets/hiv/processed"
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3 changes: 2 additions & 1 deletion datasets/hiv/.gitignore
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processed
/processed
/Pickles
15 changes: 15 additions & 0 deletions datasets/hiv/README.md
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# HIV dataset

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@agitter agitter Aug 8, 2025

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I would find it helpful to have more of the context from https://github.com/Reed-CompBio/spras-benchmarking/blob/0293ae4dc0be59502fac06b42cfd9796a4b4413e/hiv-benchmarking/README.md. What is the overall goal of this benchmarking dataset?

Now that we have know more about the types of datasets in the SPRAS benchmark, we can categorize it. This one uses omic data as input and uses curated data as a gold standard, but the curated data we determined to be a poor fit so it lacks a good gold standard.

## Raw files

See `raw/README.md`.

## File organization

See `Snakefile` for the way that all of the IO files are connected.

1. `fetch.py` - This grabs the score files from https://doi.org/10.1371/journal.ppat.1011492 - see `fetch.py` for more info.
1. `prepare.py` - This cleans up the prize files in `raw`; specifically to remove duplicates.
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The original readme was more detailed about these processing steps. For instance, one of the steps removed isoform identifiers, which I don't see mentioned here. My goal is to be able to visit the readme for a benchmarking dataset and be able to write the methods section of the manuscript from that information without re-reading all the code. We'll get bogged down during writing if we have to read all the source to remember how we processed each dataset.

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@tristan-f-r tristan-f-r Aug 8, 2025 •
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This motivation makes sense - I was very confused about what the audience for a dataset-wide README is. My main worry is that, like the other READMEs present in this repository, we would get outdated documentation that doesn't actually match with what's happening during processing, so I do want to ask a follow-up question (to determine how much we should be worried about maintaining the README:)

Would future users of SPRAS-benchmarking also be writing down the methodology described in the README as well, or is it specifically for the first benchmarking paper?

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@tristan-f-r tristan-f-r Aug 10, 2025 •
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(My question was answered in another comment)

1. `name_mapping.py` - Converts from UniProt KB-ACID to UniProt KB to meet in the middle with `kegg_ortholog.py`. We chose UniProt KB for its generality.
1. `spras_formatting.py` - Formats the input files into a SPRAS-ready format.
1. `kegg_orthology.py` - This is used to generate the KEGG ortholog file for gold standards, but this has yet to be finalized.
31 changes: 0 additions & 31 deletions datasets/hiv/Scripts/Data_Prep.py
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64 changes: 0 additions & 64 deletions datasets/hiv/Scripts/Kegg_Orthology.py
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28 changes: 0 additions & 28 deletions datasets/hiv/Scripts/SPRAS_Formatting.py
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28 changes: 18 additions & 10 deletions datasets/hiv/Snakefile
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rule all:
input:
"processed/processed_prize_05.txt",
"processed/processed_prize_060.txt",
"processed/processed_prizes_05.txt",
"processed/processed_prizes_060.txt",
"processed/phosphosite-irefindex13.0-uniprot.txt"

rule fetch:
output:
"raw/prizes_05.tsv",
"raw/prizes_060.tsv",
"raw/ko03250.xml"
shell:
"uv run scripts/fetch.py"

rule data_prep:
input:
"raw/prize_05.csv",
"raw/prize_060.csv"
"raw/prizes_05.tsv",
"raw/prizes_060.tsv"
output:
"Pickles/NodeIDs.pkl"
shell:
"uv run Scripts/Data_Prep.py"
"uv run scripts/prepare.py"

rule name_mapping:
input:
"Pickles/NodeIDs.pkl"
output:
"Pickles/UniprotIDs.pkl"
shell:
"uv run Scripts/Name_Mapping.py"
"uv run scripts/name_mapping.py"

rule spras_formatting:
input:
"Pickles/NodeIDs.pkl",
"Pickles/UniprotIDs.pkl"
output:
"processed/processed_prize_05.txt",
"processed/processed_prize_060.txt"
"processed/processed_prizes_05.txt",
"processed/processed_prizes_060.txt"
shell:
"uv run Scripts/SPRAS_Formatting.py"
"uv run scripts/spras_formatting.py"

rule copy_network:
input:
"raw/phosphosite-irefindex13.0-uniprot.txt"
output:
"processed/phosphosite-irefindex13.0-uniprot.txt"
shell:
"cp raw/phosphosite-irefindex13.0-uniprot.txt processed/phosphosite-irefindex13.0-uniprot.txt"
"cp raw/phosphosite-irefindex13.0-uniprot.txt processed/phosphosite-irefindex13.0-uniprot.txt"
3 changes: 3 additions & 0 deletions datasets/hiv/raw/.gitignore
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prizes_05.tsv
prizes_060.tsv
ko03250.xml
10 changes: 10 additions & 0 deletions datasets/hiv/raw/README.md
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# raw
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@agitter agitter Aug 8, 2025

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I'm missing where the rest of the input files come from. Having it in fetch.py does not tell me where the inputs for this dataset come from and what they are.

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@tristan-f-r tristan-f-r Aug 8, 2025

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I'm a little confused about this comment (though there was some clarification missing on a paper link, which I've just committed) - do you want docs duplication from fetch.py into raw/README.md? The inline and top-level comments in fetch.py describe more than the original hiv README did, including the origin of ko03250.xml.

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We can call it duplication, but yes. I want the readme(s) to tell the story of the dataset. There will be multiple scripts needed to execute the benchmark, and I see two different goals:

  1. the code with good comments
  2. documenting the dataset globally so that we (and potentially external readers of a manuscript) can easily translate from a dataset directory to an overall understanding of how all the pieces work together, where external information comes from, and gather that information in a manuscript.

We could go through the exercise of writing one of the paragraph for the benchmarking paper manuscript to see what information we need to expose.

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@ntalluri ntalluri Aug 11, 2025

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This is the current level of detail I was going for in the benchmarking report:

Structure this section so each dataset explains what the data is and the biological context, where the all data came from, the sources/targets/prizes (include the biological roles of the inputs?), the ids (explain why the translation is needed and why the chosen id was chosen), the gold standard, the interactome, and the preprocessing for everything.


Some `raw` files are fetched from `../scripts/fetch.py`.

The `phosphosite-irefindex13.0-uniprot.txt` is
a background interactome provided by SPRAS: https://github.com/Reed-CompBio/spras/blob/be8bc7f8d71880d7ce9c9ceeeddfefa6eb60c522/input/phosphosite-irefindex13.0-uniprot.txt.

The `ko03250.xml` is from `https://www.kegg.jp/entry/ko03250`. Specifically, if you click on the pathway image in the entry,
you'll get to https://www.kegg.jp/pathway/ko03250, where you download the KGML file (which is formatted as a `.xml` file)
under `Download` -> `KGML`. (The final file is at https://www.kegg.jp/kegg-bin/download?entry=ko03250&format=kgml).
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