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src/task_metadata.json

@@ -1458,53 +1458,79 @@
     }
   },
   {
-    "task_id": "dda-lfq-proteomics",
-    "name": "DDA Label-Free Quantitative Proteomics",
-    "description": "This task performs data-dependent acquisition mass spectrometry analysis to identify and quantify proteins. An mzML spectral file and a protein sequence database are provided.",
-    "task_prompt": "Identify proteins from mass spectrometry data by searching spectra against a protein database, controlling false discovery rate, and quantifying identified peptides and proteins. Spectral data in data/, protein database in reference/. The output should be a CSV file with the following columns: 'metric','value'.\n<example>metric,value\ntotal_spectra,1690\ndatabase_size_with_decoys,12104\nsearch_engine_hits,1604\npsms_after_fdr,16\nunique_peptides,16\nproteins_identified,9</example>",
+    "task_id": "cnv-detection-wes",
+    "name": "CNV Detection from Whole-Exome Sequencing",
+    "description": "Detect copy number variants from whole-exome sequencing data using multiple CNV callers, merge calls, and assess concordance. Copy number variants (CNVs) are genomic segments that differ in copy number from the diploid reference. WES-based CNV detection requires paired tumor-normal analysis with specialized callers that account for the non-uniform capture efficiency across exome targets. The input includes paired-end reads for both tumor and normal samples, a reference genome (chr20), and an exome target intervals BED file.",
+    "task_prompt": "Detect copy number variants from paired tumor-normal whole-exome sequencing data. The input reads for tumor and normal samples are in data/ and the reference genome with exome target intervals are in reference/. Process both samples through quality trimming, alignment, duplicate marking, and coverage calculation. Run at least two independent CNV detection methods on the paired tumor-normal BAMs using the target intervals, merge their results, and compute summary statistics. The output should be a CSV file at results/report.csv with columns: 'metric','value'.\n<example>\nmetric,value\ntumor_reads,4319\ntumor_reads_after_trim,4164\nnormal_reads,435\nnormal_reads_after_trim,408\ntumor_mean_coverage,0.0\nnormal_mean_coverage,0.0\ntumor_dup_pct,14.63\nnormal_dup_pct,0.0\ncnvkit_cnv_count,0\nfreec_cnv_count,0\ngatk_cnv_count,0\ntotal_callers_run,3\ntotal_merged_cnvs,0\ntarget_intervals,1000\n</example>",
     "download_urls": {
       "data": [
         {
           "filename": "data.tar.gz",
-          "url": "https://huggingface.co/datasets/lingzhi227/Extended-BioAgentBench/resolve/main/tasks/dda-lfq-proteomics/data.tar.gz"
+          "url": "https://huggingface.co/datasets/lingzhi227/Extended-BioAgentBench/resolve/main/tasks/cnv-detection-wes/data.tar.gz"
         }
       ],
       "reference_data": [
         {
           "filename": "reference.tar.gz",
-          "url": "https://huggingface.co/datasets/lingzhi227/Extended-BioAgentBench/resolve/main/tasks/dda-lfq-proteomics/reference.tar.gz"
+          "url": "https://huggingface.co/datasets/lingzhi227/Extended-BioAgentBench/resolve/main/tasks/cnv-detection-wes/reference.tar.gz"
         }
       ],
       "results": [
         {
           "filename": "results.tar.gz",
-          "url": "https://huggingface.co/datasets/lingzhi227/Extended-BioAgentBench/resolve/main/tasks/dda-lfq-proteomics/results.tar.gz"
+          "url": "https://huggingface.co/datasets/lingzhi227/Extended-BioAgentBench/resolve/main/tasks/cnv-detection-wes/results.tar.gz"
         }
       ]
     }
   },
   {
-    "task_id": "cnv-detection-wes",
-    "name": "CNV Detection from Whole-Exome Sequencing",
-    "description": "Detect copy number variants from whole-exome sequencing data using multiple CNV callers, merge calls, and assess concordance. Copy number variants (CNVs) are genomic segments that differ in copy number from the diploid reference. WES-based CNV detection requires paired tumor-normal analysis with specialized callers that account for the non-uniform capture efficiency across exome targets. The input includes paired-end reads for both tumor and normal samples, a reference genome (chr20), and an exome target intervals BED file.",
