Upload src/task_metadata.json with huggingface_hub
Browse files- src/task_metadata.json +26 -0
src/task_metadata.json
CHANGED
|
@@ -639,5 +639,31 @@
|
|
| 639 |
}
|
| 640 |
]
|
| 641 |
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 642 |
}
|
| 643 |
]
|
|
|
|
| 639 |
}
|
| 640 |
]
|
| 641 |
}
|
| 642 |
+
},
|
| 643 |
+
{
|
| 644 |
+
"task_id": "cutandrun",
|
| 645 |
+
"name": "CUT&RUN Epigenomic Profiling",
|
| 646 |
+
"description": "This task analyzes CUT&RUN sequencing data to identify histone modification peaks. Paired-end reads, a chr22 reference genome, and blacklist regions are provided.",
|
| 647 |
+
"task_prompt": "Identify histone modification enrichment peaks from CUT&RUN sequencing data using two independent peak calling approaches, then compute a consensus peak set. A reference genome and blacklist are in reference/. The output should be a CSV file with the following columns: 'metric','value'.\n<example>metric,value\ntotal_reads,33914\nmapped_reads,33914\nmapping_rate,100.00\nduplication_rate,0.046353\npeaks_caller_a,198\npeaks_caller_b,38\nconsensus_peaks,64\nfraction_reads_in_peaks,0.0238</example>",
|
| 648 |
+
"download_urls": {
|
| 649 |
+
"data": [
|
| 650 |
+
{
|
| 651 |
+
"filename": "data.tar.gz",
|
| 652 |
+
"url": "https://huggingface.co/datasets/lingzhi227/Extended-BioAgentBench/resolve/main/tasks/cutandrun/data.tar.gz"
|
| 653 |
+
}
|
| 654 |
+
],
|
| 655 |
+
"reference_data": [
|
| 656 |
+
{
|
| 657 |
+
"filename": "reference.tar.gz",
|
| 658 |
+
"url": "https://huggingface.co/datasets/lingzhi227/Extended-BioAgentBench/resolve/main/tasks/cutandrun/reference.tar.gz"
|
| 659 |
+
}
|
| 660 |
+
],
|
| 661 |
+
"results": [
|
| 662 |
+
{
|
| 663 |
+
"filename": "results.tar.gz",
|
| 664 |
+
"url": "https://huggingface.co/datasets/lingzhi227/Extended-BioAgentBench/resolve/main/tasks/cutandrun/results.tar.gz"
|
| 665 |
+
}
|
| 666 |
+
]
|
| 667 |
+
}
|
| 668 |
}
|
| 669 |
]
|