-    "task_prompt": "Detect copy number variants from paired tumor-normal whole-exome sequencing data. The input reads for tumor and normal samples are in data/ and the reference genome with exome target intervals are in reference/. Process both samples through quality trimming, alignment, duplicate marking, and coverage calculation. Run at least two independent CNV detection methods on the paired tumor-normal BAMs using the target intervals, merge their results, and compute summary statistics. The output should be a CSV file at results/report.csv with columns: 'metric','value'.\n<example>\nmetric,value\ntumor_reads,4319\ntumor_reads_after_trim,4164\nnormal_reads,435\nnormal_reads_after_trim,408\ntumor_mean_coverage,0.0\nnormal_mean_coverage,0.0\ntumor_dup_pct,14.63\nnormal_dup_pct,0.0\ncnvkit_cnv_count,0\nfreec_cnv_count,0\ngatk_cnv_count,0\ntotal_callers_run,3\ntotal_merged_cnvs,0\ntarget_intervals,1000\n</example>",
+    "task_id": "haplotype-phasing",
+    "name": "Haplotype Phasing and Genotype Refinement",
+    "description": "Phase genotypes and refine variant calls from 1000 Genomes Phase 3 chr22 data. 50 study samples with ~10K biallelic SNPs, 200-sample reference panel.",
+    "task_prompt": "Phase genotypes and refine variant calls from a genotype study using a reference panel. The data/ directory contains a VCF file (study.vcf.gz) with 50 individuals genotyped at biallelic SNP positions on chromosome 22. The reference/ directory contains a reference panel VCF (ref_panel.vcf.gz) with 200 individuals, and genetic recombination maps for chr22. Normalize variants, perform quality control (filter by missingness, MAF, and HWE), find shared sites between study and reference, phase haplotypes using at least two statistical phasing methods, compare phasing concordance, perform genotype imputation using the reference panel, and assess post-imputation quality including LD pruning. The output should be a CSV file with columns: 'metric','value'.\n<example>metric,value\nstudy_samples,50\nstudy_variants_before_qc,10050\nstudy_variants_after_qc,9936\nvariants_removed_by_qc,114\nreference_samples,200\nreference_variants,21751\nshared_variants,8972\nshapeit4_phased_variants,8972\nbeagle_phased_variants,8972\nphasing_concordance_pct,88.29\nimputed_total_variants,8972\nimputed_filtered_variants,8972\nnewly_imputed_variants,0\nld_pruned_variants,2662\nld_removed_variants,6310</example>",
     "download_urls": {
       "data": [
         {
           "filename": "data.tar.gz",
-          "url": "https://huggingface.co/datasets/lingzhi227/Extended-BioAgentBench/resolve/main/tasks/cnv-detection-wes/data.tar.gz"
+          "url": "https://huggingface.co/datasets/lingzhi227/Extended-BioAgentBench/resolve/main/tasks/haplotype-phasing/data.tar.gz"
         }
       ],
       "reference_data": [
         {
           "filename": "reference.tar.gz",
-          "url": "https://huggingface.co/datasets/lingzhi227/Extended-BioAgentBench/resolve/main/tasks/cnv-detection-wes/reference.tar.gz"
+          "url": "https://huggingface.co/datasets/lingzhi227/Extended-BioAgentBench/resolve/main/tasks/haplotype-phasing/reference.tar.gz"
         }
       ],
       "results": [
         {
           "filename": "results.tar.gz",
-          "url": "https://huggingface.co/datasets/lingzhi227/Extended-BioAgentBench/resolve/main/tasks/cnv-detection-wes/results.tar.gz"
+          "url": "https://huggingface.co/datasets/lingzhi227/Extended-BioAgentBench/resolve/main/tasks/haplotype-phasing/results.tar.gz"
+        }
+      ]
+    }
+  },
+  {
+    "task_id": "dda-proteomics-simple",
+    "name": "DDA Proteomics: Single-Engine BSA Identification",
+    "description": "This task performs basic DDA mass spectrometry protein identification on a BSA standard using a single search engine. An mzML spectral file and a bovine protein database are provided.",
+    "task_prompt": "Identify proteins from mass spectrometry data by searching spectra against a protein database, controlling false discovery rate, and counting identified peptides and proteins. Spectral data in data/, protein database in reference/. The output should be a CSV file with the following columns: 'metric','value'.\n<example>metric,value\ntotal_spectra,1690\ndatabase_size_with_decoys,12104\nsearch_engine_hits,1604\npsms_after_fdr,16\nunique_peptides,16\nproteins_identified,9</example>",
+    "download_urls": {
+      "data": [
+        {
+          "filename": "data.tar.gz",
+          "url": "https://huggingface.co/datasets/lingzhi227/Extended-BioAgentBench/resolve/main/tasks/dda-proteomics-simple/data.tar.gz"
+        }
+      ],
+      "reference_data": [
+        {
+          "filename": "reference.tar.gz",
+          "url": "https://huggingface.co/datasets/lingzhi227/Extended-BioAgentBench/resolve/main/tasks/dda-proteomics-simple/reference.tar.gz"
+        }
+      ],
+      "results": [
+        {
+          "filename": "results.tar.gz",
+          "url": "https://huggingface.co/datasets/lingzhi227/Extended-BioAgentBench/resolve/main/tasks/dda-proteomics-simple/results.tar.gz"
         }
       ]
     }